Predictive Maintenance (PdM)
The use of condition data and models to forecast equipment failures before they occur, so work can be planned instead of suffered.
Predictive maintenance extends condition-based maintenance with forecasting. Where CBM reacts when a measurement crosses a limit, PdM analyzes trends and patterns — vibration spectra, temperature trajectories, energy signatures, process data — to estimate that a failure is developing and roughly when it will land. The output is lead time: enough warning to order parts, schedule the outage, and intervene on a planned Tuesday rather than an unplanned Sunday.
Techniques range from straightforward trend extrapolation and statistical baselines to machine-learning models trained on failure history. The sophisticated models get the attention; in practice, most of the value in early deployments comes from reliable sensing, sane baselines, and a closed loop from alert to work order. A modest model wired into workflow beats an excellent model attached to a dashboard.
PdM only earns money against detectable failure modes — degradation that announces itself in measurable signals. Sudden-death failures remain outside its reach, which is why honest programs size their business case on the detectable share of historical failures, not on total downtime.
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