Predictive Maintenance (PdM)

Predictive maintenance is a maintenance strategy that uses condition monitoring data and analysis to estimate when equipment is likely to fail, so that maintenance can be scheduled shortly before failure rather than at fixed intervals or after a breakdown.

Predictive maintenance relies on the fact that many failures give warning signs as they develop, such as rising vibration, temperature, noise, current draw or wear debris in lubricant. Sensors or periodic measurements track these indicators, and analysis, ranging from alarm thresholds and trend extrapolation to statistical and machine learning models, estimates the equipment's condition and, where possible, its remaining useful life. Maintenance is then planned for a convenient time before functional failure.

PdM is widely applied to rotating machinery such as motors, pumps, fans, compressors, gearboxes and spindles, and to electrical equipment, hydraulic systems and production tooling. It aims to reduce unplanned downtime and secondary damage, avoid unnecessary replacement of healthy parts, and improve the planning of spare parts and labour. Common techniques include vibration analysis, oil analysis, infrared thermography, ultrasound and motor current signature analysis, increasingly combined with machine data from controllers through IIoT platforms.

Predictive maintenance is most worthwhile for critical assets whose failures are costly and whose failure modes have detectable precursors with enough warning time; for other assets, preventive or run-to-failure strategies may be more economical. Data-driven models need representative failure history, which is often scarce. Reliability-centred maintenance helps decide where PdM is appropriate, and ISO 17359 gives general guidelines for condition monitoring programmes. Any resulting work on machines is governed by the OEM documentation and qualified personnel.

Key points

Uses condition data to predict failure and schedule maintenance in time.
Relies on detectable warning signs such as vibration, heat or wear debris.
Common techniques include vibration, oil, thermography, ultrasound and current analysis.
Best suited to critical assets with detectable failure precursors.

Where AiVibe comes in

Predictive maintenance is listed among AiAmbA AI Factory use cases, with existing lines retrofitted with edge devices.

Explore AiVibe’s work in Quality, Reliability & Maintenance →

Predictive Maintenance (PdM)

Key points

Where AiVibe comes in

Related terms

Terms that refer to Predictive Maintenance (PdM)