Using machine learning to predict equipment failures and reduce unplanned downtime.
A manufacturing firm operating heavy machinery across multiple plants.
Frequent unplanned failures caused production losses and expensive emergency repairs. Sensor data was abundant but underutilized.
Ingested sensor telemetry and enriched it with contextual metadata for model training.
Built time-series models and anomaly detectors combined with domain rules for high precision.
Deployed models with monitoring, model drift detection, and integrated alerts into maintenance workflows.
