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28-34 | Understand motor applications, protection, controls

COVER: Go long on motors, drives and motion control application know-how. Modern electric motors drive Windsor Door’s high-speed production line improved reliability, while contributing to a doubling in plant production and efficiency. Courtesy: ABB

INSIGHTS ANSWERS

| PID spotlight, part 23: Filtering noise for better PID control

| New process control metrics

| Implementing responsible AI for industry

| AI-driven productivity from industrial edge to enterprise

| Smarter automation: Integrating AI into industrial control systems

of the Year 2026; More controller answers; Manufacturing education, research; October hot topics; 2026 research; Autonomous mobile robots from Midwest, events

| COVER: How to double production with people, technologies, motion controls

| Fit motor enclosures, protective sensors to the application

| 2-day changeover drops to 12 hours with new robotic system

| How to create a physical AI security, safety framework

INNOVATIONS

42 | New Products for Engineers www.controleng.com/products

Battery pack for 40-year life; Push-in wiring blocks; RFID-coded safety gate boxes; Improved servo amplifiers; DIN-rail-mounted enclosures; Improved inspection; Motor module; Simplify drive setup and integration; Networking advances; Compact industrial transmitter

47 | Back to Basics: Artificial intelligence for manufacturers MESA: If AI is hard, how are manufacturers gaining quick benefits?

NEWSLETTERS ONLINE: SUBSCRIBE

Insights for automation professionals - Control Engineering recent newsletter examples:

Nov. 19, Mechatronics and Motion Control

Nov. 17, System Integration

Nov. 13, Industrial Cybersecurity Pulse

Nov. 12, Motors and Drives

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• FROM THE EDITOR: CURATED NEWS

• MECHATRONICS & MOTION CONTROL

• MOTORS & DRIVES

• PROCESS INSTRUMENTATION & SENSORS

• PRODUCT & MEDIA SHOWCASE

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Go to www.controleng.com/subscribe and select newsletters.

u Global System Integrator Report

ALL NEW with this November/December edition.

https://www.controleng.com/globalsystem-integrator-report

NOVEMBER/DECEMBER

u Control Engineering eBook series

What’s new? Get topics you need at www.controleng.com/ebooks

u Process Instrumentation & Sensors

Featured articles in this eBook include: Understanding EPA requirements for CEMS design; Three ways sensors and smart devices improve OEE; New patent uses AI to help reduce process safety hazards. More topics at: www.controleng.com/ebooks

u Control Engineering digital edition

Digital edition advantages:

1. Click to more using live links with more text and often more images and graphics. 2. Download a PDF version. 3. Slide bar at bottoms navigates more quickly. 4. Greater sustainability. www.controleng.com/ magazine

Online Highlights

INSIGHTS

u Control Engineering hot topics-October 2025 www.controleng.com/control-engineering-hot-topics-october-2025

u Automation Fair: Scaling OT cybersecurity for modern manufacturing challenges https://www.controleng.com/scaling-ot-cybersecurity-to-meet-modern-manufacturing-challenges

u Plant Engineering: How robots maximize workforce efficiency and automation (A) https://www.plantengineering.com/how-robots-maximize-workforce-efficiency-and-automation

WEBCASTS, PODCASTS

u Motors, drives: How to better manage energy with variable speed drives (B) www.controleng.com/webcasts

u The Downtime | Episode 26: Unpacking Pack Expo https://www.controleng.com/the-downtime-episode-26-unpacking-pack-expo

u Ctrl+Alt+Mfg: Ep. 4: Making Digital Transformation Real with Alicia Lomas, Lomas Manufacturing https://www.controleng.com/podcast/ctrlaltmfg-ep-4-making-digital-transformation-real-with-alicia-lomas-lomas-manufacturing

u Ctrl+Alt+Mfg: Ep. 3: Rethink OT Security, Leah, Jeremy Dodson, Piqued Solutions (C)

https://www.controleng.com/podcast/ctrlaltmfg-ep-3-rethinking-ot-security-with-leah-and-jeremy-dodson-piqued-solutions

u Ctrl+Alt+Mfg Ep. 2: Uniting Disparate Data with John Lee, Matrix Technologies https://www.controleng.com/podcast/ctrlaltmfg-ep-2-uniting-disparate-data-with-john-lee-matrix-technologies

ANSWERS

u PID spotlight, part 22: Can I tune a noisy PID controller? (D) www.controleng.com/control-systems

u Code, not chaos: How automation helps meet regulatory demands – CDM Smith www.controleng.com/control-systems/automation

u Roundtable discussion on SCADA topics www.controleng.com/control-systems/dcs-scada-controllers/

u A3: AI-based perception, vision-guided robots, visual language models www.controleng.com/a3-ai-based-perception-vision-guided-robots-visual-language-models

u Automate 2025: Machine vision standards update www.controleng.com/automate-2025-machine-vision-standards-update

u Machine vision model training, robot set-up, compact cameras www.controleng.com/machine-vision-model-training-robot-set-up-compact-cameras

u Batch manufacturing: The hidden cost of fragmented solutions www.controleng.com/batch-manufacturing-the-hidden-cost-of-fragmented-solutions

Courtesy: ACS Inc.

(A)

(B) (C) (D)

How edge computing empowered one company's digital transformation journey

Seadrill Ltd., an offshore drilling contractor, operates one of the most advanced fleets of drill ships and semi-submersibles in the world. Technologies like edge computing help the company maintain its assets deployed in some of the most challenging offshore environments.

As Seadrill Ltd. sought to enhance its operational efficiency and shift toward more predictive maintenance models, the company ran into a familiar industry barrier –– legacy systems that were inflexible, expensive to scale and slow to adapt. The solution? An industrial internet of things (IIoT) platform called Metis.

Metis was developed in partnership with Wago Corp., Cedalo and Portainer.

“The ideal system is one where, three years from now, no one even knows what Metis is, because it just works,” said Matt Mclendon, regional sales manager for Wago and the project’s lead architect.

Metis helped Seadrill develop and deploy a transformative edge computing solution that not only solved immediate pain points but also laid the groundwork for a data-driven future across its global drilling fleet.

• Risk of potential delays in implementation and commissioning

As Mclendon and his team explored alternatives, it became clear that they needed a new approach — one that was open, secure, modular and repeatable across the fleet.

Built on open standards and strategic partnerships

The architecture was built collaboratively with the three companies mentioned. It blends off-theshelf hardware and open-source software to deliver an edge computing framework designed for industrial scalability and simplicity.

The key components of the platform are twofold. They include hardware and software components.

Hardware:

• Edge computers

• Controllers

• I/O modules

• Cloud virtual machines

Software:

• Software for edge device management

• Messaging queuing telemetry transport (MQTT) broker

controleng.com

KEYWORDS: Controllers, I/O, edge computing

Learn how Seadrill partnered with Wago Corp., Cedalo and Portainer to develop an edge computing platform to empower digital transformation.

Discover the ways in which edge computing can empower data collection, transformation and communications remotely. Determine who edge computing can act as a foundation for future upgrades.

Overcoming legacy limitations and rising costs

Seadrill’s operations team faced a common yet critical issue. Legacy control systems made it difficult and prohibitively expensive to add new data points or prototype innovative monitoring solutions. Cost was a critical component of the company’s plan.

The legacy approach involved:

• High upfront and ongoing costs

• Vendor lock-in with limited extensibility

• Complex licensing models

• Inflexible architectures requiring custom engineering

“The idea is that we can deliver something plug-and-play for our field teams,” Mclendon said. “No one needs to learn a new automation vendor platform.”

How edge computing is powered by data

A central innovation in the Metis project is the use of edge computers to enable real-time data collection, transformation and communication, right from the drilling rigs.

Traditional programmable logic controllers were considered, but edge computing offered key advantages, which include:

• Local encryption and secure data transfer

• On-rig data brokering with MQTT

• Offline operation with up to seven days of data reconciliation

• Minimal setup required; field devices simply plug in and transmit

“Our offshore personnel are maintainers, not engineers,” Mclendon said. “Choosing edge computing for the Metis solution enabled it to be simple, repeatable and robust. If the system requires advanced coding skills, it’s not viable.”

Scalability was a core concept

The platform was developed with scalability as a core principle.

“We designed the architecture so that any stakeholder — onshore, offshore, engineering or maintenance — can add a sensor and get that data flowing in real-time,” said Alec Spedding, head of mechanical systems at Seadrill.

Each rig hosts its own edge broker and firewall, allowing Seadrill to manage bandwidth usage while ensuring continuity of operations, even during connectivity outages.

Determining effectiveness through rollouts

The platform proved its value during the initial proof-of-concept and pilot rollouts. Some key performance indicators include:

• A more than 50% reduction in deployment costs compared to legacy original equipment manufacturer solutions

• Faster deployment with systems installed and operational in hours instead of weeks

• Greater reliability in extreme offshore conditions

• Immediate field acceptance with minimal training required

A foundation but not a single solution

The platform isn’t one solution to Seadrill’s pain points, but the company views it as more of a foundation. With a flexible architecture in place, the possibilities for future use cases include:

• Wireless sensor integration using low-power, low-range for hard-to-reach areas

• Continuous integration and continuous delivery/deployment automation for streamlined software deployment

‘The system helps manage bandwidth usage while ensuring continuity of operations, even during connectivity outages.’

• Real-time analytics to support asset life cycle management

• Expanded control capabilities, including skid control and subsystem automation

• Democratized data access enabling insights from every level of the organization

“This platform allows us to test bold ideas quickly and cost-effectively,” Spedding said. “It’s not just about monitoring; it’s about empowering innovation from every corner of the business.”

Edge computing fixes legacy control system issues

The legacy control system model is breaking down under its own weight: proprietary architectures, exorbitant upgrade costs and slow delivery cycles have created a bottleneck in industrial innovation.

The platform empowers digital transformation and proves that there’s a better way. Evidence comes with Seadrill’s experience developing a system that enabled real-time data collection, transformation and communication from the drilling rigs.

“The vendors should be helping us solve problems, not selling us software license managers,” Mclendon said. “We built this with open standards, trusted partners and a relentless focus on user experience. Now we can respond to new challenges in days, not months.”ce

Barry Nelson and Ken Brunnbauer are marketing representatives with Wago Corp. Image courtesy of Wago. With this article online, see a list of hardware and software used.

Insightsu

uEdge computing helped an oil & gas company enable real-time data collection from rigs.

uDigital transformation empowers local encryption and secure data transfer, as well as on-rig data brokering with MQTT

uThe solution Seadrill employed required minimal setup required with plug-and-play field devices

Latest automation mergers, October 2025: robotics, industrial interoperability

The Bundy Group reported 32 auto mation transactions in its October 2025 summary reports. Analysis on mergers and acquisitions follow, includ ing ABB Robotics, Caterpillar, Object Management Group and others.

Bundy Group, an investment bank and advisory firm provides an update on mergers and acquisitions and capi tal placement activity for this industry, with 32 October 2025 report transactions, involving ABB Robotics, Caterpillar and Object Management Group, among other companies. Technologies involved include automation, industrial artificial intelli gence (AI) robotics, sensors, networking and industrial interoperability and others. A few follow below. See the others.

SoftBank Group agreed to acquire ABB Robotics, Oct. 8

SoftBank Group has entered into a definitive agreement to acquire ABB’s robotics business, with closing expected in mid‑to‑late 2026 pending regulatory approvals in the EU, China and the U.S. The deal advances SoftBank’s “physical AI” strategy by pairing ABB Robotics’ global footprint, 7,000‑person work force and established product lines with SoftBank’s AI and robotics portfolio, positioning the combined platform to scale AI‑enabled industrial and service robotics.

EDM Association acquired Object Management Group (OMG), Sept. 23

EDM Association acquired assets of the Object Management Group, creating the world’s largest association for data, soft ware, systems and standards professionals. It aligns OMG’s standards bodies and commu nities including Digital Twin Consortium, with EDM Association’s data management frameworks and professional programs.

Caterpillar agreed to acquire RPMGlobal Holdings Ltd., Oct. 12

Caterpillar Inc. agreed to acquire RPM Global Holdings Limited, an Australian

mining software company headquar tered in Brisbane. RPMGlobal provides data driven software solutions for all stag es of the mining lifecycle. The company complements Caterpillar’s technologies in asset management, fleet management and autonomy. This acquisition aims to enhance mine site operations and create greater value for customers. ce

Clint Bundy is managing director, Bundy Group, which helps with mergers, acquisitions and raising capital. Edited by Mark T. Hoske, editor-in-chief, Control Engineering, WTWH Media, mhoske@wtwhmedia.com. ONLINE: https://bundygroup.com

ARC Advisory Group: I/O Modules market affected by edge computing

RECENT ARC ADVISORY GROUP research highlighted how the global I/O Modules market for discrete manufacturing is shifting with rapid adoption of edge computing. The integration of AI and Industrial Edge technology is expected to significantly shape the future of the I/O modules discrete market by enabling the incorporation of AI algorithms for advanced data analytics and predictive maintenance, the organization said. Edge-enabled I/O modules allow manufacturers to process critical data closer to the source, reducing dependency on centralized cloud systems and improving operational efficiency. This capability supports predictive maintenance, AI-driven analytics, and secure, decentralized architectures, making edge computing a cornerstone of smart manufacturing strategies. The I/O Modules for Discrete Automation Market Research delivers current market analysis plus a five-year market and technology forecast. It is available in multiple editions. www.arcweb.com ce

Despite slight decline, confidence remained positive in September

NEMA’S ELECTROINDUSTRY BUSINESS CONFIDENCE INDEX for Oct. 3, 2025, is based on survey responses NEMA collected from Sept. 15 to 26. The Current Conditions component eased slightly in September, slipping to 55.0 from 59.1 in August. Respondents reporting “better” conditions rose to 30%, while those viewing conditions as “worse” increased to 20%. Fifty percent of respondents indicated that conditions remained unchanged. Comments were mixed. Several respondents cited ongoing concerns about tariffs and noted that continued uncertainty is negatively affecting business. However, one panel member reported that despite the “noise,” business continued to improve, suggesting uneven impact across sectors from shifting tariff policies. https://www.makeitelectric.org/industry-impact/nema-business-intelligence/electroindustry-business-confidence-index/ ce

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INSIGHTS

New facility strengthens manufacturing education, research

uArizona State University (ASU) is helping fill the manufacturing skills gap. Students, faculty, manufacturers and area workforce are expected to benefit from new ASU specialty and research labs for additive manufacturing, robotics for smart manufacturing and industry automation, cyber manufacturing and operations research, semiconductor manufacturing, and manufacturing systems for the battery research for the energy sector. ISTB 12 includes three instructional labs, four classrooms for the School of Manufacturing Systems and Networks, and a 200-seat multipurpose classroom available for university use. The Polytechnic School in the Ira A. Fulton Schools of Engineering will occupy space in the facility. Arizona State University’s new ISTB 12 building supports training, research and industry collaboration in advanced manufacturing in areas including:

System Integrator of the Year 2026

New Arizona State University (ASU) specialty and research labs includes three instructional labs, four classrooms for the School of Manufacturing Systems and Networks, and a 200-seat multipurpose classroom available for university use. Courtesy: ASU

The 2026 winners of the System Integrator of the Year Award were announced in the 2025 Global System Integrator Report (GSIR), a supplement to Control Engineering and Plant Engineering November/December print and digital editions. What can you learn from these control system integration experts? Case study and problem-solving examples are included. Learn more in the GSIR and online in the Nov. 17 announcement. www.controleng.com/SIY

Advanced Battery Lab: Conducts research on advanced energy storage technologies, including Li-ion, Na-ion, flow and aqueous batteries. The lab helps improve battery performance, safety and durability for electric vehicles, grid storage and portable systems.

Clean Energy Systems Lab: Offers a flexible environment for developing and testing integrated energy technologies at grid scale. Working with utilities and technology developers, the lab supports research on reliable and efficient energy systems and helps move new technologies from laboratory testing to practical applications.

High Bay Collaborative Space: Also known as the Hybrid and Multi-Material Manufacturing Lab, this facility includes industrial robots and additive and subtractive manufacturing equipment used by the School of Manufacturing Systems and Networks. This lab uses Al and digital twin systems to support research on automation in aerospace, nuclear and medical device manufacturing and provides resources for workforce training. The Polytechnic School conducts research on electric and automated vehicles, focusing on powertrain optimization, safety systems and digital twin modeling for autonomous driving.

Robotics and Autonomous Systems Lab: A 5,500-square-foot focused on research in ground and aerial robotic systems. Equipped with sensors, motion tracking

and Al capabilities, the lab supports hardware and software testing in fields such as agriculture, defense and manufacturing. Reactive Material 3D Printing Lab: Supports additive manufacturing with metal alloys and ceramics and includes facilities for research in aerospace and medical device applications.

Materials Testing and Characterization Lab: Includes 5,865 square feet of shared facilities with more than $1 million in metrology equipment. The labs support research across biomedical, aerospace and nuclear industries, with tools for microscopy, spectroscopy and thermal analysis. Class 10,000 Clean Room: Includes 1,200 square feet of Class 10,000 cleanroom space on the Polytechnic campus. NATCAST operates the National Semiconductor Technology Center.

Micro-Assembly and Packaging Automation Lab: Includes 4,000 square feet of space for semiconductor assembly processes such as die attach, wire bonding and encapsulation. The lab contains precision measurement tools and supports work that connects wafer fabrication with system-level applications in computing, sensing and communications. ce

Edited by Puja Mitra, WTWH Media, for Control Engineering, from a ASU news release.

Autonomous mobile robots roll off Midwest assembly line

U.S. robotics manufacturing increased as Rockwell Automation Inc. started manufacturing autonomous mobile robots, or AMRs, at its global headquarters in Milwaukee. Rockwell Automation, an industrial automation and digital transformation company, previously purchased Otto and then created a new 25,000 sq. ft. (2,322.5 sq. m) Otto production space at the Milwaukee campus for assembly of Otto 600 (photo) and Otto 1200 AMRs. OTTO designed these robots to move heavy materials safely and efficiently across busy factory floors and in tight spaces. By reducing reliance on manual forklifts, the AMRs can help manufacturers increase safety, improve transition times, minimize damage to goods, and create more resilient and sustainable operations, said the company. ce

Rockwell Automation Inc. started manufacturing autonomous mobile robots at its global headquarters in Milwaukee in a new 25,000 sq. ft. (2,322.5 sq. m) Otto production space. Otto 600 AMR is shown. Courtey: Rockwell Automation

Learn more from Control Engineering’s sister publication, Automated Warehouse. https://www. automatedwarehouseonline.com/rockwell-automation-says-first-amrs-haverolled-off-the-line-at-milwaukee-facility/

Recent events

• A3 International Robot Safety Conference, Nov. 3-5, Houston

• Rockwell Automation Fair, Nov. 17-20, Chicago, www.automationfair.com

• Watch for more at www.controleng.com.

• Did you see? www.controleng.com/webcasts Dec. 11 webcast “Motors, drives: How to better manage energy with variable speed drives.”

Upcoming events

• A3 Business Forum, Jan. 19-21, 2026, Orlando, https://www.automate.org/events/ business-forum

• ARC Industry Leadership Forum, ARC Advisory Group, Feb. 9-12, 2026, Orlando https://www.arcweb.com/events/arcindustry-leadership-forum-orlando

• Trihedral SCADAfest, March 23-27, 2026, Orlando, https://scadafest.com

• CSIA Conference, May 5-8, 2026, Baltimore, https://controlsys.org/upcoming-events

• Hannover Fair, Germany, April 20-24, 2026

• 2026 Honeywell User Group (HUG) Conference, Phoenix, June 8-10, 2026, https:// automation.honeywell.com/us/en/about-us/ honeywell-users-group

• Automate, Chicago, A3, June 22-25 https:// www.automateshow.com

State of Automation 2025 research: Year-end insights, guidance

The Control Engineering survey and report, State of Industrial Automation 2025, created opportunities for additional coverage during the year on automation, controls and instrumentation, supporting the survey results and trends revealed in the report. Think again about what you think you know about automation.

State of Industrial Automation 2025 survey and report from Control Engineering have been supported throughout the year covering trends and topics in automation, controls and instrumentation, as shown more than 40 ways below.

• Think again about the state of industrial automation in 2025

The State of Industrial Automation report, spring 2025, analyzes how this dynamic sector is reshaping industries worldwide. This Control Engineering report has data, analysis and actionable insights for manufacturers, technology vendors and policymakers striving to thrive in an era of rapid technological change.

Filling the automation skills gap

How to continuously improve careers in automation, controls, instrumentation

Control Engineering Engineering Leaders Under 40 provide great reminders that

career development requires measurement, logical decisions and acting upon that knowledge by applying the control loop.

• Results are in: Control Engineering Career and Salary Survey, 2025

Benefits and salaries increased. Artificial intelligence and machine learning top the list of leading automation technologies. The economy and workforce shortages are tied among manufacturing business challenges at 38% with taxes and tariffs second at 27%, jumping from 8% last year.

Next-generation automation: Open, interoperable

• New insights: 100-controller

ExxonMobil Open Process Automation

Open automation ecosystems help resolve workforce challenges and provides metrics such as a 50% downtime reduction and a 20% increase in overall equipment effectiveness (OEE) over two years, according to an Automate 2025 keynote presentation.

• More answers about how to select the right controller type System integration experts provide more on “How to select the right controller type for the automation application,” a Sept. 25 Control Engineering webcast, below answering more audience questions than webcast time allowed.

• Automate 2025: Machine vision standards update

MORE TOPICS WITH LINKS: This article online has links and more topics, including Artificial intelligence bolsters advanced automation

Increased automation connectivity, integration improve cybersecurity responses

More Control Engineering automation research, discussions continue industry’s most powerful development to help guide your automated future? https://www.controleng.com/state-of-automation2025-research-year-end-insights-guidance Online controleng.com

ExxonMobil OPA-S Lighthouse Project insights A new ground-up process control system is operating with more than 100 controllers and 1000 input/output (I/O) points at the ExxonMobil Resins Finishing Plant in Baton Rouge, Louisiana, using the Open Process Automation Standard (O-PAS), as described at the 2025 ARC Industry Leadership Forum by ARC Advisory Group.

• Shell modernizes refinery control with software-defined automation

Hardware agnostic distributed intelligence of an automation platform is creating a flexible control architecture for manufacturers.

• Automate 2025: 10x proven benefits from software

Multiple global machine vision organizations updated Automate 2025 session attendees on machine vision standards, including the G3 effort to coordinate machine vision standardization.

• New SCADA functionality: Improved communications, security, navigation Discover new features available in modern supervisory control and data acquisition software.

• New facility: Six net zero benefits, seven smart manufacturing methods Tour: Understand six benefits to automated, advanced manufacturing and warehousing facility, seven smart factory attributes. Learn about new automated manufacturing, educational labs, sustainability

Mark T. Hoske Control Engineering

methods. Explore a $100 million automated, advanced manufacturing facility; see six benefits of advanced manufacturing.

Smart instrumentation, digital transformation increases visibility, capability, usability

• New smart instruments, wireless for process industry applications

Interview after Weftec provides answers about how process instrumentation and sensors help in process industries.

• Sensing, flow technology insights: Reliability, efficiency, safety

Water and wastewater measurement and flow technologies at Weftec 2025 in Chicago included electromagnetic flow meters.

• How to integrate resiliency into process controls, digital transformation

Accelerating innovation for process controls, digital transformation and automation were among topics at a major conference and show.

Electrification, automation trends, markets, safety, motion control

• Powering the electric future: Technology, policy and the path ahead

NEMA president and CEO Debra Phillips discussed “Powering the Electric Future: Technology, policy and the path to the next gen energy, manufacturing and grid system” in the keynote presentation at SPS Atlanta 2025, on tariffs, trade, technologies and other topics.

• Advantages of multi-axis servo drives in the world of automation

Selecting among technology options in a motion-control application often comes with levels of complexity, especially when dealing with multiple axes.

• Interactive hazard and operability assessments: 5 ways to safer facilities

How to improve process control safety and risk management? Hazard and operability studies need to integrate sensor data, digi-

tal models and safety criteria to optimize plant health. See five components of interactive hazard and operability assessments.

• Automate 2025: 5 ways cobots, AMRs top humanoid robots

Collaborative robots and automated mobile robots (AMRs) are more efficient and cost effective for manufacturing and logistics than humanoid robots, which have opportunities in other applications. See five industrial automation needs and five ways automation helps.

Smarter uses of controls boost automation optimization

Control Engineering helps its audience better apply automation, controls and instrumentation including with tutorials on control theory. The long-running series on proportional-integral-derivative tuning and other advanced process controls are examples.

• PID spotlight, part 20: How to tune with sticking control valves

Learn three things about loop tuning with a sticking control valve.

• APC 2.0 spotlight, part 2: Base-layer advanced process control

Base-layer advanced process control provides new capabilities.

• How modern MES fits into the fourth industrial revolution

Manufacturing execution systems (MES) are changing the ways in which manufacturers operate in the fourth industrial revolution.

• Advice compendium for controls and automation programmers

Industrial controller programmers can hone their control system programming skills for programmable logic controllers, programmable automation controllers, industrial PCs, embedded controllers and edge using extensive advice from two programming experts. ce

Mark T. Hoske is editor-in-chief, Control Engineering, WTWH Media, mhoske@wtwhmedia.com.

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Content Specialists/Editorial

Mark T. Hoske, editor-in-chief 847-830-3215, MHoske@WTWHMedia.com

Gary Cohen, senior editor GCohen@WTWHMedia.com

Sheri Kasprzak, executive editor, engineering, automation and controls, SKasprzak@WTWHMedia.com

Stephanie Neil, vice president, editorial director engineering, automation and control, 508-344-0620 SNeil@WTWHMedia.com

Jill Lowe, webinar manager JLowe@WTWHMedia.com

Amanda Pelliccione, marketing research manager 978-302-3463, APelliccione@WTWHMedia.com

Puja Mitra, contributing editor PMitra@WTWHMedia.com

Contributing Content Specialists

Suzanne Gill, Control Engineering Europe suzanne.gill@imlgroup.co.uk

Agata Abramczyk, Control Engineering Poland agata.abramczyk@trademedia.pl

Lukáš Smelík, Control Engineering Czech Republic lukas.smelik@trademedia.cz

Aileen Jin, Control Engineering China aileenjin@cechina.cn

Editorial Advisory Board www.controleng.com/EAB

Doug Bell, president, InterConnecting Automation, www.interconnectingautomation.com

Daniel E. Capano, senior project manager, Gannett Fleming Engineers and Architects, www.gannettfleming.com

Frank Lamb, founder and owner Automation Consulting LLC, www.automationllc.com

Joe Martin, president and founder Martin Control Systems, www.martincsi.com

Rick Pierro, president and co-founder Superior Controls, www.superiorcontrols.com

Eric J. Silverman, PE, PMP, CDT, vice president, senior automation engineer, CDM Smith, www.cdmsmith.com

Mark Voigtmann, partner, automation practice lead Faegre Baker Daniels, www.FaegreBD.com

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Ed Bullerdiek, process control engineer, retired

PID spotlight, part 23: Filtering noise for better PID control

What is filtering? How does it work?

How do we integrate it into the PID controller tuning process?

Process noise can negatively impact PID controller performance, and it complicates tuning PID controllers, as we learned in PID spotlight parts 21 and 22. In short, we have to do something to hide (or ignore) the noise while keeping the actual process response.

1: This shows attenuation of normally distributed white noise using first order and moving average filters. Figures courtesy: Ed Bullerdiek, retired control engineer

The most common approach to hiding process noise is to apply a “low pass” filter. This is a fancy way of saying we are going to use a running or moving average of the last X measurements, which has the effect of filtering out fast (high frequency) noise while passing the slow (low frequency) process signal (hence the name low pass filter).

Unfortunately, a low pass filter does not provide a crisp cut between noise and process response. Of course, if you have a very slow process then you can apply a lot of filtering with little impact on controller performance. However, if you have a fast process then filtering will impact controller performance. In these latter cases you will need to make a tradeoff; how much filtering can you add to fix the performance problems noise can create without adding so much filtering that the filtering creates its own performance problems?

Filtering white noise

Figure 1 shows how a first order lag filter and a moving average filter attenuates normally distributed white noise. A relatively small amount of filtering can produce a significant reduction in noise. A first order filter achieves about a 70% reduction at 6 seconds. A moving average filter achieves 70% at 12 seconds. Adding more filtering in either case rapidly results in diminishing returns.

It appears that for true white noise a first order filter is our best option, and it should be limited to no more than about 10 seconds. (Let’s state for the record that you will rarely encounter true white noise; you may have to use more filtering. But the basic premise stands; a little bit of filtering helps a lot, adding more doesn’t help as much.)

Filtering cyclic noise

Cyclic noise, whether sine wave or other wave

FIGURE

‘Adding a first order or moving average filter adds apparent process deadtime and lag to the system, which can limit how aggressively a controller is tuned.’

form, responds differently to first order and moving average filters. In Figure 2 the noise attenuation of a first order filter looks the same as white noise; there is a rapid drop toward 70% reduction at the 5 second mark followed by rapidly diminishing returns for additional filtering. The moving average filter eliminates the noise signal if the filter period is a multiple of the cycle period (the attenuation curve looks like a bouncing ball).

I have never used a moving average filter simply because refining doesn’t have processes where cyclic noise can be expected. Note also that most control equipment supports adding a first order filter (so does most instrumentation) whereas adding a moving average filter requires additional programming. Furthermore, the moving average calculation may lose important process variable information like instrument status.

That said, if you have a process with a cyclic noise component then it may be worth your while to consider using a moving average filter. If you choose to stick with a first order filter noise attenuation is proportional to the cycle time. The best tradeoff on filtering is to set the filter time equal to the cycle time. In this case 10 seconds is about the best. If you have a 30 second cycle set the first order filter time to 30 seconds.

How does filtering affect the process?

Adding a filter affects how the controller sees the process. The process doesn’t change, however adding a first order filter looks to the controller like the process has an additional lag. This, of course, affects controller tuning; making the process appear slower means we must slow down the controller tuning. If you are tuning for disturbance rejection the slower controller tuning will allow larger and longer lasting disturbances. Whether or

FIGURE 2: Attenuation of cyclic noise (10 second cycle) using first order and moving average filters is shown.

not this will be a problem depends on how fast the process is.

Figure 3 shows how a first order filter affects the apparent deadtime and lag of a very fast process; one with one 10 second lag and no deadtime. The process itself has an infinite lag/deadtime ratio, which means we could set the controller gain as high as the controller will permit and still have stable control. If we are looking for extremely tight control, a high controller gain may be necessary.

Onlinecontroleng.com

KEYWORDS: Proportional-integral-derivative, PID tutorial LEARNING OBJECTIVES

Know that a little filtering rapidly reduces noise; adding more has diminishing returns. Also know when to use a first order or moving average filter.

Understand that adding a filter may adversely impact PID controller performance. You may have to make tradeoffs between filtering and tuning to optimize performance. Know the filter guidelines; understand when they may be ignored. Understand filtering derivative action of a controller.

CONSIDER THIS

Filtering may be necessary to improve PID controller performance, but may also negatively affect PID controller performance. What tradeoffs might you have to make to get good control?

ONLINE

With this article online, find two examples detailed with 10 more graphics. Link to PID spotlights, parts 1-22 and with this article online, starting with “Three reasons to tune control loops: Safety, profit, energy efficiency.”

https://www.controleng.com/articles/ three-reasons-to-tune-control-loops-safety-profit-energy-efficiency

ANSWERS

‘If performance is not an issue, or the process is very slow, then filtering within reason will not cause a problem.’

However, once we start adding a filter, the apparent lag rises rapidly from 10 to near 20 seconds when the filter reaches 10 seconds. A filter time beyond 10 seconds dominates the apparent “process” response. Worse yet, when the filter exceeds 3 seconds, we start to see the apparent deadtime increase. This causes the lag/deadtime ratio to rapidly decrease to about 14 when the filter time reaches 10 seconds. A lag/deadtime ratio of 14 tells us our controller gain should be no more than 7 times the baseline controller gain. Depending on the controller performance you need, this could be a problem.

The fix for this is to lower the filter time, however, this will increase noise. More noise means we have to live with the controller performance limitations and tuning issues we identified in the previous two articles.

Figure 4 presents a much different picture of how a filter will affect the apparent process. This is a process with three 30 second lags, which has an apparent deadtime of 22 seconds and an apparent lag of 74 seconds for a lag/deadtime ratio of 3.3. The maximum stable controller tuning for this process has a moderate ability to eliminate process disturbances; we can expect that adding filtering will negatively impact tuning.

Adding a filter rapidly raises the apparent deadtime, lowering the lag/deadtime ratio. The lag/deadtime ratio drops as low as 2.2 when the filter time reaches 25 seconds. Even if we use the quick start filter guide (PID spotlight part 21) and set the filter to one-tenth the apparent lag (7 seconds) the lag/deadtime ratio is 2.7, and the maximum gain we can use on the controller is 20% lower.

Our takeaway from this is if controller performance is a concern, then if noise filtering is required, we will have a balancing problem for fast and even some moderately slow processes. Rules of thumb may not provide sufficient guidance. However, if performance is not an issue or the process is very slow, then filtering within reason will not cause a problem.

About those filter guidelines

There are multiple opinions on how to set the first order lag filter constant (Tfilter). Suggested options are:

1. Tfilter < T1 / 10 (One-tenth the process lag)

2. Tfilter < Ti / 10 (One-tenth the integral tuning constant)

3. Tfilter < Dt / 5 (One-fifth the process deadtime)

If you do an open loop test then setting the filter constant to no more than one-tenth the process lag is simple. If, however, you determine your

FIGURE 3: The impact of a first order lag filter on a process with one 10 second lag and no deadtime is shown.

tuning constants using closed or heuristic methods then setting the filter to no more than one-tenth the integral constant works. If you have a deadtime dominant process you may be better served setting the filter to no more than one-fifth the process deadtime.

Regardless, these should not be considered hard and fast rules. A very fast process such as our first example with no deadtime and a 10 second lag would allow you no more than a 1 second filter. Depending on the severity of the noise this may not be adequate. You may be forced to make a compromise. In simulation it appeared that a 6 second filter was required to bring a +/-10% white noise band to a reasonable level. The controller gain was reduced by 75%, and integral was sped up 17%, which still achieved good disturbance rejection.

Our second example with three 30 second lags was tuned for disturbance rejection with a controller gain of 1.67, integral of 1.125 minutes/ repeat and derivative of 0.28 minutes. A 10% mixed noise band (5% white, 5% cyclic with a 10 second cycle) worked reasonably well with a 10 second moving average filter (matching the cycle), however the derivative constant had to be reduced to 0.10 after adding a 6 second derivative filter. This resulted in slightly more oscillatory control. Derivative filterSome control systems allow you to filter the derivative response. Because derivative multiplies noise adding a filter just to the derivative allows you to focus the noise reduction to just the derivative. Most systems implement the filter as a first order lag and set the filter time constant as a fraction of the derivative time. Guidance is to set this ratio between 0.1 and 0.2 (divide derivative time between 5 and 10).

I have not worked with derivative filtering outside of simulations. However, it appears to be a powerful tool to allow us to use derivative when noise is present. Also, based on the simulations, the guidance is more a suggestion than a hard and fast rule.

Summary of filtering process noise for PID tuning

Filtering process noise may be necessary to permit proper tuning and operation of a PID controller and prevent unwanted control valve movement.

However, filtering does not come without tradeoffs. Adding a first order or moving average filter adds apparent process deadtime and lag to the system, which can limit how aggressively a controller is tuned. This will likely not be an issue for very slow processes, but could be significant for fast processes or processes with low lag/deadtime ratios.

A process noise filter rapidly reduces noise at small values, but the effect of increasing the filter time diminishes rapidly.

There are guidelines for setting filter constants, however, these guidelines are not hard and may be exceeded when necessary.

Using a derivative filter can preserve the advantages of derivative action while mitigating the effect of derivative multiplication of noise on control valve movement. ce

Ed Bullerdiek is a retired control engineer with 37 years of process control experience in petroleum refining and oil production. Edited by Mark T. Hoske, editor-in-chief, Control Engineering, WTWH Media, mhoske@wtwhmedia.com.

MORE ONLINE

uUnderstand what can... and cannot be accomplished with PID tuning. Insightsu

uSee prior and future articles in this PID tutorial series.

uesolve long-standing PID tuning challenges.

FIGURE 4: This shows the impact of a first order lag filter on a process with three 30 second lags and no deadtime.

George Buckbee, Sage Feedback LLC, Clarks Summit, Pennsylvania, and Robert Rice, Control Station, Ashford, Connecticut

Can a new process control metric deliver essential operating information?

Overall controller effectiveness (OCE) is now helping process manufacturers consolidate complex process control data into easily used scorecard values with rapid paybacks.

Much like how overall equipment effectiveness (OEE) revolutionized discrete manufacturing performance, overall controller effectiveness (OCE) is now helping process manufacturers consolidate complex process control data into easily used scorecard values, helping them better understand operations and make meaningful efficiency and productivity improvements with rapid paybacks.

While an overabundance of data may not be as problematic as a scarcity, neither scenario is ideal. Over the past few years, many industrial processing plants have transitioned from the one extreme of extracting scanty and manually sourced data, to the other extreme of receiving a flood of automatically logged data. As a result, many operations teams are forced to transition from a low-visibility situation into a more overwhelming situation.

Proportional-integral-derivative (PID) control loop performance monitoring (CLPM) software provides a key tool for helping plant personnel make sense of these massive data volumes, conduct analyses and optimize their process automation systems. Even so, there remains a need to understand how to best prioritize activities and integrate them into work processes that support timely decision-making.

Additionally, communicating the results to management and linking actions to business results are also important aspects (see Figure 1).

Recognizing the acute need to further simplify and streamline process optimization efforts, a leading CLPM solution provider has developed a new metric, software tools and services. Namely, the OCE metric joins the rank of key performance indicators (KPIs) that help end users — from the plant floor up to the corporate office — better understand and improve their operations by presenting performance in a normalized and easy-to-understand scorecard format, so they can readily compare equipment, production lines and even entire sites.

Extending a proven optimization concept

Discrete manufacturing companies managing factory-based machining, assembly lines, logistics and other activities have long faced the same challenges as their continuous processing plant coun-

FIGURE 1: Process manufacturers generate massive amounts of data from their numerous PID controllers. CLPM software is increasingly an essential tool, providing easy-to-use metrics and visualization that help operators, engineers and management understand where performance is lagging. Figures courtesy: Control Station

terparts. They have substantial volumes of raw data at hand but need ways to transform this into useful information so they can make optimal decisions and communicate throughout the organization.

To address this need, OEE became universally accepted in the discrete manufacturing world since its introduction in the 1980s. OEE is a composite value formulated by multiplying the following three input characteristics, each normalized from 0% to 100%, which results in an OEE output ranging from 0% to 100%:

• Availability: running without unplanned stops

• Performance: running normally and as fast as possible without constraints

• Quality: producing good parts without defects

While OEE is not a precise measure, it does provide a consistent way of evaluating the performance of a single machine, a production line and even an entire plant. Furthermore, it is a high-level tool, usable by all team members, for providing guidance regarding improvement opportunities.

The new OCE metric builds on the concept of OEE but is tailored to provide a simplified approach for process control industries (see Figure 2). Continuous processes generate massive amounts of field-sourced data, such as flow, pressure and temperature, along with significant derived and calculated values. It is nearly impossible for any human to look at raw data lists or even trends and determine whether production is running efficiently or not or to determine the root cause of any potential problems. OCE is a composite value designed to communicate this vital knowledge by distilling essential information from masses of data, based on the three most relevant process control characteristics, providing an OCE output ranging from 0 to 100%:

• Availability: running in normal mode without being overridden

• Performance: controller output (CO) running within its designed range without constraints

• Quality: process variable (PV) operating near setpoint (SP) within acceptable limits

There are many reasons for loops to behave poorly. Sometimes the control logic is flawed, or the operations team takes a loop out of its designated “normal” mode because they lack confidence in the control system. Perhaps a mechanical element is improperly sized, sticking or broken, leading to a windup of the CO. In many cases, poor tuning causes oscillations or an otherwise unacceptable mismatch between PV and SP. Even if the physical control loop is working well, there may be other related problems — such as historian misconfiguration, computer operating system updates and connectivity issues — which may fly under the radar.

Using OCE, users can quickly spot performance outliers like these and take action to correct them. The benefits of PID loop tuning software, advanced regulatory control loop analytics and CLPM solutions are already well known and will continue to play an important role in detailed troubleshooting and optimization efforts. Adding OCE to the toolchest further equips process manufacturers with an intuitive means for benchmarking control at the unit- and plant-wide level and for identifying specific areas that are undermining performance and productivity.

In the process industries, there is a common rule of thumb that automation represents approximately 10% of the capital cost for new production plants. Indeed, many end users expend between $10,000 to $20,000 for hardware, software and labor associated with configuring a single PID loop. At such a cost, ensuring continued performance and quality is critical to maximizing the return on these investments. Intuitive metrics like OCE help world-class process manufacturers to transform massive amounts of raw data into actionable information and to maintain their pursuit of peak production. ce

George Buckbee, P.E., is the founder of Sage Feedback LLC. Robert Rice is the vice president of engineering at Control Station.

FIGURE 2: The process control oriented OCE metric is calculated much like the proven OEE measurement used by discrete manufacturing industries. For a single asset or any grouping of assets, OCE provides a 0 to 100% result based on normalized data from the key controller performance attributes of availability, performance and quality.

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With this article online find: uScaling OCE throughout the enterprise uNavigating toward positive business results uTwo more graphics

ANSWERS

Petra Nieuwenhuizen, Aveva

Responsible AI for industry: At scale with results

Implementing AI elements strategically is crucial to achieve practical, impactful results.

As consumer-grade artificial intelligence (AI) becomes a routine part of daily life, the conversation around AI is shifting. The question is no longer if or when to adopt AI, but rather how to implement it responsibly, at scale and with lasting impact. AI’s potential is clear: improve uptime, reduce waste, optimize designs, close labor gaps and make better decisions, faster. But translating that potential into measurable results remains elusive for many.

AI has tremendous potential to transform complex industrial workflows. Utilizing sophisticated techniques to recognize patterns, detect anomalies, offer expert guidance and anticipate future

FIGURE 1: Connected data will ensure the results are seamless, scalable and impactful, no matter if you're in the design stage to improve productivity, the operations stage to target efficiency, or the optimization stage to maximize reliability and performance. All images courtesy: Aveva

scenarios, AI produces benefits across all phases of the industrial asset lifecycle. Today’s operations are navigating not only technological complexity but also rising volatility, workforce turnover and mounting pressure to meet sustainability and compliance goals. In this environment, deploying AI is not just about algorithms, it’s about trust, context and alignment with business outcomes.

Successfully incorporating AI into industrial environments requires meeting rigorous demands and taking a thoughtful, strategic approach. Whether you are in the design stage aiming to improve productivity, the operations stage targeting efficiency across operations, or the optimization stage maximizing process and asset performance and reliability, thoughtfully connecting all datasets will ensure results are seamless, scalable, accessible and immediately impactful (Figure 1).

Industrial AI differences

AI in consumer tools can hallucinate or get it “mostly right.” That may be acceptable in a general purpose chatbot. But AI in industrial operations requires more rigor, as an inaccurate prediction can lead to downtime, safety incidents, or regulatory exposure. High-stakes environments demand much more: traceability, data integrity, domain awareness and the ability to scale across production lines, plants and geographies.

The sheer volume of sensor data available today is staggering, with both industrial and commercial sectors experiencing exponential data growth. Users are ready to unlock value from their systems (Figure 2). Leveraging AI to connect data sets—both across currently isolated silos and also throughout the industrial lifecycle—is the key. Correspondingly, data center players are expecting a massive $1.8 trillion of capital deployment globally from 2024 to 2030 to support the growing demand for data-intensive applications, according to Boston Consulting Group. Agent-based systems, or agentic AI, are emerging that learn and

adapt across the industrial lifecycle, offering a more comprehensive and deep operational intelligence. These systems evolve with operations, continuously improving without requiring constant reprogramming, which satisfies a critical need in environments with limited resources.

Moving to maximum value

For many companies, AI implementations stall in the pilot phase. They run isolated experiments that never scale, or struggle to prove ROI because the AI isn't embedded into daily work processes and therefore difficult to align with real business outcomes. A better approach embeds AI into core processes and workflows from the start (design, operations, planning and maintenance), ensuring the system learns from real data, empowers the user and delivers value at every step.

The foundation for this is a connected, contextual data environment. AI draws its power from patterns, and patterns emerge from relevant data in context. That means industrial AI must operate across engineering models, real-time operations and enterprise systems, not just within one stage. The ability to draw on all of this, without rigid integration, is what separates holistic industrial intelligence from clever point tools.

AI tools are proving helpful in all aspects of the industrial lifecycle. From optimizing plant designs to streamlining workflows, AI-driven automation is increasing productivity and efficiency. Gen AI with language models is changing the way humans interact with industrial software, driving a more human-like, intuitive experience.

Intelligent scheduling built on AI technology is increasing supply chain resilience by enhancing demand forecasting, enabling smarter inventory management and analyzing logistics to minimize the impact of disruptions. Data- and AI-driven choices result in dynamic process optimization, real-time quality control and accurate predictive maintenance. Sustainability and compliance initiatives can be propelled by AI insights that optimize resource usage, achieve decarbonization goals and reduce waste.

Four ways to use AI and succeed

Organizations that succeed tend to share traits:

• They treat data as a strategic asset, not just a byproduct of operations.

2: Managing industrial data without AI is no longer practical. AI enables organizations to extract insights and maximize value from previously disconnected data sets.

experience and 20+ years

innovation to help businesses turn their data into intelligent action, and today has over 20 AI-infused products ready to support all industries and users.

• They choose open, agnostic systems that integrate across disciplines and vendors.

• They invest in human-AI collaboration, ensuring operators and engineers trust the insights.

• They demand explainability and governance, because trust is earned, not assumed.

Advanced AI algorithms can optimize and streamline existing processes. ce

Petra Nieuwenhuizen is a senior marketeer in Aveva’s portfolio team. Edited by Mark T. Hoske, editor-in-chief, Control Engineering, WTWH Media, mhoske@wtwhmedia.com.

controleng.com

With this article online, see:

- The future of industrial AI

- Addressing AI concerns

- Responsible AI unlocks potential

- Four common branches of industrial AI ALSO Control Engineering provides more on industrial AI. https://www.controleng.com/ ai-and-machine-learning

FIGURE

FIGURE 3: AVEVA brings 50 years

industrial

of AI

ANSWERS

Sean Saul, Emerson

AI-driven productivity from industrial edge to enterprise

A hybrid AI architecture orchestrates AI deployments across cloud, edge and core control system compute environments based on use cases, performance and security needs.

In just a little over three years, artificial intelligence (AI) has come to dominate the technology landscape. It is increasingly rare to find process manufacturing professionals who haven’t engaged with generative AI tools, whether to help with crafting documents, conducting research, or general question and answer-style interactions. As a result, many teams are pursuing opportunities to deploy advanced AI techniques in operational technology (OT) environments to drive productivity in manufacturing operations. The common force behind today’s most transformative AI tools—and the foundation that gives them their impressive capabilities—is cloud computing. Processing power and rapid scalability available in cloud environments are key factors making AI tools universally accessible and increasingly insightful.

Cloud approach impedes OT adoption

These challenges are solvable. OT technologies are advancing, and key AI tools are already available, with more on the way. Forward-thinking organizations are already deploying more high-performance computing capabilities closer to their underlying processes via edge hardware platforms.

Today, that strategy primarily manifests via deployment of edge solutions that are physically isolated from core control system components. In coming years, the OT technology stack will be further enhanced by the emergence of software-defined architectures working in tandem with enterprise operations platforms to provide a cohesive approach to AI workload orchestration.

OT architecture evolution

One of the keys to successfully deploying AI in OT infrastructure is the capability to seamlessly migrate cloud-native workloads into an on-premises environment. Today, automation solutions providers are equipping edge platforms with AI accelerators, allowing variants of models like those deployed in the cloud to run on local systems. The goal is to deliver solutions that can run on-premises—delivering the security and latency necessary for OT environments—while still providing reasoning capability and generating natural language responses that are verifiably accurate without fabrications.

controleng.com

KEYWORDS: Industrial AI, process control AI

CONSIDER THIS

Do your process controls integrate elements of AI? How?

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Get more on industrial AI from Control Engineering.

https://www. controleng.com/ ai-and-machine-learning

The tight coupling between cloud computing and AI has slowed the pace of adoption of OT use cases as OT teams are typically reluctant to connect systems to the cloud. Cloud connectivity can be impacted by network constraints impacting performance, and in some jurisdictions data governance and regulatory requirements could limit adoption. These are critical issues for organizations focused on safety, availability, and competitive advantage. Yet, even if OT teams were more willing to connect control systems to the cloud, in many cases the latency would still be too high for real-time, mission-critical operations.

Those local models are quickly demonstrating that the industrial edge is the next frontier for OT-driven productivity gains, with a strong underlying data foundation with clear context critical for success. Edge environments can seamlessly connect AI use cases with the rich data model and streaming real-time operational data that distributed control systems uniquely provide. Most importantly, that processing can be performed quickly and securely at the edge, making it possible to tie the results more directly to mission-critical, real-time goals.

‘Not every AI solution will need to reside on the same compute platforms as core control functions.’

The capabilities of on-premises AI for OT environments do not end with edge deployments. As automation solution providers build out their software-defined control offerings, the AI models needing the lowest latency will be able to reside even closer to the underlying process dynamics— eventually operating in the same virtualized environment as other control system functions. With extremely low cycle times, these software-defined systems will help operators determine the best action in complex scenarios by using AI to capture and embed knowledge, making it available on demand. These systems will have the capability to automate operator-guided, multi-step workflows, dramatically reducing the time from analysis to problem resolution (Figure 1).

Orchestrated hybrid environment

Effective deployment of AI in OT architectures hinges on a flexible technology foundation—one that aligns with the operational philosophy and the unique performance requirements of each process. Not every AI solution will need to reside on the same compute platforms as core control functions. Even when the most advanced software-defined control systems are available and running AI workloads in the same environment as core control functions, many AI applications will still be better suited for edge or cloud environments.

Technologies likely to run at the software-defined control layer are real-time, deterministic, low-latency applications like advanced process control, quality control, and other solutions that need to deliver results in seconds or milliseconds to be safe and effective. In an edge environment, AI solutions that support reliability, sustainability and other operational excellence outcomes will take advantage of powerful hardware-based AI accelerators to deliver results that can be provided with slightly longer cycle times.

Scenario and planning tools, performance engineering software, and enterprise virtual advisors for a fleet of OT systems are likely to continue residing in cloud environments, where they can scale seamlessly

FIGURE 1: From the intelligent field, through the edge, and into the cloud, AI technologies will continue to improve operational excellence and unlock autonomous operations. All figures courtesy: Emerson

and deliver results that are not as time sensitive.

As these compute environments continue to interconnect, OT teams will need an orchestration architecture to coordinate AI workloads across cloud, edge, and core control systems — ensuring optimal performance throughout the automation stack. The most effective solutions will be solutions designed to integrate seamlessly. ce

Sean Saul, vice president of DeltaV platform at Emerson. Edited by Mark T. Hoske, editor-in-chief, Control Engineering, WTWH Media, mhoske@wtwhmedia.com.

FIGURE 2: Enterprise operations platforms will deliver effortless integration of AI and automation technologies for enterprise optimization.

ANSWERS

Felipe Sabino Costa, Moxa Americas Inc.

Three ways industrial AI enhances traditional control systems

Artificial intelligence (AI) enables contextaware decision-making, adaptive learning and predictive optimization to extend the value of traditional automation frameworks.

Artificial intelligence (AI) is no longer a future aspiration for industrial control systems (ICS); it has already become a transformative force within operational environments. By enabling context-aware decision-making, adaptive learning and predictive optimization, AI extends the value of traditional automation frameworks rather than replacing them. Where legacy automation relies on rigid control logic and reactive feedback loops, AI empowers systems to adapt dynamically, anticipate deviations and respond before disruptions occur.

This integration represents more than a technical evolution. It is a paradigm shift in how industries manage efficiency, resilience and cybersecurity across critical infrastructure. By embedding AI models securely within ICS, organizations can drive safer operations, streamline processes and enhance protection against increasingly sophisticated threats.

global supply chains, the limitations of fixed feedback loops become evident. AI introduces a step change: Moving from post-event reaction to proactive, in-process inference. Machine learning models can continuously evaluate streams of operational data, anticipate deviations and adjust process variables before thresholds are breached. This capability transforms the role of control systems from static execution engines into adaptive platforms capable of evolving alongside the environment they govern.

AI-enhanced functions in ICS

AI is being embedded into ICS to deliver functions that enhance efficiency, safety, and security far beyond what static control systems can achieve.

Smarter diagnostics leverage anomaly detection algorithms to identify subtle deviations in equipment behavior. Instead of relying solely on alarms triggered when thresholds are exceeded, AI continuously evaluates signals such as vibration, pressure or temperature, detecting early warning signs that operators or traditional systems would overlook.

https://www. controleng.com/ ai-and-machine-learning Online controleng.com u

KEYWORDS: Industrial AI, AI for controls

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How is AI helping your control systems?

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The evolution of control logic

For decades, industrial automation has been built on deterministic control logic: programmable logic controllers (PLCs), distributed control systems (DCS), and supervisory control and data acquisition (SCADA) platforms executing predefined rules.

As industrial environments grow more complex with fluctuating energy demands, distributed renewable resources, and interconnected

Autonomous tuning allows AI models to dynamically calibrate control loops. In conventional systems, operators manually adjust setpoints and proportional-integral-derivative (PID) parameters, AI can adapt these values in real time, ensuring stable performance despite external changes such as fluctuating loads, ambient conditions or material variability.

Predictive behaviors extend beyond diagnostics to full operational forecasting. AI can anticipate bottlenecks, energy surges or material shortages, enabling preemptive adjustments that maintain productivity and reduce downtime. By forecasting outcomes, AI helps transform maintenance and operations from reactive to predictive.

AI strengthens cybersecurity. Adaptive detec-

tion models monitor network traffic and system logs, learning from patterns of normal behavior to detect anomalies that could indicate intrusions or malicious activity. Unlike traditional intrusion detection systems that rely on fixed signatures, AI adapts continuously, reducing false positives and increasing responsiveness to novel attack techniques. These functions reshape industrial control, positioning AI as a critical enabler of safer, smarter and more resilient operations.

From feedback to in-process inference

The transition from reactive feedback to proactive inference is one of the most significant impacts of AI in ICS. Traditional systems operate on a closed feedback loop: A process variable deviates, a sensor detects the deviation and the control system adjusts.

AI-driven inference enables real-time decision-making directly within the process cycle. By deploying machine learning models at the edge, close to sensors, actuators and controllers, organizations can achieve low-latency interventions that anticipate issues before they occur. AI can analyze turbine data to detect early-stage thermal stress and adjust operational parameters before a fault develops, or it can monitor pumping systems to optimize energy usage continuously rather than only when demand spikes. This shift enhances efficiency and safety. Anticipating failures reduces the risk of accidents, environmental impact and costly unplanned shutdowns. By embedding AI inference at the edge, industrial systems gain speed and autonomy, enabling a new standard of proactive resilience.

Cybersecurity and AI in OT

Cybersecurity is among the greatest challenges facing industrial automation, and AI is emerging as a force multiplier in defending operational technology (OT) environments. Traditional approaches— signature-based detection, manual monitoring and perimeter defenses—are insufficient against modern adversaries who exploit supply chains, deploy ransomware or leverage advanced malware targeting ICS.

AI introduces adaptive threat intelligence, continuously learning from diverse datasets and dynamically updating detection capabilities. In OT, this means monitoring network traffic and processing variables and control logic to identify suspicious combinations that static tools would miss. An unexpected command issued to a valve during abnormal network activity may signal a cyberattack; AI can correlate these events in context and

raise alerts in real time. A critical advantage is context-aware anomaly detection, which reduces false positives that often overwhelm security teams. By analyzing IT-style events and process-specific data, AI ensures that alerts reflect genuine risks to operations, enabling faster and more accurate responses.

AI helps address workforce shortages in industrial cybersecurity. By automating repetitive tasks— log analysis, correlation of alerts and triage of minor anomalies—AI frees human experts to focus on complex investigations and strategic defense. This is particularly valuable in critical infrastructure, where skilled OT security professionals are scarce. Deploying AI in cybersecurity carries risks. Models must remain explainable so that operators understand why specific actions are recommended. Without transparency, trust in AI-driven security may erode. Adversaries may attempt adversarial attacks by manipulating datasets or exploiting model weaknesses to bypass detection. These risks reinforce the importance of aligning AI with governance frameworks such as ISA/IEC 62443, ISO 27000 and the NIST Cybersecurity Framework, ensuring that AI augments rather than replaces proven defense-in-depth strategies.

AI supports an augmented intelligence model, where technology enhances human judgment. This approach ensures that organizations maintain accountability and situational awareness while benefiting from automation and scalability. ce

Felipe Sabino Costa, Sc, MBA, PMP, CCNA, CISAUS DHS is senior product marketing manager of networking and cybersecurity for Moxa Americas Inc. Edited by Mark T. Hoske, editor-in-chief, Control Engineering, WTWH Media, mhoske@wtwhmedia.com.

FIGURE: AI-driven inference enables real-time decision-making in the process cycle. By deploying machine learning models at the edge, close to sensors, actuators and controllers, organizations can achieve low-latency interventions that anticipate issues before they occur. Courtesy: Control Engineering with information from Moxa Americas Inc.

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With this article online... u AI and the workforce gap uBuilding resilient and secure AI-driven control u AI enables traditional controls, self-optimizing systems

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Brandon Canclini, ABB NEMA Motors

How to double production with people, technologies, motion controls

Windsor Door transformed operations

with a culture of continuous improvement, innovation, and investments in people and operations, including $21 million in a new high-speed production line that doubled plant production and efficiency. Advanced motor technologies helped.

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KEYWORDS: Highperformance motor application, manufacturing efficiency

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indsor Door, an Arkansas-based manufacturer, provides commercial and residential garage doors by investing in people, engineering and motion-control technologies, including a $21 million for a new high-speed production line.

The high-speed line doubled plant production and efficiency. In recent years, the company has undergone a significant transformation with a culture of continuous improvement. The shift began with people before production lines.

Windsor Door drives growth by developing in-house talent and strengthening workplace culture while investing in modern equipment. The company’s approach emphasizes progress and transparency, leveraging Arkansas-made materials, local partnerships and talent to spark innovation.

Windsor Door is a third-generation family-owned company based in Little Rock, Arkansas, that designs, manufactures and distributes American-made residential and commercial garage doors, for more than 60 years.

When his sign business closed during COVID-19, Bobby Strahan joined Windsor in

2021 at the entry level, beginning his career at Windsor Door’s Los Angeles distribution center. Though his father, Bob Strahan, serves as president, Bobby Strahan chose to prove himself from the from the ground up, learning every aspect of the business. His willingness to cover second shifts and take on temporary assignments, including frontline roles, gave him a deep understanding of operations while identifying improvement opportunities.

Building stronger manufacturing

As plant manager, Bobby Strahan prioritized placing people in roles where they could succeed by capitalizing on individual strengths and leadership potential. This shifted the plant from a highstress, high-turnover environment to one defined by collaboration and respect. An approachable style broke down the old us-versus-them divide between managers and production crews. Greater trust led to stronger accountability and deeper investments.

Motion control challenges

To help drive operational change, Strahan brought in Chris Pindell, a certified engineer with a background in steelwork and automation. Hired as engineering manager, Pindell contributed technical expertise and a hands-on leadership style that aligned with Windsor’s evolving culture.

“When I came on board, there was a clear desire to change, but they needed someone who could translate vision into execution,” Pindell said.

Pindell worked to eliminate patchwork fixes and reactive maintenance repairs, replaced with structured daily meetings and long-term planning. Doing so reduced overtime with stronger plant organization and accountability.

“At first, there was some pushback,” Pindell added. “But over time, people saw the value. It kept us moving in sync and built trust across our team.”

Motors and motion controls

A major upgrade came with the decision to replace outdated, low-efficiency motors with high-performance electric motors. The resulting high return on investment from using high-performance motors was especially apparent on Windsor’s high-speed line, with significant improvements in operational uptime and energy efficiency, Pindell said. Before, frequent overheating and breakdowns caused delays and downtime. The new motors now power roll formers and drive systems with consistent long-term performance.

“Our old motors would constantly overheat,” Pindell said. With the new motors, “we haven’t had a single motor failure on the high-speed line.”

Because the motor supplier also is an Arkansas-based company, the local connection reinforces Windsor Door’s commitment to elevating the state’s manufacturing economy by partnering with businesses that share its values and dedication to quality.

$21 million toward growth

As part of the growth driven by Strahan and Pindell’s strategy, in 2024, Windsor invested $21 million in a new high-speed production line at its Arkansas headquarters. The high-speed line has doubled plant production and efficiency, in manufacturing low-cost, high-quality builder doors.

Windsor’s reliance on Arkansas-based steel mills began during a national supply chain shortage when out-of-state shipments were delayed for weeks. Local mills allowed Windsor to pick up materials and deliver them directly to its facility to keep operations moving during that critical period. Streamlining logistics helped Windsor and the Arkansas steel industry by reinforcing local demand and creating new direct and indirect job opportunities across the state. Today, Windsor sources 80% to 90% of the steel from Arkansas, reducing costs and shortening supply lines.

RFID, new lines, lower risk

Current and future projects include relocating the central compressor to a quieter, safer space and implementing a radio frequency identification (RFID) system at the loading dock to automate inventory tracking. After adding a new strut

line in August 2025 and a new production line in first-quarter 2026, Windsor anticipates hiring additional staff and adding new shifts to meet demand. Results extend beyond production improvements. Windsor Door reports a safety rate of 1.3 injuries per 100 employees annually, thanks to improved processes backed by better equipment and a more engaged workforce.

“There’s real pride in the work now,” Strahan emphasized. “We’ve created an environment where good work is recognized and rewarded.” ce

Brandon Canclini is product manager, ABB NEMA Motors. Edited by Mark T. Hoske, editor-in-chief, Control Engineering, WTWH Media, mhoske@wtwhmedia.com.

COVER: ABB’s Baldor-Reliance electric motors drive Windsor Door’s high-speed production line improved reliability, while contributing to a doubling in plant production and efficiency. Courtesy: ABB

Joshua Hunter, PE, and Lilly Vang, PE, CDM Smith

Fit motor enclosures, protective sensors to the application

Appropriate motor enclosure and motor protective sensors are vital to ensuring safe and long-lasting motor operation. The chosen motor enclosure and protective sensors provide advantages and disadvantages that must be considered based on the intended operation and environment in which the motor will be installed.

Matching industrial electric motor applications with the correct enclosure is important for safe and reliable electric motor use. Electric motors are common at most industrial facilities and are available in a variety of sizes and enclosures for use in several different applications. The application and location influence which motor design and protective sensors are recommended and/or required. Therefore, to ensure safe and long-lasting motor operation, it is important to understand the common enclosure types and general motor protective sensors for different locations and applications.

Enclosure types for motor applications

There are several types of motor enclosures. Some of the common types include:

• Open drip proof (ODP)

• Weather protective (WP)

• Totally enclosed

• Explosion proof (XP)

• Submersible

ODP motor enclosures are an open style enclosure with open vents that allow air to flow directly over the motor windings. This provides excellent motor cooling to ensure the motor does not overheat, and it provides a boost to efficiency. An unfortunate downside to this motor enclosure type is that it allows airborne debris and dust to enter the motor chamber—this can strictly limit the suitable locations for this type of motor enclosure. In general, it would only be acceptable to use an ODP motor enclosure in clean indoor locations.

WP motor enclosures are like ODP motor enclosures with a few additions. These motor enclosures come in two types: weather protective 1 (WP1) and weather protective 2 (WP2). WP1 motor enclosures are essentially ODP motor enclosures with

FIGURE 1: 75HP TEFC motor at a wastewater treatment plant. Images courtesy of CDM Smith.

additional screens to prevent the entrance of larger debris particles. This provides a modest upgrade over the ODP motor enclosures and can allow the WP1 motor enclosures to be installed in most relatively clean indoor locations, while still providing substantial motor cooling.

WP2 motor enclosures take the WP1 design to another level by adding ventilation passages with three or more directional changes for the cooling air to travel through. This addition prevents the entrance of most debris and allows these motor enclosures to be installed outdoors as long as the wind speed is not expected to exceed 100 miles per hour (mph), which could drive rain into the motor.

Totally enclosed motor enclosures, as the name implies, are completely enclosed. This prevents the inside and outside air from freely mixing, thereby impeding any ingress of water, dust, and debris. These motor enclosures are available in a variety of types, including totally enclosed fan cooled (TEFC), totally enclosed air over (TEAO), totally enclosed non-ventilated (TENV), totally enclosed force ventilated (TEFV), totally enclosed air to air cooled (TEAAC), and totally enclosed water to air cooled (TEWAC). Figure 1 shows a TEFC motor at a wastewater treatment facility.

Motor cooling for totally enclosed designs

The major difference in all these types is how the motors are cooled. Since the motor is totally enclosed, it will more easily suffer from overheating compared to the more open motor enclosure types that allow direct air flow. For example, motors within TEFC enclosures, operating at lower speeds, common on a variable frequency drive, can have overheating problems because the motor speed is tied to the fan speed. The other totally enclosed enclosures can have additional overheating problems that require design considerations, such as temperature monitoring.

XP motor enclosures are designed for hazardous locations in both inside and outside areas. The enclosure is manufactured with cast iron to withstand an internal explosion without the motor frame bursting or rupturing. The enclosures are built for different hazardous classes and indicate the type of hazardous environment in which it can be placed. These hazardous areas include Class 1, Class 2, and Class 3. Figure 2 shows an XP motor at a wastewater treatment facility.

Submersible motor enclosures, also known as completely enclosed non-ventilated enclosures, are designed to be submerged during operation. These motor enclosures are built with heavy-duty castiron frames and use the surrounding liquid for heat dissipation. If not submerged, these motors can overheat within 15 minutes during in-air operation. These motors are often submerged in hazardous environments and therefore must be designed for the different hazardous classes, much like XP motor enclosures. An additional concern with submersible motor enclosures is the ever-present risk for water to enter the motor. To prevent the ingress of water, these motor enclosures are built with moisture seals and use submersible-rated cable for power and control. However, regardless of the construction, there is always a chance for water to enter the motor, thus requiring a moisture probe to help prolong the motor lifespan.

Motor protective sensors and controls

Motors are typically subjected to electrical and mechanical stresses. Motor control protects the mechanism and operates the motor based on the application. For example, the motor for a pump transferring water from a wet well through a pipe may require an open/close indication from a check valve inside the pipe to ensure that the valve is open while the motor is running. If the check valve is closed, the motor is electrically locked and cannot start until the valve is open. The sensors are in

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FIGURE 2: 5HP XP motor at a wastewater treatment plant.

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place to turn on/off the motor, as needed, to protect against damage to the motor. However, for all motors, the following motor failure and faults are common, regardless of the application: overloads, overheating, vibration, etc.

- Overloads – Typically caused by either (1) undersizing the motor to perform the work for the application, or (2) a mechanical jamming. Overloads can cause an increased current draw for the motor, thus causing the upstream protective device to trip and/or the motor to overheat and subsequently wearing down the motor windings over time.

- Overheating – Can occur as a result of overloading a motor, insufficient cooling at low speed, locked rotor, etc. Overheating a motor can lead to significant issues such as insulation breakdown and burnt windings, which can damage the motor and potentially cause a fire.

- Vibration – Typically accompanies noise in a motor, which usually indicates motor issues (e.g., failing bearings, loose components, lack of lubrication) that may be detrimental to the motor. The vibration can also cause windings to shake the motor and cause internal flaking or abrading, which damages the motor. Vibrational failure is a more common issue for larger motors.

- Winding temperature resistance-type temperature detectors (RTDs) – Six platinum, threewire RTDs embedded in the stator windings, two per phase, symmetrically installed between stator coils where the highest temperatures will occur. The detectors require a transmitter to translate the analog signal to a discrete signal that can trigger an alarm or shutdown.

- Vibration sensors – Magnetic-mounted accelerometer used to monitor motor vibration in vertical, horizontal, and/or axial directions. The sensor requires a transmitter to translate the analog signal to a discrete signal that can trigger an alarm or shutdown.

- Moisture sensors – Conductivity probes that are typically made of a metal conductor mounted in an insulator located in the sealed motor stator cavity. If there is moisture present in the submersible motor, the probe will measure the reduction in the resistance across the metal conductor. The sensor requires a transmitter to translate the analog signal to a discrete signal that can trigger an alarm or shutdown. These sensors are mainly applicable for submersible motors. In some cases, because submersible motors are sealed, vendors offer the option to have a transmitter that can handle signal from both moisture and temperature probes in the submersible motor.

Motor temperature switches, overload devices

‘

A transmitter may handle signal from moisture and temperature probes in the submersible motor.

To protect against these common failures and faults, motors are physically equipped with the following typical motor protective sensors and controls:

- Winding temperature switch – Three embedded bimetallic temperature thermostat switches in the motor winding that can be either normally open or normally closed, depending on owner or engineer specifications. These switches act as a simple on/off switch (discrete signal) that can trigger an alarm or shutdown. Motors in XP enclosures are built for hazardous locations and may not have the capability to have normally open winding temperature switches. Therefore, the control logic for the motor needs to account for the extra control relays necessary to shut off power to the motor when temperature in the motor winding is high.

Winding temperature switches are commonly seen in smaller motors because they are a standard motor accessory offered by most vendors. Typically, for larger motors around 100 horsepower (HP) or greater, the owner may opt for a more expensive thermal protective sensor (RTDs). RTDs allow for a more flexible measuring of the temperature in the motor stator winding but requires an extra component to translate the temperature signals. Similar to RTDs, vibration sensors are also commonly paired with larger motors because spare motors of higher HP are not readily available for replacement and are not cost-effective to frequently replace. Figure 3 is a photo of an 800HP 4160V WP motor with vibration sensor and RTDs for a high service pump station.

Per the National Fire Protection Agency 70 (NFPA 70), more commonly known as the National Electric Code (NEC), Article 430.32 states that

FIGURE 3: 4160V, 800HP WP1 motor for a high service pump station.

all continuous-duty motors are required to have (1) a separate overload device that will trip at different percentages, depending on the motor ratings, and (2) a thermal protector, or electronically protected, to prevent dangerous overheating.

Overload relays are typically a part of a magnetic motor controller, which are located upstream to the motor and are not necessarily considered a motor accessory. The overload relay for a magnetic motor controller is typically made of a bimetallic material or a melting alloy that opens the circuit if a current exceeds a set point or if the overload relay is heated over a period of time, which is indicative of an undersized motor. Therefore, in most cases, when a motor is controlled by a magnetic motor controller with overload relays, additional thermal sensors are not necessary because the overload relays have the capability for thermal memory retention and overcurrent tripping.

Per NEC Article 430.126, adjustable-speed drive systems or variable speed drive (VFD) systems are required to provide protection against motor overtemperature conditions, and the protection must have the capability for thermal memory retention. Typically, VFDs do not have overload relays that can retain thermal memory. As such, additional thermal motor protective sensors and controls are required.

Figure 4 shows a control diagram for a booster pump pushing water from a wet well to a pipe with a check valve. Because the motor for the booster pump is only rated at 100HP 480V, RTDs and vibration sensors were not considered; however, since it is controlled by a VFD, the motor is required to have thermal protection (temperature winding switches). However, alarm and shutoff signals for RTDs and vibration sensors are usually represented in the control diagram as normally closed/normally open contacts from the sensor’s transmitter. The winding temperature switch (TSH) is in the motor and is a normally closed switch that only opens when the motor winding temperature increases. When the motor is energized and there are no alarms, control relay 1 (CR1) is energized, which means that all CR1 contacts in Figure 4 are shown as the opposite, and a start signal is sent to VFD to start the motor. If there are no motor winding temperature issues, TSH remains closed, which means control relay 4 (CR4) is energized and CR5 is deenergized because the normally closed contact

for CR4 is now open owing to the energized state of CR4. When the motor is overheating, either due to overloading or other issues, TSH will open, thus deenergizing CR4. If CR4 is deenergized, the normally closed contact for CR4 will close and energize CR5. CR5’s normally open contact will close when CR5 is energized and will cause CR6 to be energized. This will open the CR6 contact upstream of CR1 on Line 6, thus opening the start signal to the VFD and causing the motor to shut off.

Knowing various motor enclosures and the motor protective sensors can help with water/ wastewater treatment facilities and other rugged environmental motor applications. ce

Joshua Hunter, PE, and Lilly Vang, PE, are professional electrical engineers at CDM Smith, Denver, Colorado. Edited by Mark T. Hoske, editor-in-chief, Control Engineering, WTWH Media, mhoske@wtwhmedia.com.

FIGURE 4: Elementary control diagram for a 480V, 100HP WP motor for a booster pump.

Industrial motor protection insights

uUnderstand the different types of motor enclosures

uLearn about common motor protective sensors and controls

uDetermine how to select types of motor enclosures and motor protection devices

ANSWERS

Shane Novacek, Beckhoff Automation LLC.

Two-day changeover drops to 12 hours with new robotic system

World’s most powerful X-ray laser at SLAC National Accelerator Laboratory uses PC-based control, EtherCAT communications and robotics to speed research.

At SLAC National Accelerator Laboratory in Menlo Park, California, an advanced robotic system by Square One Systems Design and Beckhoff USA helps with maximizing access for researchers, streamlining operations to fully utilize “beam time,” and improving research outcomes. Developed by the

Jackson, Wyoming-based Square One, the TriSphere Robotic Positioning System offers unparalleled capabilities.

Square One’s patented Tri-Sphere robot is a parallel robot whose design meets the rigorous demands of high-energy physics research. Like revolute jointed industrial robots, the new robot offers six degrees of freedom in movement. Unlike traditional robots, the new robot delivers huge payload capacity, ultra-high precision, and a compact design that fits seamlessly into tight spaces. The new robot conforms to the EPICS standard (Experimental Physics Integrated Control System) which is widely adopted in the physics community. EPICS provides researchers and technicians with a standardized control system architecture and software toolkit to interface with and control high-end equipment. EPICS’ many toolkits improve process tracking performance and optimizes analysis of the metadata gathered during experiments.

SLAC's deployment of Tri-Sphere robots is part of larger upgrades to their Linac Coherent Light Source (LCLS), the world’s most powerful X-ray free-electron laser. A recent upgrade (LCLS-II) increased the capabilities of the original system from 120 pulses per second to 1 million pulses per second, and a future upgrade (LCLS-II-HE) will increase the X-ray energy. This opens a new realm of advanced research projects previously considered impossible by scientists, including a new generation of solar energy technologies, superconductors, advanced drug discovery, and other areas.

Breaking through physics research limits

The unique design of the Tri-Sphere robot offers several advantages to accommodate the rapid succession (removal and replacement) of complex research setups and operate in the demanding environment typical of facilities like LCLS. The new robot’s compact geometry means it can fit into the

FIGURE 1: Square One’s patented Tri-Sphere Robotic Positioning System is a state-of-the-art parallel robot designed to meet the rigorous demands of high-energy physics research projects such as those conducted at SLAC. Images coutesy: Square One Systems Design

tight confines of a mainstay in research facilities, the hutch, or the equipment used in research facilities where X-ray beams pass through test samples. A high-precision positioning system ensures that the robot can precisely move research equipment into beams as narrow as 100 nanometers.

The new robot supports rapid movement and repositioning of heavy objects with the accuracy required to perform cutting-edge experiments.

“The Tri-Sphere robot is designed with heavy payloads in mind and has the ability to handle up to 12,000 pounds – which is essential when positioning heavy objects in national labs like SLAC," said Bob Viola, director of engineering at Square One Systems Design. “This performance far exceeds that of conventional robots that may be more suited to industrial use.”

Maximizing beam time is essential to accommodate as many experiments as possible.

“National labs like SLAC are literally priceless national resources, and every second of beam time counts,” Viola said. “The ability to perform quick changeovers without compromising precision or reliability is a game-changer.”

Jace Walsh, chief controls engineer at Square One, said, “The Tri-Sphere’s asymmetric work envelope and software-tunable rotation point provide unmatched versatility and precision, allowing it to adapt to a wide range of experiments. This flexibility is crucial for experiments, where the ability to quickly and accurately reposition experimental setups can significantly impact research outcomes.”

The upgrade integrates automation and control technology from Beckhoff across multiple experimental hutches, allowing SLAC to conduct high-precision experiments with minimal downtime. The staff can set up a new center for the beam in the Tri-Sphere’s user-friendly front-end software, dial in new configuration settings, and enter new height parameters and rotation settings.

The SLAC Tri-Sphere systems are mounted on air casters. This enables the robots to be quickly moved in and out of different hutches. The TriSphere can handle delicate samples with precision, another key advantage.

“The robot features a vacuum transfer system to ensure that the system can handle a wide variety of container types without damage, including delicate products with soft-touch finishes,” said Viola. “This is crucial for experiments using highly sensitive sample materials.”

Automating what’s next in research and discovery

Instrumental to the success of the robotic positioning system has been the integration of PC- and EtherCAT-based control technology from the automation supplier. The Tri-Sphere currently relies on embedded PCs as the primary controller, leveraging real-time EtherCAT communication and high processing speeds to seamlessly handle all automation and control tasks. The embedded PC software for motion control.

“As a clean, all-in-one package,” Walsh said, “advanced automation technologies have been instrumental in optimizing the Tri-Sphere system’s performance,” says Walsh. “The real-time EtherCAT communication and fast processing speeds” of the embedded PC made this possible, he said. “Unlike traditional PLC technologies, PC-based automation allows us to handle all automation and control functionality on one device, with seamless integration” across the automation platform, the robot controller, and the machine vision system.

EtherCAT’s automatic addressing of its highly modular devices, numerous wiring topology options, and high device count – up to 65,535 devices in one network – ensure a robust and scalable network infrastructure. In addition, the compact size of the DIN-rail mounted EtherCAT communication terminals easily fit in compact enclosures distributed throughout the robot and.

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KEYWORDS: Motion control, machine safety, robots CONSIDER THIS Robotic system designs can provide precise repeatable motion control.

INSIDE LOOK

The Square One engineering team includes Sam Johnson (mechanical engineer), Wilton Springer (mechanical engineer), Connor McCullough (electrical engineer), Erik LaCourt (controls engineer) Bob Viola (director of engineering), Jace Walsh (controls engineering manager), Ryan Freeman (mechanical engineer), Dena Horstkotte (mechanical engineer). Beckhoff Automation field engineers, including Ryan Kirkland, provided project support.

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FIGURE 2: Tri-Sphere relies on Beckhoff's CX2033 Embedded PCs with AMD Ryzen V1202B processors as the primary controller, real-time EtherCAT communication and TwinCAT NC PTP software for motion control. Square One is a member of Beckhoff’s Integrator Group (BIG).

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FIGURE 3: The Tri-Sphere robot’s high-precision positioning system can move research equipment into beams as narrow as 100 nanometers. The Beckhoff CX2033 runs TwinCAT NC PTP software for motion control. EtherCAT Terminals can also incorporate Beckhoff’s compact drive technology. Square One integrated EL7041 and EL7047 stepper motor terminals to handle some Tri-Sphere’s motion requirements. The system uses EL5042 dual interface terminals for the required encoders.

Not just relegated only to data acquisition, the terminals can also incorporate compact drive technology. Integrated stepper motor terminals handle some Tri-Sphere’s motion requirements. The system also leverages dual interface terminals for the required encoders, enabling direct connection of absolute encoders with BiSS C or SSI interface.

Safety I/O terminals and Safety over EtherCAT (FSoE) technology provide robust machine safety functionality that integrates seamlessly with SLAC’s personnel protection system and equipment protection connections from the lab for sending safety status whenever personnel are in a hutch and initiating e-stops if they’re ever needed.

“TwinSAFE supports these unique safety requirements, ensuring safe access to the hutches at all times and reliable control of these powerful positioners,” said Viola.

FIGURE 4: A detailed view of one of the three jack units that comprise a Tri-Sphere robot. Beckhoff’s TwinSAFE I/O terminals and (functional) Safety over EtherCAT (FSoE) technology provide robust machine safety functionality that integrates seamlessly with SLAC’s personnel protection system and equipment protection connections from the lab for sending safety status whenever personnel are in a hutch and initiating e-stops if they’re ever needed.

Square One is expanding use of the new system.

“We are in the process of upgrading several older Mark IV systems around the country to Beckhoff controllers and exploring new applications for Tri-Sphere technology,” said Viola. “Flexibility and scalability” of the automation and control solutions are key to fueling our ongoing innovation, he said.

‘Integrated stepper motor terminals handle some motion requirements.’

The Tri-Sphere system is compatible with the seismic anchoring requirements typical of installations in California. This ensures that the systems can withstand seismic activity and maintain their precise positioning. Through a kinematic base designed by Square One, the Tri-Sphere meets the demanding requirements to withstand extreme vibrations.

A high-energy future for the world’s leading research projects

“When SLAC can prepare an experimental work setup on a Tri-Sphere outside of the working hutch without shutting down the beamline, it speeds things up,” Viola says. “The system reduced the time required for SLAC experiment changeovers from two days to just 12 hours.”

Looking ahead, the potential applications for the Tri-Sphere Robotic Positioning System are vast. Advanced diagnostics and modularity of the automation have been “crucial in achieving new levels of safety and reliability,” Walsh said. The automation company’s responsiveness and commitment to application engineering and technical support with field engineers have helped.

The Tri-Sphere, as demonstrated by its successful deployment at SLAC, is helping overcome key challenges in many areas of scientific research. With proven flexibility and performance to adapt to a wide range of difficult testing spaces, the system has since been deployed at other world-renowned laboratories to help reach the next big discovery. ce

Shane Novacek is marketing communications manager, Beckhoff Automation LLC. Edited by Mark T. Hoske, editor-in-chief, Control Engineering, WTWH Media, mhoske@wtwhmedia.com.

Sunil Doddi, CAP, CFSE, CFS

How to create a physical AI security, safety framework

Explore

the security and safety aspects of the emerging industry known as physical artificial intelligence (AI). As physical AI evolves from concept to reality, understanding its implications for human safety and security becomes critical.

It’s necessary to explore the security and safety aspects of the emerging industry known as “physical AI,” a term heavily promoted by Nvidia. As the physical artificial intelligence (AI) evolves from concept to reality, understanding its implications for human safety and security becomes critical.

What is physical AI, LLM AI?

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KEYWORDS: Physical AI, safety, cybersecurity

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Physical AI can be defined as an artificial intelligence system with integrated mechanical components that perform physical activities in a physical environment with real-world sensor feedback. These systems represent a fundamental departure from traditional AI systems, such as large language model AI (LLM AI), which work in virtual spaces. Unlike traditional LLM AI systems, such as ChatGPT by OpenAI or Claude by Anthropic, which operate in virtual spaces, physical AI systems perform physical tasks and activities in the physical world.

Other known terms related to these systems are generative physical AI, embodied AI and there may be other terms as well. While there are subtle differences between these systems, those distinctions are not particularly relevant for the security and safety analysis in this article. All these systems share similar challenges and risk profiles that need to be examined comprehensively.

Examples of physical AI

The examples of physical AI include fully or partially autonomous personal robots and vehicles (AVs). This article covers physical AI systems comprehensively rather than a specific industry. However, there are some references to industrial applications, personal robots and autonomous vehicles.

Physical AI scope extends far beyond these primary examples. Industrial collaborative robots, autonomous drones and similar systems with AI integration all fall under this category. Each of these applications presents unique challenges while sharing common security and safety concerns.

Physical AI vulnerabilities

Physical AI systems exhibit security vulnerabilities remarkably similar to those found in industrial automation control systems (IACS). As with any protection methodology, physical AI adheres to the principle that a system is only as strong as its weakest link, and safety-security vulnerabilities can emerge from multiple interconnected components.

The complexity of these systems cannot be overstated. Each Physical AI system might contain millions of lines of code. This massive codebase creates an extensive attack surface where vulnerabilities can easily hide, making thorough security validation a challenging task.

AI processing can occur in different architectural configurations. Systems can run remotely on dedicated servers, operate in cloud environments, process locally on edge devices or employ hybrid models that combine multiple configurations. Most AI systems typically operate on server-based or cloud infrastructures, though there is a growing trend toward hybrid implementations.

Edge AI, which processes data locally on a device, may offer better privacy protection and fewer security vulnerabilities compared to remote or hybrid models. However, this approach comes with its own limitations, particularly regarding computational

power to perform tasks reliably, and hence its inability to handle complex processing tasks.

The security risks facing physical AI systems include the possibility of remote hijacking, where malicious actors gain unauthorized control over the physical AI system either for ransom reasons or, attackers might focus on controlling critical safety functions or deliberately disabling essential safety features, potentially putting human users at risk.

Risks and human factors for physical AI

Like IACS systems, physical AI systems face challenges from unintentional mistakes. Similar to security vulnerabilities in IACS systems, unintentional threats may occur during software updates or system modifications, potentially introducing new risks or exposing previously unknown vulnerabilities.

Over-the-air software updates present another category of risk. Since most current physical AI systems operate on remote servers, cloud platforms or hybrid configurations, attacks using human-in-themiddle techniques could potentially occur during the update process, compromising system integrity.

Cybersecurity framework, physical AI

The cybersecurity approach for physical AI can be built upon established concepts from the IACS security lifecycle, which provides a proven framework starting from the initial design stage and continuing through final decommissioning. However, due to the intimate nature of how physical AI systems work closely with humans, an additional critical step must be incorporated: secure decommissioning.

This secure decommissioning requirement stems from significant data gathering and privacy concerns unique to physical AI. These systems are specifically designed to assist users at a deeply personal level, which means they inevitably gather extensive amounts of personal data about the users. Physical AI systems are more intrusive than traditional devices like laptops or smartphones due to their continuous physical presence in our personal spaces and their ability to observe and record our daily activities.

The privacy implications extend far beyond what most people might initially consider. These systems collect biometric data, including facial features and voice recordings, behavioral patterns, environmental mapping data and detailed logs of personal interactions. Simply disposing of used physical AI

systems in traditional disposal facilities could allow some of the sensitive information to fall into the wrong hands and become subject to reverse engineering attempts by malicious actors.

Data gathering concerns must be addressed at the fundamental design level of these systems rather than being treated as an afterthought. The types of sensitive information collected include biometric identifiers, detailed behavioral analysis, comprehensive environmental mapping, and extensive records of personal interactions and preferences. Ethics must be considered as another critical component that should be integrated throughout the entire development and deployment process.

The human factor introduces additional complexity through what I call “trust bias.” Many people naturally tend to trust automated systems and assume the automated systems will not make mistakes, particularly when these systems perform reliably in routine situations. However, unless a task is repetitive and operates in a controlled environment, there remains a slight chance of error that could potentially harm someone in the physical world. Even with repetitive tasks, systematic design faults might still occur, creating unexpected failure modes.

Functional safety framework, physical AI

Any mechanical component that performs tasks in the physical world carries the inherent potential to harm people if it does not function as designed

FIGURE 1: Development of physical artificial intelligence (AI) should integrate safety, reliability, security, training and ethics. Graphics courtesy: Sunil Doddi

‘Even with repetitive tasks, systematic design faults might still occur.’

ANSWERS

or if it encounters situations outside its operational design. The established IEC 61508 standard [covering functional safety of electrical, electronic and programmable systems] could serve as an excellent starting point for functional safety integration in physical AI systems, providing a systematic framework for identifying, assessing and mitigating safety risks.

However, traditional safety approaches are not sufficient for the dynamic environments in which physical AI operates. An adaptive safety approach proves more practical than conventional traditional safety measures, which typically rely on fixed failure states and predetermined response protocols. An adaptive safety approach becomes essential because physical AI systems must operate in unpredictable and highly changing, adaptive environments where static safety rules may prove inadequate.

Recent viral videos circulating on the internet have demonstrated these challenges clearly. In a notable incident, a robot became highly unstable, and an investigation revealed that the primary cause was its programming instruction to maintain stability at any cost; the programmers did not think that it could create dangerous scenarios. This example perfectly illustrates why dynamic safety assessment approaches are more crucial for physical AI than traditional fixed safety protocols.

Another recent video showed a robot stepping on a child's foot, apparently because the system failed to properly recognize or appropriately respond to the presence of a human. These realworld incidents highlight the critical importance of sensitive sensor detection and appropriate adaptive safety response protocols in physical AI systems.

A simple shutdown function in physical AI systems is insufficient. Because humans interact with these systems in unpredictable ways, one shutdown mechanism may not be adequate, unlike in a typical process plant, where a de-energized state is fail-safe in most cases. A “de-energized” state in physical AI may not equate to a safe shutdown.

Privacy and ethics implications

When considering privacy concerns related to

physical AI, we should not assume that camera vision represents the only privacy threat. The absence of visible cameras does not mean that users cannot be observed or monitored. Advanced sensing technologies like LiDAR can create detailed three-dimensional maps of environments and detect human presence and movement. Similarly, WiFi sensing technology can analyze radio frequency patterns to detect and track human movement within rooms, even through walls and other obstacles.

These sensing capabilities mean that Physical AI systems can maintain comprehensive awareness of human activities and behaviors even when traditional cameras are not present or are disabled. This creates unprecedented challenges that existing privacy frameworks may not adequately address.

The comprehensive nature of data collection by physical AI systems extends far beyond what most users might expect or consent to. Systems can track daily routines through movement analysis, record conversations and build detailed profiles of personal preferences and behaviors over extended periods.

Physical reasoning and AI limitations

Physical reasoning can be defined as a cognitive process that involves how objects behave in the physical world. Understanding physical reasoning represents one of the main challenges facing current physical AI implementations. At the human level, this process involves innate physical understanding that humans (and even many animals) have naturally. Although the AI systems use advanced predictive and other techniques, they do not perceive things as we humans naturally do. We may never know how the AI systems truly perceive these scenarios, though they keep getting better with new technologies.

For example, when a car turns and disappears from view, we instinctively understand that the car continues to exist on the other side of the road. From an AI system's perspective, however, the car may have simply disappeared when it moved out of the sensor’s range. This limitation in understanding object permanence represents a fundamental challenge in physical reasoning for AI systems, which can lead to inappropriate responses or behaviors when objects move out of direct sensor range.

AI hallucinations are a critical challenge

AI hallucinations represent perhaps the biggest

FIGURE 2: Deployment of physical artificial intelligence (AI) can be enhanced by using the framework as shown.

challenge facing physical AI implementation today. These hallucinations are erroneous outputs generated by AI systems that can create significant safety challenges with real-world consequences. When AI systems mistake objects or mishandle situations due to hallucinations, the results can be dangerous in physical environments.

AI hallucinations occur primarily due to poor training data quality, including various forms of input bias that can skew the system’s understanding of physical reality. Inadequate training data, biased sampling data sets, or insufficient diversity in training scenarios can contribute to an AI system’s tendency to hallucinate and misinterpret real-world situations.

Risk mitigation and safety strategies

Addressing these challenges requires a comprehensive approach that begins with establishing human evaluation metrics and realism scoring systems for any physical AI implementation. Training AI software in carefully designed virtual environments is the crucial step, and these virtual environments should have their own measurable physical reality score (PRS) to ensure they adequately prepare AI systems for real-world operation. Digital twin technologies can provide valuable tools for creating these training environments.

The software development process must prioritize personnel safety from the earliest design stages rather than treating safety as an add-on feature. This means incorporating safety considerations into fundamental system architecture decisions and maintaining safety as a core requirement throughout the development lifecycle.

A human operator center should maintain responsibility for emergency response, operating 24/7 with qualified personnel capable of immediate intervention when physical AI systems encounter problems or unusual situations. This human oversight provides a critical safety net when AI systems reach the limits of their capabilities or encounter scenarios outside their training data.

Since training data inevitably involves real people and their personal information, ethical considerations must be integrated with security and privacy concerns throughout the development process, with appropriate consent mechanisms and privacy protections. The entire system development

process should begin with ethical frameworks rather than attempting to add ethical considerations after the technical development is complete.

Future standards, regulatory needs

The unique challenges presented by physical AI necessitate the development of comprehensive new standards that address the issues identified above. Current standards and regulations, while valuable, were not designed comprehensively for safety and security of physical AI. Development of such standards will require collaboration between industries, privacy advocates, ethicists and regulatory bodies to ensure comprehensive coverage of all relevant concerns.

The rapid advancement of physical AI technology demands that these frameworks should be flexible enough to accommodate technological advancements. Physical AI presents us with a transformative technology that improves human life, but it also introduces unprecedented risks.

Testing framework for physical AI risk

Before deploying any physical AI system, it should undergo a physical reality interaction test (PRiT), analogous to a site acceptance test (SAT) in IACS environments. PRiT should include a scoring system, or a “physical realism score,” modeled on a risk-matrix framework, with defined quantitative or qualitative criteria. Initially, the industry may rely on qualitative or semi-quantitative scoring; over time, fully quantitative metrics may be developed. PRiT results must directly inform the system’s risk assessment, ensuring that identified vulnerabilities are addressed before release.

Once deployed, continuous physical reality interaction testing (CPRiT) should occur, and CPRiT feedback should be incorporated into the system design. CPRiT performance data should then feed back to the digital twin for further optimization. This closed-loop feedback process enables ongoing optimization of reliability and safety. ce

Sunil Doddi is a Certified Automation Professional, Certified Functional Safety Expert and Cybersecurity Fundamental Specialist. Edited by Mark T. Hoske, editor-in-chief, Control Engineering, WTWH Media, mhoske@wtwhmedia.com.

‘Current standards and regulations, while valuable, were not designed comprehensively for safety and security of physical AI.’

Insightsu

Industrial physical AI insights

uExplore physical AI and how it differs from LLM AI.

uUnderstand the fundamental physical AI security vulnerabilities, unintentional risks and human factors, for physical AI along with cybersecurity challenges.

uLook at risk mitigation and testing framework for physical AI development.

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C a S e S tudy

Reliable flow measurement despite difficult conditions

Challenge: Schluchseewerk AG wanted to record and optimize the efficiency of its turbines at the pumped storage plant in Wehr, Germany, despite difficult flow conditions. For this purpose, it was looking for a reliable flowmeter.

Solution: Endress+Hauser’s Proline Prosonic Flow W 400 ultrasonic flowmeter, is installed from the outside on pipes. It measures independently of the pressure and is easy to install, saves space and does not interrupt the process.

ReSult: Schluchseewerk AG has precise measurement despite short inlet runs and disturbed flow profiles, precise determination of turbine efficiency, and optimum process control and monitoring.

SummaRy: At the Schluchseewerk AG Wehr pumped storage plant, water is pumped from the Wehra basin to the higher Hornberg basin. When the water flows back, it generates electricity using a turbine and generator.

Previously, water volumes were estimated based on basin size and turbine output, but precise measurement is crucial for optimizing turbine efficiency. Schluchseewerk AG needed accurate flow measurements to verify performance improvements after changing the turbine impeller geometry. However, finding a suitable flowmeter was challenging due to large pipe diameters (up to 2 meters), high pressures (over 60 bar), difficult access, and short inlet runs.

Endress+Hauser’s Proline Prosonic Flow W 400 with FlowDC function provided the solution. This noninvasive ultrasonic flowmeter installs externally on pipes up to 4 meters in diameter, measures independently of pressure, and is easy to install without interrupting the process. It maintains high accuracy even with short inlet runs and disturbed flow profiles, ensuring reliable measurements under challenging conditions.

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Modernizing a Complex Legacy Line with Control Upgrades and Virtual Commissioning

CHALLENGE:

Multi-day production stoppages from failing Allen-Bradley PLCs cost an automotive manufacturer revenue and customer relationships. Limited diagnostics, scarce spare parts, and tight schedules meant they couldn’t afford extended shutdowns or failed upgrades.

SOLUTION:

Patti Engineering replaced legacy PLCs with Siemens S7-1500 controllers while keeping production running. Virtual commissioning using Siemens Process Simulate and FANUC’s ROBOGUIDE tested all logic before installation - dramatically reducing risk and downtime.

RESULTS:

Immediate ROI through restored uptime and eliminated emergency downtime. Enhanced diagnostics slashed troubleshooting time, while the digital twin foundation enables predictive maintenance and continuous optimization - transforming a one-time investment into ongoing competitive advantage.

SUMMARY:

When unplanned downtime began eroding profits and threatening delivery commitments, a major automotive manufacturer faced a critical decision: continue patching a failing system or invest in a modernization that would pay for itself through restored productivity.

Running three shifts with over 100 conveyor sections and 12 robotic cells, the engine assembly line’s complexity meant any upgrade attempt carried significant financial risk. Patti Engineering eliminated that risk through virtual commissioning by building a complete digital replica using Siemens Process Simulate and FANUC’s ROBOGUIDE to test every scenario before installation.

The phased rollout enabled the line to keep running between upgrade installations, protecting revenue during the transformation. The new Siemens S7-1500 platform delivered immediate returns: restored uptime recovered lost production capacity, faster diagnostics slashed repair costs, and improved fault recovery prevented cascade failures.

Beyond immediate savings, the project created lasting value. The simulation environment evolved into a digital twin that enables performance optimization and predictive maintenance, turning a capital investment into an ongoing competitive advantage. The project demonstrated how strategic modernization transforms cost centers into profit drivers.

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CASE STUDY

Heritage Petroleum, Trinidad and Tobago

SUMMARY:

The largest oil producer in the Caribbean used the DataHub™ Smart MQTT Broker in a recent project to integrate AVEVA™ InTouch, Historian, and Web Client with new IoT-enabled wellmonitoring devices, incorporating sophisticated redundancy switchovers based on MQTT quality of service. The system integrators responsible for the project performed a systemwide upgrade instead of rip-and-replace, which dramatically cut hardware costs and saved months of time.

KEY TAKEAWAYS:

1. Full integration of remote sensor data with AVEVA™ InTouch, Historian, and Web Client, using Cogent DataHub MQTT Smart Broker technology.

2. Sophisticated redundancy switchovers based on MQTT quality of service.

3. System-wide upgrade instead of ripand-replace dramatically cut hardware costs and saved months of time.

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CASE STUDY

Yokogawa and Aramco Achieve Major AI Milestone at Fadhili Gas Plant

CHALLENGE:

Aramco sought to improve the efficiency, stability, and sustainability of operations at its Fadhili Gas Plant amid fluctuating ambient conditions and increasing energy demands.

SOLUTION:

Yokogawa deployed multiple coordinated AI agents based on the Factorial Kernel Dynamic Policy Programming (FKDPP) reinforcement learning algorithm. Trained in a plant simulator and integrated with Yokogawa’s CENTUM VP system, these autonomous control agents optimize acid gas removal operations.

RESULTS:

Initial results show a 10–15% reduction in amine and steam usage, a 5% reduction in power consumption, greater process stability, and significantly reduced need for manual operator intervention.

SUMMARY:

Yokogawa has reached a historic milestone in industrial AI deployment with Aramco at the Fadhili Gas Plant in Saudi Arabia. The project marks the first successful implementation of multiple autonomous control AI agents in a live plant environment.

Using Yokogawa’s proprietary Factorial Kernel Dynamic Policy Programming (FKDPP) reinforcement learning algorithm, the AI agents autonomously optimize acid gas removal (AGR) operations. Developed and validated through simulation before live integration with the CENTUM VP control system, the solution enables safe and reliable autonomous process optimization.

Early operational data indicates significant performance gains, including a 10–15% reduction in amine and steam use, around 5% lower power consumption, and improved process stability—even under changing ambient conditions.

Aramco’s Senior Vice President of Engineering Services, Khalid Y. Al Qahtani, highlighted the collaboration as part of Aramco’s broader AI-driven efficiency and sustainability strategy. Yokogawa President & CEO Kunimasa Shigeno emphasized that the project demonstrates the company’s readiness to lead the transition from industrial automation to industrial autonomy (IA2IA), marking a key step toward a new era of intelligent, autonomous plant operations.

Back to Basics

GETTING STARTED WITH INDUSTRIAL AI

If AI is hard, how are manufacturers gaining quick benefits?

MESA’s research on “Making Manufacturing Analytics and AI Matter,” shows that companies using artificial intelligence (AI) are gaining benefits, amid data challenges.

Is it hard to benefit from artificial intelligence (AI) in manufacturing? The answer is: in some ways yes, in others no.

MESA’s latest research program, “Making Manufacturing Analytics and AI Matter,” shows that companies using AI are already gaining benefits, sometimes quickly. However, these companies face significant challenges in reliably having the data they need to feed these systems.

These are difficult times for most manufacturers. Uncertainty reigns in supply, workforce, regulation, supplier quality, cybersecurity threats, competition, costs and more. In our survey of over 420 people from manufacturing companies, 100% report that some of those challenges are having a significant negative impact on their business.

Fortunately, the same 100% are gaining benefits from their analytics and AI programs. The top areas that benefit significantly from analytics match very nicely to those challenges. Most respondents have achieved cost reductions, efficiency, productivity, quality and error-proofing. Over a third have seen an improvement in on-time perfect orders.

5 ways leaders are using industrial AI

Nearly every manufacturer is investing in operations, analytics or AI. The vast majority use predictive analytics (more than 4 in 5), and 25% use generative AI (GenAI). As always, not everyone has the same level of business success. To understand what matters, we sorted top performers versus others based on their ability to improve common operational metrics. The differences in operational metrics also showed better business performance. What these leaders are doing differently from others:

• Invest in smart manufacturing, including analytics and all forms of AI

• Focus on getting data to operations personnel for their decisions and tasks.

• Ensure AI use cases are based on business value

• Seek vendor-delivered analytics specific to their industry or embedded in applications.

• Experiment with analytics and AI to begin the learning.

Predictive AI and machine learning (ML)

Can we use analytics to preempt problems and optimize the future? What does it take to go beyond reports that look at history and dashboards that describe current status to be proactive? Most manufacturers in this research have been using predictive analytics, such as machine learning (ML), for over a year. About half of the top performers have used predictive tools for over three years.

One thing top performers do differently is focus predictive AI on product and process quality. These are upstream and can cause customer-facing issues that others try to predict. Predicting quality problems is crucial to performance on business metrics such as on-time delivery and customer satisfaction, as well as cost of goods sold and profitability.

Top performers are also more likely to use a digital twin of the plant as part of their efforts to predict issues in manufacturing. This virtual representation of a plant enables safe offline problem forecasting. Digital twins also support whatif analysis for process and automation changes, taking simulation to the next level of realism and accuracy based on actual plant operations.

“Making Manufacturing Analytics and AI Matter” research was conducted by Tech-Clarity Inc. (www.tech-clarity.com), for MESA. See an executive summary at https://members.mesa.org/ap/ Form/Fill/pl8QoCBp. ce

Julie Fraser is vice president of research for operations at Tech-Clarity, MESA International lifetime member and facilitator of MESA’s Smart Manufacturing Community. Edited by Mark T. Hoske, editor-in-chief, Control Engineering, WTWH Media, mhoske@ wtwhmedia.com.

Onlineu

With this article online...

u Challenges and benefits of predictive AI.

uGenerative AI for industrial manufacturing

u Challenges, benefits of GenAI

uData as foundation for industrial AI benefits

u It is time to invest in AI

uMESA AI research back story (www.mesa.org).

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Control Engineering November December 2025

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