ENGINEERING SOLUTIONS PREDICTIVE AND PREVENTIVE MAINTENANCE
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Janelle Armstead-English, Seeq Corp., Chicago
How to avoid downtime with predictive maintenance
By strategically deploying advanced analytics and AI to existing systems, users can increase plant reliability, operational efficiency and production uptime via predictive maintenance.
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n the process industries, there is often a thin line between reliable production and costly disruption — and many plants are required to run nearly continuously with limited windows to take equipment offline for maintenance. As asset fleets age, product portfolios expand and regulatory expectations around safety, emissions and reporting increase, manufacturers face several challenges in the pursuit of maximizing uptime. With this backdrop in mind, one of the key differences between a plant that runs reliably and one that struggles with unplanned downtime is how effectively it uses its available data. That information can exist in multiple places and if it is siloed or buried in spreadsheets, it can be difficult to know where to start.
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When the data is available for proper analysis, it unlocks the ability to apply a predictive maintenance strategy that empowers plant personnel to address potential issues before they become outright failures and sources of downtime.
Conventional data wrangling challenges As analytical technologies have matured, engineers in the process industries are approaching data challenges differently than in the past. Previously, 80% or more of analytics software time was spent on manual tasks within spreadsheets, such as collecting, cleansing and preparing data, which left minimal time for meaningful analysis (see Figure 1). Automated analytics platforms and artificial intelligence (AI) are reversing that ratio, handling Plant engineering — www.plantengineering.com
7/28/26 9:58 AM