Predictive maintenance often fails because the organization has data, but not the data needed to answer the maintenance question.
Start with failure modes
Define what failure is being predicted and what signals change before it.
Data quality beats model complexity
Timestamp alignment, operating state, calibration, missing values and maintenance history usually matter more than a sophisticated algorithm.
History is the missing dataset
A failure timestamp is rarely enough; component, symptom, intervention and root-cause context improve labels.
Measure the outcome
Track lead time, precision, avoided failures and technician workload. Prediction after the event is not predictive maintenance.
Final takeaway
The engineering value comes from a measurable improvement, explicit boundaries and a design that remains understandable when the system is under pressure.