Predictive maintenance is often introduced with a model that forecasts failure. The harder first step is building a reliable record of operating conditions, maintenance work and what “failure” means for a specific asset.
A machine fails twice in the history, both after major maintenance changes. That may be too little consistent evidence for a failure-prediction model. A condition-monitoring rule and better work-order records can be a more responsible first step.
Reconstruct the history
Link sensor data with work orders, component changes, operating loads and downtime reasons. Separate planned replacement from a true failure. Note missing periods and sensors added halfway through the history.
Maintenance data often contains ambiguous work orders such as “checked machine” or “issue resolved.” Improving those descriptions can be as valuable as adding sensors. Ask technicians what observations would have helped them diagnose a past failure. The resulting data design should fit their work rather than make them spend more time filling forms for a distant model.
Choose an actionable horizon
Maintenance may need days to schedule a shutdown or only hours to check a bearing. Define the lead time and intervention cost before selecting a model. A simple condition threshold may be a better first baseline.
Test in the workflow
Measure useful warnings, false alarms and missed events under real production conditions. Give technicians the evidence and a way to record their diagnosis. A prediction is valuable when it enables a safe, timely maintenance choice.
