Goal:
Use machine learning on vibration, sensor, and SCADA data to predict and prevent failures across rotating machinery — compressors, pumps, and turbines.
Impact Keywords:
- Predictive Maintenance
- Operational Reliability
- Downtime Reduction
- OPEX Optimization
Approach:
LunarTech Lab developed a predictive analytics pipeline integrated directly with existing CMMS systems.
- Data Unification:
- Streamed sensor data (vibration, temperature, current draw) into a centralized data lake with automated cleaning and labeling.
- Health-Score Modeling:
- Built unsupervised anomaly-detection models and RUL (Remaining Useful Life) estimators to predict impending failures.
- Maintenance Optimization:
- Generated risk-based maintenance schedules and spare-part demand forecasts.
- Real-Time Dashboards:
- Delivered site and fleet-level reliability dashboards with anomaly alerts via email/SMS.
- CMMS Integration:
- Linked outputs to systems like SAP PM and Maximo for automatic work-order creation.
Summary:
With AI-based early warnings, the company reduced unplanned downtime by 40%, saving over $10 M annually in lost production and emergency repairs.
