ANTICIPATING MAINTENANCE PROBLEMS WITH PREDICTIVE ANALYTICS
Joe Reckamp, Analytics Engineering Group Manager, Seeq Corporation
By combining retrospective data analysis with predictive tools in advanced analytics platforms, process manufacturers can predict failures, enhance productivity, and establish optimal maintenance schedules.
PROCESS TECHNOLOGY APR/MAY 2025
P 24
rocess engineering teams have in the past relied on stored data to improve operational efficiency, through preventive maintenance, failure mitigation strategies and process optimisation. These efforts emphasised monitoring and were built around available operational data — often recorded manually — to identify problems. As time passed, these explorations progressively began using data stored in process historians and other databases to refine insights, leveraging diagnostic analytics to investigate issues and anomalies. However, these procedures are reactive in nature, and the aim today is to move more
towards proactive approaches that leverage historical data and context to drive more accurate process improvement decisions. Modern advanced analytics platforms are empowering manufacturers to implement proactive protocols that anticipate and address potential issues before they become failures, providing improved performance, enhanced operational efficiency, and increased uptime. Today, advanced analytics can guide organisations through problem-solving journeys by examining historical, current and predicted time series data, which unveils insights that drive improved decision-making.
TYPES OF ANALYTICS The word ‘analytics’ is typically associated with IT software products, platforms and the cloud. We therefore need to qualify what we mean when we use the term. For example, advanced analytics describes the use of statistics, machine learning and artificial intelligence in data analysis to glean insights. Other modifiers can be used to differentiate the analytics type based on utility and complexity. ‘Diagnostic analytics’, for example, describes the use of information from past events to respond to a given situation, investigating a raw set of historical data and applying statistical analysis to identify patterns and produce insights. In this case however, there is an unavoidable lag between the event or issue under analysis and the action taken to improve future performance, eliminating the ability to predict events before the fact. PROCESSONLINE.COM.AU