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Industrial WSN Analysis Cost and Functionality Comparison Real World Deployment Evaluation

Mick Flanigan Suhaime Hassim Intel Corporation


Introduction Mick Flanigan Intel Corporation, Oregon, 11 years

Suhaime Hassim Intel Corporation, 7 years

8 years as Vibration Engineer

7 years as Vibration Engineer

Deployment Engineer for Intel Mote Technologies in Factories, Ships, etc

Lead Deployment Engineer for JFS1 Project

Developed Intel’s first centralized machine modeling and alarm system based on PDM

Lead Engineer for Global PDM team: Asia, EU, US, Central America, Middle East

Currently work in Intel Research on Body Area Sensor network and other Health applications

Currently based in Oregon on assignment as TD Engineer for Wireless Vibration systems evaluation


Problem Statement „

Market available models of data collection for PDM applications separate into two classes: „ „

Online Surveillance and Protection systems Handheld Collection system


Problem Statement (cont.) „

„

„

These systems do not address the growing need to monitor low cost critical system at higher frequency than monthly Online systems are very expensive: practical for million dollar+ equipment or sub-hourly collection needs Handheld capture is only practical on a >monthly basis, as labor to support rises quickly


Handheld System: Magnetic Advantages „ Low initial cost „ Good for small plants „ Great for Infrequent Collection „ Excellent diagnostic tool

Disadvantages „ User errors „ Un-correlated data „ High labor costs as machine counts scale „ Difficult to support high collection frequency


Handheld System: Wired Advantages „ Easy to scale „ Good for low collection frequencies „ Moderate implementation costs „ Excellent measurement repeatability

Disadvantages „ User errors „ Costs similar to wireless system „ High labor costs as machine count scales „ Difficult to support high collection frequency


Online Ethernet System Advantages „ Large machine population support „ Reliable and frequent data collection „ Excellent for machine protection

Disadvantages „ High installation cost „ Costly to scale „ Lock into proprietary system, costly to change


Wireless Systems Advantages „ Large machine population support „ Reliable and frequent data collection „ Low cost „ Easy to Scale

Disadvantages „ Unable to capture sub minute intervals in our model „ Subject to RF Interference „ Security is a concern


High Level Architecture Data Server User definition of queries via GUI Push query config to sensor net Fetch sensed data Convert and import into Enshare

Intranet

Backbone

Sensor Clusters

Stargate Cluster Head Store query configuration Control SN sleep/wake cycle Acquire data from SN Store results when disconnected Sensor Node Form mesh topology Accept queries, sense, and respond Edge processing (FFT, gSE)


SNAPS Navigation Tree

Menu & Toolbar


System Configuration Parameters FFT Data Acquisition Data Type

Sensor Type

FMAX

Base Units

Lines

Tach State

Calibration Averages

Single Value Data Acquisition Units

Sensor Zero Slope

Data Type

Sensor Type

FMAX

Base Units

Lines

Window

Filter

Filter

Warm up Time

Warm up Time

Signal Detection

Signal Detection

Units Sensor Zero

Calibration Averages

Window

Example: Data Type: Spectrum Sensor Type: Accelerometer Engineering units: G’s Calibration Value: 100mV/e.u. Sensor Zero: 0 G’s Fmax: 90000 Lines: 3200 Averages: 4 Window: Hanning Filter: None Warm up time: 10 deci-seconds Signal Detection: Peak Tach present: No If no, RPM: 1785 Output Units Desired: IPS

Tach State

Slope

Not user configurable

Example: Data Type: Numeric Sensor Type: Temperature Engineering units: deg.K Calibration Value: 10mV/e.u. Sensor Zero: 240 deg.K Fmax: 1500 Lines: 25 Averages: 1 Window: None Filter: None Warm up time: 10 deci-seconds Signal Detection: Peak Tach present: No If no, RPM: 0 Output Units Desired: deg.F


Intel Mote 2

Picture and Features


MDA440 Sensor Board

„

Sensor inputs „

6 AC-coupled accelerometer inputs „ „

„

3 DC-coupled 0-5V inputs (current transducers + MISC) Tachometer input (for RPM and triggering purposes) Imote2

Bottom

Top

„

Provides 6 channels of temperature integrated vibration sensors Surface temperature readings in Deg F, Deg C or Deg K


Supported Sensor Types „

Accelerometers: „ „ „

„

Voltage Inputs: „ „

„

10, 50, 100, 500 mV/G calibration values Supports integrated temperature Supports 18-30VDC ICP styles only 0-5VDC channels Example: Current transducers, pressure, temperature, flow, etc

Tachometer Input: „

Supports magnetic proximity type sensors: channel will support up to 5V pk-pk.


Triggered Measurements „

Utilizing a channel as a trigger allows for three distinct benefits: „

Users link to vibration channel to act as an operational gate: „ „

„

User sets a minimum value assigned to the trigger channel If the minimum value is not met, the acquisition of further data from the specified node can be cancelled, preventing zero data entering the database

Added benefit: Node does not collect unwanted data, extending battery life


Various Types of Vibration Monitoring „

Convert raw data into usable data as defined by end users: „

„

„

Raw Waveform data is rarely used in industrial applications Fast Fourier Transform (FFT’s) are most useful in analysis of machine defect frequencies Overall magnitude representations provide good alarming points


Why do Signal Processing on Mote? „

„

Problem: Machines typically have vastly different sampling rate requirements Possible solutions: „

„

„

Complex hardware that can dynamically adjust sampling rate combined with simple software driver: Expensive, custom per app Simple Hardware with one sampling rate and simple software driver: Not flexible, does not cover all machine types Simple Hardware with one sampling rate, software driver that dynamically adjusts sampling rate: Need the CPU power, Memory for real-time


Edge Processing using Intel Mote2 Frame processing Real time processing gSE A/D samples at 100 kHz

Fine-tune sampling rate

Power-of-2 decimation

Hanning window

Averaging over N frames

DC removal

FFT

Ready for transmission

Magnitude


Achieving Long Battery Life „

Contributing factors to battery lifetime „

Power consumptions in different modes „ „ „

„ „ „ „ „

Sensing (sensor + board power consumption) Data transfer (mote + radio) Sleep (mote in sleep mode)

Sensor types and quantity Collection frequency Measurement types and set points Number of nodes in a cluster Use trigger to stop collection on non-running machines


Improving the Battery Lifetime: Implemented Improvements „

Edge processing „

„

Supporting trigger based collections „

„

Perform down-sampling, FFT at the Imote2 saves power Avoid collecting and transmitting data when machines are off

Software optimizations „ „

Reduced awake time from 90 to 29 mins per hour (10 Node) Improved discovery, routing, warm-up time, averaging, caching


Advantage of Edge Processing Current draw in different modes

160

272

RF time (s) assuming 50 kbps

80

250 200 150 100 50 0 Collecting data

Processing data

Transfering data

Mote Mode

Processing Time (s) Capture Time (s)

40

Downsampling performance using WMMX 300

0 Raw data transfer

Local realtime down sampling

Local spectral analysis

Filtering time for 4K blocks (ms)

Time (seconds)

120

Current Consumed (mA)

Local processing advantage

250

200

" C code"

150

WMMX optimized code

100

50

0 2

4

8

16

32

64

Downsampling factor

128

256

Sampling rate (real-time constraint)


Overview of Intel JFS1 „

„ „

„

Located in Hillsboro Oregon Built in Nov 2005 Humidity and temperature control environment House critical computing for Intel manufacturing


Deployment Area High Temperature Room

Cooling Towers

Low Temperature Room

Roof


JFS1 Network Topology B

D

E

C Cluster ID

IP Address

TO #

Location

Motes

PID

Config

Cluster B

10.7.244.203

JFS1SCC1JFS1SCC1CSC29CSC29-A15

JFS1 CTs

8

0x78

15.4 Æ Eth

Cluster C

10.7.244.202

JFS1SCC1JFS1SCC1CSC29CSC29-A16

JFS1 LT CH

6

0x29

15.4 Æ Eth

Cluster D

10.7.244.201

JFS1SCC1JFS1SCC1CSC29CSC29-ASCC1SCC11/4011/401-D2

JFS1 HT CH

11

0x17

15.4 Æ Eth

Cluster E

10.7.244.200

JFS121JFS1213/4523/452-D2

JFS1 Roof

4

0x6E

15.4 Æ Eth


Monitoring Details Equipment Pump Chiller Air Compressor Cooling Tower Air Handler

Qty 8 5 2 8 2

Sensors 40 20 20 40 12


Devices Wilcoxon 793T – 10/100mV/G accelerometers with embedded temperature capability.

Flex-Core AC Split-Core Current Transducer .

Honeywell tachometer model 3015A


Devices Crossbow MDA 440: Series of sensor/data acquisition boards designed to interface with Imote2.

Stargate: High-performance processing platform designed for sensor, signal processing, control and wireless sensors networking applications.


Data Types and Frequency Data types: „ RPM „ Amperage (Current Transducer) „ Temperature (deg F) „ Battery voltage (Vdc) „ Accelerometer bias voltage (Vdc) „ Vibration IPS (magnitude and FFT spectrum) Data Collection Frequency: „ Once every 24 hours


Results

FFT (ips)

Waterfall FFT (ips)

Bias voltage (Vdc)

Battery voltage (Vdc)


Battery Lifetime Effect of collection frequency

Battery life (days)

Effect of Collection Frequency on Battery Life 600 500 400

10 Node Cluster

300 200

4 Node cluster

100 0 1

3

6

9

12

24

Number of collections per day „ „

Assumes machinery always on Data based on model and chiller room profiling Based on 6 accels capturing FFT and MAG IPS and G’s, temperature BOV, Battery voltage, RPM and one current transducer input


Key Learning „

„

„

„

Network Limited to 35 nodes as software is written today 15.4 communications are robust indoors and outdoors Stargate connectors can be susceptible to vibration Collection cycles more than every 8 hours may dictate hard power to motes


Key Development Areas „ „

„

„

Need board for 4-20mA sensors Compartmentalize clusters and XML programming to allow system scaling, ease of management Harden Stargate systems, or find a better solution with embedded 802.11, 15.4 solutions Integrate devices into CBM software platform


Cost Comparison: JFS1 Project Costs COMPONENTS

COSTS

Stargates

$3,600

MDA440’s

$23,925

Sensors

$6,120

Cables

$25,160

Tachometers

$1,450

Current Transformers

$1,700

Server

$3,800

Labor

$48,300

TOTAL

$114,055


JFS1 Deployment Cost Comparison Components

Wireless

Handheld Magnetic

Handheld Wired

Online Ethernet Based

Switch boxes

-

-

$17,250

-

Enwatch cabinets

-

-

-

$174,000

Handheld

-

$19,250

$19,250

-

Stargates

$3,600

-

-

-

MDA 440’s

$23,925

-

-

-

Cables

$6,120

-

$6,120

$6,120

Sensors

$25,160

$1,200

$25,160

$25,160

Tachometers

$1,450

-

$1,450

$1,450

Current Xformers

$1,700

-

$1,700

$1,700

Server

$3,800

$3,800

$3,800

$3,800

Labor

$48,300

$14,000

$43,100

$57,300

TOTAL

$114,055

$38,250

$117,830

$269,530


Cost per Point

Equipment and Installation costs only $1,200 $964

$1,000 $800 $600

$447

$408 $400 $171* $200 $0 Wireless

Handheld

Handheld Wired

Ethernet

* Does not include monitoring remote Cooling Tower and AHU points


Collection Frequency

Cost vs. Capture Seconds

Ethernet Surveillance, Protection

Minutes

Hourly

Ethernet Capture

Wi-Fi Based Capture

Daily

Weekly

Handheld Magnetic

Handheld Wired

Monthly

$10

$100

$500

Cost per Point

$1000


Major WSN Benefits „ „

„

„ „

Less costly to deploy than Ethernet Provide more reliable, more frequent collection than handheld model at no added cost JFS1 model excellent for moderate to infrequent data collection (>hourly) Edge processing reduces data transmission size Large opportunities for edge network intelligence (rule sets, Statistics, Bayesian)


PRE-WORK TO DEPLOY WSN Understand your application:

„ „ „ „ „ „

„

Data Type, Collection interval, CPU needs, security Network Traffic, Bandwidth Environmental Concerns: RF noise, Metal, Power Define radio, infrastructure, cohabitation, legacy compatibility Ensure RF testing has been completed and there are no issues with interference in production processes

These items will determine the needed CPU, memory, radio and security for your system


ROADMAP 2002 Conference Room Usage: Mica2 Initial Trials

JF3 Imote2 Trials

BP Imote2 Deployment

Future? Future?

JF3 Mica2 Trials CUB3 Mica2 vs. Imote1Trials

2004

2007

Decision Point

2008

W

or ld w

le rA Ro n

2009+

In te l

A T9

JF S1

2006

D 1D

Vi et na m

2 Ph as e B P

2005

id e

cr es

Future?

2 C UB 3

Ph as e

1 C UB 3

Ph as e

1 Ph as e

1

JF 3

Ph as e B P

2003

JFS1 Imote2 Deployment


Q&A


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