Prognostics and Health Management
Prognostics and Health Management combines condition assessment, diagnostics, prediction, and decision support to manage equipment health and future failure risk.
What this term means in maintenance
Prognostics and Health Management combines condition assessment, diagnostics, prediction, and decision support to manage equipment health and future failure risk.
PHM capabilities
PHM may include:
- Health monitoring
- Fault detection
- Diagnosis
- Degradation trending
- Remaining-life estimation
- Risk assessment
- Maintenance recommendations
Practical example
Motor current, vibration, and temperature are combined to assess condition and estimate when planned intervention may be required.
Data requirements
Useful PHM needs trustworthy sensors, asset context, operating conditions, failure history, and verified outcomes.
Decision support
Predictions should support planning, parts, shutdowns, and risk decisions.
Common mistake
A prediction without uncertainty, explanation, and maintenance workflow is difficult to trust or use.
Related concepts
Related maintenance terms
Keep exploring connected CMMS, reliability, and maintenance planning terms.
Fault Detection and Diagnostics
Fault Detection and Diagnostics is the process of identifying abnormal equipment behavior and determining the likely fault or affected component.
Remaining Useful Life
Remaining Useful Life is an estimate of how long an asset or component can continue performing acceptably before repair, replacement, or failure.
Asset Health Index
An Asset Health Index is a combined score or classification representing the condition, performance, reliability, and risk of an asset.
Prognostics and Health Management FAQs
- What does PHM stand for?
PHM stands for Prognostics and Health Management.
- What does PHM combine?
Health monitoring, fault diagnosis, degradation trending, remaining-life prediction, and decision support.
- Why should predictions show uncertainty?
Remaining life and failure timing are estimates affected by load, condition, data, and model limits.