Predictive Maintenance for Grid Operations
An energy provider experienced costly unplanned outages due to reactive maintenance strategies and limited visibility into asset health.
Headline outcome
45% Reduction in unplanned downtime
The challenge
Maintenance was largely calendar-based, with critical assets failing between scheduled inspections and others being serviced unnecessarily.
“Deployed ML-based predictive maintenance models integrated with IoT sensor data and automated alerting workflows.”
Our approach
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01
Built a unified OT/IT data platform integrating SCADA and sensor feeds.
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02
Trained failure-prediction models per asset class with engineering domain experts.
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03
Embedded alerts into existing CMMS and operational workflows.
The result
The operator shifted to condition-based maintenance, dramatically reducing unplanned outages while extending the working life of high-value assets.
45%
Reduction in unplanned downtime
35%
Lower maintenance costs
2x
Improvement in asset lifecycle
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