Predictive Maintenance (PdM)
Predictive maintenance uses sensor data and analytics to predict when equipment will fail, so maintenance happens just before failure — not on a fixed schedule and not after a breakdown. It cuts unplanned downtime and avoids unnecessary scheduled work.
Predictive maintenance (PdM) monitors the actual condition of an asset — through vibration, temperature, oil, ultrasound or process data — and uses analytics or machine learning to forecast failures before they happen. It sits between reactive maintenance (fix after failure) and preventive maintenance (fix on a calendar). The payoff is fewer surprise breakdowns and less wasted maintenance labour, focused on critical and high-cost assets such as pumps, motors, fans and compressors.
Related terms
Condition Monitoring · Remaining Useful Life (RUL) · Vibration Analysis · CMMS
Related guides
Software
Augury
Machine health monitoring for rotating equipment using vibration and AI.
Siemens Senseye Predictive Maintenance
Scalable predictive maintenance that learns from existing condition data.
AVEVA Predictive Analytics
Early-warning analytics for critical process and power assets.