Predictive maintenance schedules service when equipment shows signs of degradation, rather than replacing parts at fixed intervals or waiting for failure. A predictive digital twin can use current readings and historical patterns to estimate when a component may need attention. section 1.3.3
A practical path from equipment data to a maintenance decision is:
- Capture physical readings with IoT sensors and use cloud systems to process and store them. section 1.2
- Look for anomalies or forecast a limit being crossed; a rising bearing temperature might indicate that a safe limit will be exceeded within 48 hours. section 1.3.3 section 1.4.2
- Plan inspection, repair capacity, and spare parts before failure. Forecasts depend on historical patterns remaining relevant. section 1.3.3 section 2.1.1
A pump maintenance decision
A plant loses 12 hours a month to centrifugal pump bearing failures at $15,000 per hour. Six-month replacement intervals discard 40% of bearings while they are still sound, yet 15% fail before replacement. Servicing bearings based on their actual health could reduce avoidable replacement, support earlier spare-parts orders, and limit unplanned downtime. section 2.1.1
The same scheduling problem applies at fleet scale: digital representations of individual jet engines can predict which engines need maintenance and when, so repair capacity matches demand. The goal is an actionable maintenance plan, not merely a failure forecast. section 1.4.2
