Predictive Maintenance Approach for Electric Power Systems Using Machine Learning

Electricity generation and distribution systems are classified as essential infrastructures where system reliability and operability depend on the state of their assets. Conventional maintenance policies such as corrective maintenance and time-based preventive maintenance can be inefficient and fail to detect latent failure symptoms, causing system failures, higher maintenance costs, shorter lifetimes of the equipment, and disruptions in system services. This work suggests a novel maintenance policy based on machine learning for predictive maintenance of electric power systems that will enable maintenance tasks to shift from a reactive approach to proactive decision making in order to avoid failures and plan maintenance tasks efficiently. The new system utilizes information about equipment operating conditions along with advanced features extraction and anomaly detection for accurate diagnosis of faults and health of the assets. The novelty of the new system is in integrating fault diagnosis and maintenance planning under a unified predictive maintenance framework.

Predictive Maintenance, Machine Learning, Electric Power Systems, Asset Condition Monitoring, Prognosis Analytics

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