Machinery Prognosis Health Monitoring: Machine Learning
Machine learning–based Machinery Prognosis Health Monitoring is a condition monitoring approach that not only detects anomalies as they occur, but also predicts equipment degradation and potential failure before it affects operations. The method leverages historical and real-time operational data — vibration, temperature, pressure, current, flow rate, and other process parameters — collected through a data historian system as the foundation for model training.
Machine learning models are trained to recognise the normal behavioural patterns of each asset across its full range of operating conditions. Deviations from these patterns are quantified as asset health indicators, allowing early symptoms of failure to be identified well before conventional alarm thresholds are exceeded. The prognostic stage then estimates the rate of degradation and the Remaining Useful Life (RUL) of the equipment to support maintenance planning.
Core functional scope:
- Condition monitoring — continuous, centralised asset health surveillance
- Anomaly detection — early identification of deviations from the normal operating baseline
- Diagnostics — tracing the contributing parameters behind each anomaly
- Prognostics — estimating degradation trends and remaining useful life
- Decision support — risk-based prioritisation of maintenance actions
The key benefit is a shift from reactive and calendar-based maintenance toward predictive maintenance: reduced unplanned downtime, extended overhaul intervals, optimised spare parts and labour costs, and improved overall operational reliability and safety.