Predictive Maintenance (PdM) uses sensor telemetry, vibration signatures, thermal profiles, and operating context to forecast when a piece of equipment will fail or drift out of spec. The goal is to replace calendar-based preventive maintenance (which over-services healthy assets) and reactive maintenance (which absorbs downtime cost) with interventions scheduled a few days or weeks before failure.
Common ML approaches: regression on remaining-useful-life (RUL) targets from run-to-failure histories; anomaly detection on multivariate sensor streams when failure data is sparse; classification models that predict probability of failure in the next N operating hours. Survival models (Cox, Weibull) are still widely used because they handle censored data well.
Production reality is usually the bottleneck, not model accuracy. PdM programs stall on inconsistent OT data (mixed OPC-UA, legacy SCADA, spreadsheet logs), unclear ground truth for failure events, and no standing review process with plant-floor engineers. Mature deployments pair the model with a CMMS workflow, a per-asset confidence threshold, and a feedback loop where maintainers label false positives.
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