Predictive maintenance uses condition and operating data to estimate when equipment may require attention. It is different from preventive maintenance, which follows a calendar or usage schedule even if the equipment appears healthy.
Useful signals
Vibration, temperature, motor current, operating hours and fault history can reveal changes in machine condition. Select signals based on known failure modes rather than collecting every available sensor reading.
Baseline first
Record how a healthy machine behaves at different loads and speeds. An alarm threshold without operating context may trigger false warnings, especially when product mix or production rate changes.
Financial justification
Estimate the expected cost of avoided breakdowns, emergency repairs, lost output and quality losses. Subtract sensors, connectivity, analysis, training and maintenance response costs. A predictive system has little value if nobody acts on its warnings.
Start with critical assets
Choose one machine where unplanned failure is costly and where failure patterns can reasonably be monitored. Track false alarms, early detections, downtime avoided and maintenance actions over time.
Conclusion
Predictive maintenance is a reliability program supported by data, not a promise that machines will never fail.
To see how connected factory software can support your manufacturing workflows, request a Factovare demonstration.