Summary
Applied machine learning to monitor and predict failures across critical grid assets. Replaced time-based maintenance with condition-based, proactive interventions. Integrated sensor, inspection, and operational data into a unified asset health view. Reduced unplanned downtime, lowered maintenance costs, and optimized capital planning.