Predictive maintenance of industrial energy equipment

Context

Extended operation of in-service equipment generates progressive wear of critical components, whose failure leads to costly downtime at the end operator and degradation of contractual availability. Maintenance performed at fixed intervals does not leverage the actual wear signal transmitted by telemetry: interventions are sometimes premature (waste of parts), sometimes too late (on-site failure). The goal of the solution is to anticipate relevant failures several usage cycles before they occur, to estimate a Remaining Useful Life accompanied by a calibrated confidence interval, and to monitor model drift in production in order to trigger retraining at the right time.

Solution

A complete predictive maintenance pipeline leveraging fleet telemetry was deployed. Raw signals are consolidated in a time-series store and enriched by automated usage-cycle detection and a per-equipment-type health index. A probabilistic forecasting model (Temporal Fusion Transformer via Darts) is trained per equipment family to predict the wear trajectory and estimate the remaining useful life. Confidence intervals are post-hoc calibrated via conformal prediction to give the business a reliable upper bound. The whole pipeline is industrialised around a FastAPI service, a Celery orchestrator for asynchronous training, an MLflow registry for run traceability and S3-compatible storage (MinIO) for model artefacts. A drift monitoring module (PSI, KS, interval coverage) watches model stability in production and triggers retraining when thresholds are crossed. A visualisation layer surfaces to maintenance teams the equipment at risk, its failure horizon and the associated uncertainty.

Value

• - Anticipation of failures several weeks before they occur, based solely on usage telemetry
• - Estimation of Remaining Useful Life with a calibrated confidence interval (conformal prediction)
• - Automatic detection of model drift in production to decide on retraining
• - Shift from corrective maintenance to planned and prioritized maintenance