Predictive Maintenance
Predictive maintenance uses sensor data, condition monitoring, and AI/ML models to predict when equipment is likely to fail — enabling maintenance to be scheduled based on actual asset condition rather than fixed intervals (preventive maintenance) or failure events (corrective maintenance). Common sensor types include vibration, temperature, acoustic emission, current, and pressure. Predictive maintenance is the primary value proposition of AI-native APM platforms like Tractian and TwinThread.
Why it matters
Unplanned downtime in industrial operations costs an estimated $50B annually in the US alone. Predictive maintenance shifts maintenance from reactive (fix it when it breaks) and periodic (fix it on a schedule) to condition-based (fix it when the data says it is approaching failure). The payback period is typically 6–18 months for rotating equipment programs with good sensor data.
Related concepts
External References
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Cite this definition
Finocchiaro, Michael. “Predictive Maintenance.” DemystifyingPLM PLM Glossary, 2026, https://www.demystifyingplm.com/glossary/predictive-maintenance