Diagnostics, prognostics, and predictive maintenance — a clean distinction
Three terms that constantly collide in scoping documents — and a simple decision framework for when each one actually applies.
Çeviri hazırlanıyor — şu anda İngilizce orijinal metni okuyorsunuz.
Why the terms collide
When a vendor says 'predictive maintenance' and the buyer hears 'diagnostics,' pilots fail in scoping. The NASA definitions from the reliability-engineering literature are useful because they separate the terms along the information flow: observation → explanation → forecast → action.
Condition monitoring — what is happening now?
Sensors, SCADA tags, and manual inspections supply the current state: vibration, temperature, current signature, pressure. No interpretation yet. This is the data substrate and is mostly a solved problem once connectors are in place.
Diagnostics — what happened and why?
Diagnostics classifies anomalies into concrete fault types: bearing wear, imbalance, cavitation, phase asymmetry. It requires domain knowledge, fault models (FMEA), and labelled data. Good diagnostics cut alarm overload because they separate 'meaningful' from 'noise.'
Prognostics — what will happen?
Prognostics estimates remaining useful life (RUL) or failure probability within a time window. It needs stable asset classes, sufficient degradation history, and calibrated confidence intervals. A forecast without confidence is useless — maintenance teams need a trust score, or the worse hypothesis costs more than the status quo.
Predictive maintenance — what do we do?
Predictive maintenance is the operational practice that turns diagnostics and prognostics into work orders. It joins health index, criticality, spares availability, and SLA impact. Real ROI lives here — and this is where pure 'AI platforms' fail when they ignore workflow.
A decision framework for your first use case
Ask three questions: (1) Are asset classes repeatable enough for models? (2) Is maintenance reactive today, or already condition-based? (3) Who makes the call inside the workflow? Start with diagnostics when alarm overload is the pain; with prognostics when unplanned outages dominate downtime cost; with predictive maintenance when both predecessor stages are technically and organisationally in place.
Common confusions that kill pilots
Anomaly detection is not diagnostics — it says 'unusual,' not 'outer-race bearing wear.' RUL on assets with two years of data is speculation, not prognosis. 'AI-powered' platform language without FMEA, without CMMS integration, and without engineering review does not produce an operational result. Settling these distinctions up front materially raises the odds of a successful pilot.