Predictive Maintenance
Sensor-driven ML models that forecast equipment failures, optimize schedules, and cut unplanned downtime by up to 65%.
We ingest vibration, temperature, pressure, and current data from thousands of sensors, train gradient-boosted and deep-learning models on historical failure logs, and deploy them as real-time scoring services at the edge or in the cloud.
Key Features
01
Sensor Analysis
Multi-variate anomaly detection across vibration, thermal, acoustic, and electrical signals in real time.
02
Failure Prediction
Remaining-useful-life estimation using survival analysis and sequence models trained on historical logs.
03
Maintenance Scheduling
Constraint-aware scheduling that balances risk, spare-parts inventory, and production targets.
04
Cost Analysis
Total-cost-of-ownership modeling that quantifies the ROI of predictive vs. reactive maintenance.
Gas turbines and rotating machinery, wind-farm gearboxes, rail fleet monitoring, CNC machining centers, and HVAC systems in large facilities.