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In many industrial facilities, generator condition monitoring can be constrained by installation complexity and limited measurement coverage, making early fault detection challenging.
The e-PCM addresses this gap with a sensorless approach.
By continuously analysing voltage and current signals, it monitors the generator’s electrical behaviour to detect both electrical and mechanical issues, without the need for additional sensors. Utilizing a self-learning model, the e-PCM system establishes normal operating conditions and continuously compares real-time data against this reference to identify deviations at an early stage.
The result is a non-intrusive, intelligent condition monitoring solution that enables predictive maintenance, reduces unplanned downtime, and supports confident maintenance decision-making.
The e-PCM is developed by Artesis Technology System.
The e-PCM system applies a model-based monitoring approach by creating a mathematical representation of the generator’s normal operating behaviour. During commissioning, the system analyses electrical signals across different load and operating conditions to establish this baseline model.
Real-time electrical data is continuously compared against the established model. Rather than relying on fixed thresholds, the e-PCM identifies deviations in machine behaviour as faults develop. These changes, reflected in the relationship between electrical input and mechanical output, are captured as variations in the model, enabling early and reliable fault detection even under varying operating conditions.
The e-PCM then processes these deviations to determine fault characteristics and severity, providing structured diagnostic insight to support maintenance planning and intervention.
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