Enhancing condition monitoring with machine learning

IN 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 identified subtle changes in machine behaviour, maintenance teams can intervene before these develop into damaging failures. The benefits of early intervention include reduced downtime, minimal secondary damage and improved overall equipment efficiency. Condition monitoring also supports better operational decision-making by increasing transparency in how machines behave under different loads, speeds and production conditions. 

However, there’s a caveat. As production systems become more complex and flexible, traditional 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 multi-sensor interactions that are difficult to model. The result is earlier and more reliable fault detection, fewer false alarms and systems that scale more effectively across machines and production lines.

Building a strong data foundation

When implementing machine learning within control monitoring systems, operators and engineers must have a strong data foundation. While ML algorithms are powerful, their effectiveness depends on the quality and relevance of the data they receive. Selecting appropriate sensors, positioning them accurately and ensuring strong sampling rates are essential first steps. 

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 type and duty cycle all influence asset behaviour. By capturing contextual information alongside condition data, ML models can learn how a machine behaves under different operating conditions. This enables systems to distinguish between regular process variation and early indicators of fault detection, helping reduce false alarms and improve confidence in the insights provided.

From analysis to action

Once data is available, the focus shifts to how insights are generated and used on the factory floor. In many cases, ML-based condition monitoring is most effective 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 functions can function simultaneously. 

Beckhoff’s approach brings measurement, analysis and control together within a unified 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 alongside traditional control tasks, allowing engineers to apply analytics using built-in libraries or standard open interfaces.

There are several benefits of this system-integrated 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.

As machine learning becomes more widespread in industrial settings, its role in condition monitoring will only increase along with its influence over maintenance strategies. By combining high-quality data, operational context and incremental deployment, engineers 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.

Mark Richards is UK sales manager at Beckhoff UK

www.beckhoff.com