FEATURE Machine learning


FROM DIAGNOSTICS TO DECISION-MAKING FROM DIAGNOSTICS TO DECISION-MAKING Automated


Condition monitoring is crucial to asset health in industrial facilities. Mark Richards, UK sales manager for  learning (ML) technologies evolve, condition monitoring capabilities will increase and improve


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n today’s industrial facilities, condition monitoring plays a critical role in helping engineers and operators keep a constant eye on temperature, power consumption and equipment health. With UK and European manufacturers losing over £80 billion to downtime in 2025 alone, according to data from IDS-INDATA, the need for early insights is clear.


Condition monitoring delivers value by providing


early, actionable insight into asset health. Having  maintenance teams can intervene before these  intervention include reduced downtime, minimal secondary damage and improved overall equipment  better operational decision-making by increasing transparency in how machines behave under  However, there’s a caveat. As production systems  rule-based monitoring approaches can struggle to keep pace. This is where machine learning is starting to reshape things. ML-based condition monitoring systems can learn what “normal” operation looks like directly from historical data. They can account for changing operating stats, process variability and  The result is earlier and more reliable fault detection, fewer false alarms and systems that scale more  When implementing ML within control monitoring systems, engineers must have a strong data foundation. While ML algorithms are  and relevance of the data they receive. Selecting appropriate sensors, positioning them accurately and ensuring strong sampling rates are essential.


automationmagazine.co.uk


It is also important to factor in the operational context. Industrial machinery and equipment rarely operate in a single steady state – variations in speed, load, product  capturing contextual information alongside condition data, ML models can learn how a machine behaves  systems to distinguish between regular process variation and early indicators of fault detection, helping  insights provided.


Once data is available, the focus shifts to how insights


  when deployed incrementally. Initial models can help provide advisory alerts, highlighting deviations from learned normal behaviour rather than triggering automated responses. The deployment architecture is also important. Performing analysis close to the machine enables faster response times, while higher-level systems can aggregate information across multiple assets for long- term analysis. Integrated automation environments can support both approaches, meaning machine learning and condition monitoring can function simultaneously.   EtherCAT measurement terminals capture a wide range of condition signals – from vibration and temperature to current and pressure – and transmit them  technology. Within TwinCAT, these raw signals are processed alongside traditional control tasks, allowing engineers to apply analytics using built-in libraries or standard open interfaces. 


approach. Time-synchronised data capture improves analysis reliability across multiple channels. Combining condition data with other operational signals improves overall fault detection accuracy, and as mentioned earlier, reduces false alarms. Furthermore, because signal acquisition and analytics are live within the same control framework, engineers can more easily correlate ML-derived insights with process context and decision logic.


 can move beyond reactive maintenance toward more predictive and resilient operations. When measurement, analytics and control are tightly integrated, condition monitoring becomes not just a diagnostic tool, but a practical decision-making asset.


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March 2026 17


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