{"id":33344,"date":"2026-08-12T10:37:00","date_gmt":"2026-08-12T09:37:00","guid":{"rendered":"https:\/\/www.engineernewsnetwork.com\/blog\/?p=33344"},"modified":"2026-08-12T10:37:01","modified_gmt":"2026-08-12T09:37:01","slug":"artificial-intelligence-and-machine-learning-emerge-as-cornerstones-of-predictive-maintenance-in-power-industry","status":"publish","type":"post","link":"https:\/\/www.engineernewsnetwork.com\/blog\/artificial-intelligence-and-machine-learning-emerge-as-cornerstones-of-predictive-maintenance-in-power-industry\/","title":{"rendered":"Artificial intelligence and machine learning emerge as cornerstones of predictive maintenance in power industry"},"content":{"rendered":"\n

Artificial intelligence (AI) and machine learning (ML) are becoming foundational to predictive maintenance (PdM) across the power industry, enabling utilities to learn the normal operating behavior of critical grid assets and identify early indicators of deterioration. By continuously analysing operational and sensor data, these technologies surface anomalies and emerging failure modes before they escalate, supporting timely and targeted interventions. As digitalisation expands across generation, transmission and distribution networks, AI\/ ML-driven PdM is becoming critical capability for safer, efficient and more resilient power operations, says\u00a0GlobalData<\/strong><\/a>.<\/p>\n\n\n\n

GlobalData’s latest report, Strategic Intelligence: Predictive Maintenance in Power (2026)<\/em><\/strong><\/a> shows that power companies such as \u00d8rsted, Florida Power & Light, and National Grid are enhancing PdM with AI\/ML by combining high-frequency sensor data, inspection imagery, and operational history to detect anomalies early, predict failure probability, and optimise maintenance planning and outage scheduling. PdM is also helping utilities and grid operators maintain stability amid renewable variability and shifting power flows.<\/p>\n\n\n\n

Rehaan Shiledar,\u00a0Power<\/strong><\/a>\u00a0Analyst at GlobalData, comments: “Energy tracking is emerging as a critical reliability metric in PdM.\u00a0\u00a0This helps to spot performance decline long before equipment trips or fails. By translating technical condition signals into expected energy loss under forecast demand, weather, and dispatch, it sharpens maintenance prioritisation around risk-to-deliver and real economic impact, particularly where revenues and downtime costs vary by market conditions and time.”<\/p>\n\n\n\n

Energy tracking also strengthens PdM models by comparing expected versus actual output to detect incipient issues and validate maintenance effectiveness. Reflecting this shift, utilities are increasingly embedding advanced metering infrastructure (AMI) and grid-sensing data into reliability and asset health programs. Power companies such as Duke Energy and Southern California Edison are using load and voltage tracking to manage transformer and feeder stress, target replacements, and proactively improve system performance.<\/p>\n\n\n\n

Shiledar continues: “Digital twin technology and augmented reality (AR) are increasingly being deployed in tandem, forming a powerful, complementary combination that brings real-time intelligence. A digital twin delivers a continuously synchronised virtual representation of a physical object, enriched by live data streams often rendered as a high-fidelity 3D model. AR, by contrast, serves as the intuitive visualisation layer, projecting the digital twin’s context-aware information such as asset status, diagnostics, and guided procedures directly onto the physical environment.”<\/p>\n\n\n\n

GE Vernova is utilising digital twins for large-scale power generation equipment (turbines and boilers) combined with wearable AR\/immersive headset guidance for field technicians. Likewise, Siemens combines a digital twin framework with AR to merge the physical and virtual worlds across the value chain, thereby developing actionable insights and informed decisions.<\/p>\n\n\n\n

Shiledar adds: “Carbon pricing is emerging as an economic driver to PdM adoption in the power sector by making inefficiency and unreliability explicitly costlier. As equipment degrades through fouling, seal leakage, blade wear, control drift, insulation aging, or rising transformer losses, power plants often consume more fuel per megawatt-hour. This incurs higher auxiliary loads; under carbon pricing, these losses translate directly into recurring CO\u2082 charges.”<\/p>\n\n\n\n

PdM and digital asset management help utilities detect deterioration early using sensor and performance analytics. Carbon pricing also raises the value of avoiding forced outages, trips, and restarts that are emissions-intensive and can trigger higher-emitting backup generation. Reflecting this shift, utilities and power generators can credibly position digital monitoring and analytics as asset-performance and emissions-adjacent capabilities.<\/p>\n\n\n\n

Shiledar concludes: “Utilities and power generators are accelerating PdM to improve reliability on aging assets while controlling operation & maintenance (O&M) costs. Easier-to-deploy technologies, such as IIoT sensors, edge computing, and analytics are making condition monitoring more practical and scalable. Rapid renewable growth is strengthening the case further, as distributed wind and solar fleets make downtime costly and remote monitoring essential. At the same time, safety, regulatory, and ESG expectations combined with improved cybersecurity and proven ROI, will push organisations to scale PdM from pilots to fleet-wide programs.”<\/p>\n","protected":false},"excerpt":{"rendered":"

Artificial intelligence (AI) and machine learning (ML) are becoming foundational to predictive maintenance (PdM) across the power industry, enabling utilities to learn the normal operating behavior of critical grid assets and identify early indicators of deterioration. 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