AL and ML emerge as cornerstones of predictive maintenance in power industry, says GlobalData

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 synchronized 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 visualization layer, projecting the digital twin’s context-aware information such as asset status, diagnostics, and guided procedures directly onto the physical environment.”

GE Vernova is utilizing 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.

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 CO₂ charges.”

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.

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 organizations to scale PdM from pilots to fleet-wide programs.”