March 13, 2026 
Condition monitoring plays a critical role in allowing engineers and operators to constantly monitor temperature, power consumption, and equipment health. With data from IDS-INDATA showing that UK and European manufacturers will lose more than £80bn due to downtime in 2025 alone, the need for early insight is clear. Additionally, as machine learning (ML) technology evolves, condition monitoring capabilities will further improve, explains Mark Richards, UK Sales Manager at Beckhoff UK.
Condition monitoring provides value by providing early, actionable insight into asset health. Maintenance teams can identify subtle changes in machine behavior and intervene before they develop into major failures. The benefits of early intervention include reduced downtime, minimized collateral damage, and improved overall equipment efficiency. Condition monitoring also supports better operational decision-making by increasing transparency into how machines perform under different loads, speeds, and production conditions.
However, there are some caveats. As production systems become more complex and flexible, traditional rules-based monitoring approaches can become difficult to keep up with. This is where machine learning is starting to reshape things. ML-based condition monitoring systems can learn what “normal” behavior looks like directly from historical data. These can take into account changes in operating statistics, process variability, and interactions of multiple sensors that are difficult to model. The result is earlier and more reliable fault detection, fewer false alarms, and the system can scale more effectively across machines and production lines.
Building a strong data foundation
When implementing machine learning within control and monitoring systems, operators and engineers must have a strong data foundation. ML algorithms are powerful, but their effectiveness depends on the quality and relevance of the data they receive. Selecting the right sensor, accurately positioning it, and ensuring a strong sampling rate are important first steps.
It is also important to consider operational conditions. Industrial machinery and equipment rarely operate in a single steady state. Variations in speed, load, product type, and duty cycle all affect asset operation. By capturing context information along with state data, ML models can learn how machines behave under different operating conditions. This allows the system to distinguish between regular process fluctuations and early signs of failure detection, reducing false alarms and increasing confidence in the insights provided.
From analysis to action
As data becomes available, the focus shifts to how insights are generated and used on the factory floor. ML-based condition monitoring is often most effective when introduced gradually. The initial model helps provide advisory alerts that highlight deviations from learned normal behavior rather than triggering automatic responses.
Deployment architecture is also important. Running analytics close to the machine improves response times, and higher-level systems can aggregate information across multiple assets for long-term analysis. An integrated automation environment can support both approaches. This means machine learning and condition monitoring capabilities can work at the same time.
Beckhoff’s approach integrates measurement, analysis, and control within an integrated platform. EtherCAT measurement terminals capture a wide range of condition signals, from vibration and temperature to current and pressure, and transmit them synchronously to the controller via high-speed fieldbus technology. Within TwinCAT, these raw signals are processed in parallel with traditional control tasks, allowing engineers to apply analysis using built-in libraries or standard open interfaces.
This systems integration approach has several advantages. Time-synchronized data capture improves reliability of analysis across multiple channels. Combining condition data with other operational signals improves overall fault detection accuracy and reduces false alarms as previously discussed. Additionally, because signal acquisition and analysis are performed within the same control framework, engineers can more easily relate insights gained from ML to process context and decision logic.
As machine learning becomes more prevalent in industrial environments, its role in condition monitoring will only grow, and so will its impact on maintenance strategies. The combination of high-quality data, operational context, and incremental deployment allows engineers to move beyond reactive maintenance to more predictive and resilient operations. When measurement, analysis, and control are tightly integrated, condition monitoring becomes more than just a diagnostic tool, but a practical decision-making asset.
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