- The shift towards data-driven maintenance reflects a broader digital transformation across the sector.
Artificial intelligence (AI) and machine learning (ML) are rapidly becoming the backbone of predictive maintenance (PdM) across the power industry.
These technologies empower utilities to learn the normal operating behavior of critical grid assets and detect early indicators of deterioration well before failures occur. By continuously analysing operational and sensor data, AI/ML systems surface anomalies and emerging failure modes while intervention is still timely and cost-effective.
As digitalisation expands across generation, transmission, and distribution networks, AI/ML-driven PdM is emerging as a critical capability for safer, more efficient, and more resilient power operations, according to GlobalData, a leading data, analytics, and intelligence platform.
GlobalData’s latest report highlights how leading power companies are putting these capabilities into practice. Utilities such as Ørsted, Florida Power & Light, and National Grid are enhancing PdM with AI/ML by combining high-frequency sensor data, inspection imagery, and operational history.
This integrated approach lets them detect anomalies early, predict failure probability with greater confidence, and optimise maintenance planning and outage scheduling. Beyond individual asset health, PdM is also helping utilities and grid operators maintain stability amid renewable variability and shifting power flows.
The shift toward data-driven maintenance reflects a broader digital transformation across the sector. Rather than relying on fixed, calendar-based service intervals, operators are increasingly able to act on the specific condition of each asset. This precision not only extends equipment life but also reduces unnecessary interventions, concentrating resources where they deliver the greatest reliability benefit.
A critical reliability metric
Rehaan Shiledar, Power Analyst at GlobalData, identifies energy tracking as one of the most significant emerging developments in the field. “Energy tracking is emerging as a critical reliability metric in PdM,” he explains. “This helps to spot performance decline long before equipment trips or fails.”
The value of this approach lies in its economic translation of technical signals. By converting condition data into expected energy loss under forecast demand, weather, and dispatch, energy tracking sharpens maintenance prioritisation around risk-to-deliver and real economic impact.
This is particularly powerful where revenues and downtime costs vary by market conditions and time, allowing utilities to focus scarce maintenance budgets where losses would be most acute.
Energy tracking also strengthens the underlying PdM models themselves. Comparing expected versus actual output enables operators to detect incipient issues and validate the effectiveness of completed maintenance work.
Reflecting this shift, utilities are increasingly embedding advanced metering infrastructure (AMI) and grid-sensing data into their reliability and asset health programs. Companies such as Duke Energy and Southern California Edison, for instance, are using load and voltage tracking to manage transformer and feeder stress, target timely replacements, and proactively improve overall system performance.
Digital twins and AR working in tandem
Another key trend shaping the future of PdM is the convergence of digital twin technology and augmented reality (AR). “Digital twin technology and augmented reality are increasingly being deployed in tandem, forming a powerful, complementary combination that brings real-time intelligence,” Shiledar notes.
A digital twin delivers a continuously synchronised virtual representation of a physical asset, enriched by live data streams and 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.
Together, they bridge the gap between rich data models and hands-on field work.
This pairing is already seeing real-world deployment. GE Vernova is utilizing digital twins for large-scale power generation equipment, including turbines and boilers, combined with wearable AR and 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, enabling actionable insights and more informed decision-making at every stage of an asset’s life.
Carbon pricing as an economic driver
Shiledar also points to carbon pricing as an increasingly important economic driver of PdM adoption. “Carbon pricing is emerging as an economic driver to PdM adoption in the power sector by making inefficiency and unreliability explicitly costlier,” he says.
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 and incur higher auxiliary loads. Under carbon pricing regimes, these losses translate directly into recurring COâ‚‚ charges, giving operators a tangible financial incentive to address degradation early.
Carbon pricing also raises the value of avoiding forced outages, trips, and restarts, which are emissions-intensive and can trigger higher-emitting backup generation. Through PdM and digital asset management, utilities can detect deterioration early using sensor and performance analytics, reducing the likelihood of these costly events.
Reflecting this shift, utilities and power generators can credibly position digital monitoring and analytics as both asset-performance and emissions-adjacent capabilities, aligning reliability goals with broader climate and sustainability commitments.
Looking ahead, Shiledar sees sustained momentum behind PdM across the industry. “Utilities and power generators are accelerating PdM to improve reliability on aging assets while controlling operation and maintenance (O&M) costs,” he concludes.
Several factors are making this expansion practical and scalable. Easier-to-deploy technologies such as IIoT sensors, edge computing, and advanced analytics are making condition monitoring more accessible than ever before.
Rapid renewable growth is strengthening the business 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 return on investment—are pushing organisations to scale PdM from isolated pilots to comprehensive, fleet-wide programs.
