solution.predictive_maintenance()
Predictive maintenance fails when it starts from algorithms rather than real failure modes and the operational capacity to act on insights.
Algorithms search for generic "anomalies" without mapping to specific failures: when they find something, nobody knows if it is a real symptom or noise.
A useful model grows out of conversations with maintenance: which failures are we hunting, with which signals, with which lead time.
The system raises alerts but the maintenance workflow is not integrated: the warning sits in a dashboard nobody opens between shifts.
Every alert must be closed with validation, ownership and feedback to the model. Without this loop, the system does not learn and nobody trusts it.
Vibration, temperature and current data exist but are not linked to actual maintenance events: the model cannot tell a failure from a normal reset.
Linking field data, CMMS and intervention records is the technical precondition for any reliable scoring.
Evaluation of available data: failure history, process data, installed sensors.
If needed, installation of additional sensors (vibration, temperature, current) and data collection.
ML model training on historical data, validation and alarm threshold configuration.
Connection to the maintenance system to automatically generate predictive work orders.
We avoid the “algorithm only” approach: a full chain is required to connect insight and execution.
Pipeline that acquires, normalizes, scores risk and updates asset status over time.
Alert, validation, assignment and closure rules to avoid insights that nobody acts upon.
Failure modes, scoring, intervention workflow — insights turn into closed actions.
