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Oil & Gas
8 weeks
Energy Operations Company
PredictML
Industrial Predictive Maintenance
Running across the client's asset fleet. Expanding to additional facilities.
85%+Fault detection accuracy
34%Reduction in unplanned downtime
72hrAdvance warning window
$2M+Annual savings per asset
The Challenge
Predict critical equipment failures from multi-sensor time-series data across a fleet of industrial assets to prevent unplanned downtime costing $500K+/day.
Our Approach
Ensemble time-series classification with gradient-boosted models, focal loss for extreme class imbalance, and custom feature engineering on vibration, torque, and pressure signals.
Tech Stack
Gradient BoostingTime-Series ClassificationFocal LossFeature EngineeringStreaming Pipeline