How to Avoid Downtime with Predictive Maintenance

Learning objectives Differentiate between traditional time-based (or reactive) maintenance and predictive maintenance strategies in the process industries, identifying the specific operational, financial and safety benefits of shifting to a data-driven and condition-based approach. Understand the three primary ways that modern advanced analytics and AI platforms enable predictive strategies. Recognize the role of the SME in applying AI to real-world industrial use cases, as well as why human-in-the-loop considerations are critical for sustainable AI-based predictive maintenance that scales asset health indicators and reduces unplanned downtime. Predictive maintenance insights Process manufacturers can strengthen uptime, safety and performance by breaking down data silos and using advanced analytics and AI to turn scattered operational information into actionable predictive maintenance insights. Predictive maintenance helps plants shift from calendar-based repairs to condition-based interventions, enabling earlier detection of degradation, fewer unplanned shutdowns and measurable gains in reliability, efficiency and cost control. In 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. 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 significant portions of the data preparation automatically and enabling engineers to focus on interpreting insights and driving action. As a result, these subject matter experts (SMEs) can now quickly begin analyzing new datasets and they are scaling insights across entire enterprises using AI. Processors are leveraging this shift to strengthen predictive maintenance, which is accelerating the transition from understanding past events to anticipating future risks and determining optimal responses. AI’s growing role in the process industries Meanwhile, AI is increasingly optimizing process conditions, improving production quality and enhancing asset reliability. Its most relevant applications related to predictive maintenance are machine learning (ML) and generative AI and these technologies learn patterns from both historical and real time data. The most successful and only sustainable models, however, also consider the SME who understands the process details and imparts their wisdom to elevate the analysis, where AI amplifies the human in the loop. It then uses these patterns to forecast future operational behavior, detecting subtle signs of degradation that would be difficult or even impossible to manually spot. When it comes to leveraging AI, the process industries have taken a cautious approach overall, recognizing that safety, product quality and regulatory compliance cannot be compromised. AI must be deployed on a solid foundation of robust processes, clear governance and high-quality data and most manufacturers are focusing on narrow, high-value use cases where models are well bounded, behavior is explainable and benefits are measurable. The shift to predictive maintenance At its core, predictive maintenance is a shift from servicing equipment based on a calendar date — or because something has already failed — to servicing it based on its need, according to the data. However, this data spans far beyond a handful of present sensor readings on equipment like a pump or compressor, also encompassing high-resolution time series information from process historians, condition monitoring systems, lab and quality information and more. It may also live buried in maintenance reports, as well as in minor trips or issues. When these sources are contextualized and analyzed together, it becomes possible to estimate both the likelihood and timing of failure and then schedule interventions when they provide genuine value to throughput, quality and safety — not simply because a preset interval has elapsed. Predictive maintenance sits at the intersection of asset reliability, process performance and margin protection and it is a key component of operational excellence that plants strive for. Furthermore, the same patterns that signal an impending equipment issue are often the patterns that quietly erode yield, increase rework and push a plant off its emissions and sustainability targets. For example, a pump operating off its curve, a fouled heat exchanger or a control valve that is sticking does not merely threaten the maintenance budget; it threatens the stability and economics of the entire unit. These production threats create the risk for off-spec products and other negative outcomes. In continuous and batch operations alike, a single unplanned shutdown can translate into millions of dollars in lost production, off spec material and emergency work. Because plants are designed to run for long stretches and typically shut down at most only a few weeks each year for major turnarounds, there is limited room to absorb additional unplanned downtime. Time-based maintenance is expensive and conservative by design, yet it still leaves producers exposed to unexpected failures and it consumes resources that could be redirected to higher-value reliability improvements. The diversity of assets inside a single complex further complicates matters. For example, this can include pumps, compressors, control valves, actuators, heat exchangers, furnaces, reactors, distillation columns, filters, membranes and supporting utilities. Each plant has its own unique failure modes, which were historically managed via original equipment manufacturer (OEM) guidance and local experience through periodic route-based inspections, but these are difficult to standardize and scale. Fortunately, there are much more reliable ways. Advanced analytics platforms provide for predictive maintenance For many processors, data fragmentation is the primary obstacle to predictive maintenance, with critical signals spread across disparate systems. Without a unified, contextual data model, engineers must spend significant time manually consolidating information to answer even basic operational questions. Modern analytics and AI platforms address this challenge by integrating and aligning data from multiple sources, automatically identifying anomalies and inconsistencies. They present insights through intuitive visualizations, including trends, heat maps and scatter plots, making emerging risks easier to detect (see Figure 2). As more labeled data becomes available, ML models continuously improve to deliver increasingly accurate failure predictions and more effective maintenance recommendations. Figure 2: Users create a contextualized plant story in Seeq, an advanced analytics and AI platform. Courtesy: Seeq Corp. These platforms are built f