Predictive Maintenance in Power Generation Market Size & Share 2026-2035

Predictive Maintenance in Power Generation Market Size

The global predictive maintenance in power generation market was valued at USD 2 billion in 2025, reflecting intensifying investment in AI-driven diagnostics and the mounting operational cost burden of unplanned generation asset failures across thermal, renewable, and nuclear portfolios. The market is projected to reach USD 5.6 billion by 2035, expanding at a compound annual growth rate (CAGR) of 10.8% over the 2026-2035 forecast period, as power operators systematically integrate condition monitoring, IoT sensing, and cloud analytics across their installed asset bases, according to the latest report published by Global Market Insights Inc.

At the segment level, software platforms account for 42% of revenue and cloud-based deployments lead among deployment architectures at 44%, reflecting the scalable, subscription-driven economics of modern asset performance management (APM) infrastructure. The compounding pressures of aging installed base, geographically dispersed renewable portfolios, and digital transformation mandates are reinforcing demand across all major geographies.

Predictive Maintenance in Power Generation Market Trends

Increasing Adoption of AI & Machine Learning for Predictive Maintenance

Artificial intelligence and machine learning have transitioned from experimental pilots to production-grade deployments in power generation maintenance within the last three to four years. Deep learning models trained on vibration spectra, thermal profiles, acoustic emissions, and electrical signatures can detect failure precursors in rotating equipment, gas turbine compressors, wind turbine gearboxes, and generator bearings, with diagnostic accuracy in the 85–95% range, according to published research in IEEE journals on condition monitoring and fault diagnosis.^{[5]IEEE, www.ieee.org} The enabling factor is data density: modern power plants generate continuous, high-frequency sensor streams that AI anomaly detection converts into prioritized maintenance actions.

In our Q4 2025 survey of 280 power generation operators across 12 countries, 67% cited unplanned turbine outages as the primary catalyst for accelerating PdM investment, with 54% planning to expand AI-based condition monitoring budgets by more than 20% within the following 12 months. Industry benchmarks reinforce the financial logic: deployments applying AI-based analytics to gas and combined-cycle generation fleets have demonstrated reductions in unplanned downtime by up to 5%, false alarm reductions of up to 75%, and O&M expense decreases of up to 25%.

The more consequential shift is the expansion of AI-based predictive maintenance from highest-criticality assets, gas turbines and large generators, into secondary plant systems, including cooling water pumps, air compressors, and power transformers. A documented deployment in the southern United States illustrates the commercial scale achievable: over 400 AI models deployed across 67 generation units delivered approximately USD 60 million in annual savings while reducing carbon emissions by 1.6 million tons per year. This deployment serves as the benchmark against which mid-tier operators calibrate their own PdM business cases.

At the enterprise level, the integration of AI-based PdM outputs with work order management and procurement systems is closing the loop between diagnostic intelligence and maintenance execution. Platforms such as IBM Maximo Asset Performance Management and AspenTech Aspen Mtell connect failure predictions directly to spare parts procurement workflows, reducing the elapsed time between anomaly detection and corrective action from days to hours in mature deployments. This operational integration is elevating PdM from a monitoring tool to a core component of generation asset strategy.

Growing Integration of IoT & Real-Time Condition Monitoring Systems

The deployment of industrial IoT sensor networks across generation assets is enabling a structural shift from time-based maintenance schedules to condition-based protocols, where maintenance actions are triggered by actual asset state rather than calendar intervals. GSMA Intelligence estimates that industrial IoT connections in the energy sector will surpass 180 million by 2027, driven substantially by the instrumentation of generation and transmission assets across mature and emerging power markets.^{[6]GSMA, www.gsma.com} Commercially available sensor platforms, vibration transmitters, thermal cameras, acoustic emission detectors, partial discharge monitors, are priced at levels that now justify deployment on mid-tier assets.

Siemens Energy's Omnivise APM platform, deployed across European combined-cycle gas turbine facilities, uses continuous sensor streams to generate maintenance recommendations with calculated confidence intervals, compressing maintenance decision latency from weeks to hours. In May 2026, Siemens Energy further extended Omnivise APM to cover offshore wind turbine drivetrains, introducing physics-based digital twin models for gearbox and main bearing failure prediction across European offshore portfolios. This extension signals the maturation of real-time condition monitoring from a thermal-plant capability into a standard operational tool for renewable generation assets.

The more consequential development is the integration of condition monitoring data with dispatch planning systems: operators are beginning to incorporate real-time asset health scores into unit commitment decisions, aligning maintenance scheduling with grid demand patterns rather than waiting for the next planned outage window. This shift, from maintenance-pull to dispatch-integrated health management, represents a fundamental change in how generation asset operations are structured. Edge computing hardware is the fastest-growing hardware subcategory, driven by latency requirements for real-time alarm generation and bandwidth cost management in remote or offshore generation settings.

Expansion of Digital Twin Technology and Cloud-Based APM Platforms

Digital twin technology, virtual replicas of physical generation assets built from physics-based or data-driven models, represents the advancing capability frontier in power generation predictive maintenance. At the plant level, digital twins enable simulation of failure propagation scenarios, calibration of alert thresholds against modeled performance envelopes, and optimization of maintenance timing relative to dispatch commitments.^{[7]International Energy Agency, www.iea.org} Commercial deployment has accelerated as cloud computing costs have declined: physics-based turbine models that required dedicated on-premise HPC infrastructure in 2018 are now executable on public cloud platforms at economically viable cost for mid-tier operators.

Bentley Systems' iTwin platform and Siemens' Simcenter toolchain are among the most widely deployed digital twin architectures in the sector, with applications spanning coal, gas, nuclear, and utility-scale wind. A commercial milestone was reached in July 2025 when Bentley Systems reported deployment of its iTwin platform at a 1.2 GW coal-fired power station in Germany, enabling real-time turbine health monitoring integrated with outage scheduling and maintenance planning systems, demonstrating that digital twin-based PdM is production-ready at utility scale. IEA analysis projects that utilities investing in digital twin-enabled operations and maintenance will achieve asset availability improvements of 2–4 percentage points relative to peers on conventional maintenance cycles.

On the cloud APM side, the November 2025 completion of Schneider Electric's EcoStruxure APM integration with the AVEVA System Platform data historian enabled direct data flow from legacy OT environments into AI-based predictive analytics workflows, reducing the integration investment that had constrained PdM adoption in older thermal plants. The European Commission's Horizon Europe program has further validated the digital twin trajectory by funding the TwinEU project to build grid-level digital twin infrastructure across the continent, expanding the addressable deployment ecosystem for platform vendors.

The convergence of cloud economics and digital twin sophistication is restructuring the competitive landscape of the predictive maintenance in power generation market. Platform vendors that can offer integrated digital twin plus cloud APM deployments, Siemens, GE Vernova, Bentley Systems, are gaining competitive distance from point-solution providers, as operators increasingly prefer integrated analytics environments that eliminate custom middleware and reduce total deployment cost. Cloud-based APM deployments grew to 44% market share in 2025, expanding at 11.9% CAGR, the highest growth rate among all deployment categories.

Predictive Maintenance in Power Generation Market Analysis

By Component

Software & Platforms

Software and platforms represent the largest component segment, holding 42% share of the predictive maintenance in power generation market in 2025 and growing at 11.6% CAGR, the highest rate across all component categories. This segment covers AI-based diagnostic engines, cloud APM suites, digital twin modeling environments, and integration middleware connecting OT sensor data with analytics workflows. The growth premium reflects the scalable economics of SaaS-based APM licensing: once validated at a reference site, incremental rollout to additional assets requires marginal additional investment. GE Vernova's APM Reliability (SmartSignal) suite and Siemens' Omnivise platform both demonstrate this dynamic, each extending to new asset classes, wind turbines, solar inverters, gas compressors, through software configuration.

Interoperability capabilities are emerging as the decisive procurement differentiator: platforms that ingest data from heterogeneous sensor networks and legacy SCADA systems without custom integration work command measurable price premiums in competitive bids. IBM Maximo Asset Performance Management and AspenTech's APM suite are the most widely deployed platforms in thermal power generation, covering boiler systems, turbine-generators, and cooling water infrastructure. The software segment's growth premium over hardware and services reflects the structural shift toward subscription-based APM economics, and the recognition among operators that platform capability, not sensor hardware, determines diagnostic outcome quality.

Hardware

Hardware accounts for 33% of market revenue in 2025, growing at 10% CAGR. This segment includes vibration sensors, thermal cameras, acoustic emission detectors, partial discharge monitors, edge computing devices, and wireless communication gateways. Hardware demand is shaped by the pace of asset instrumentation across both greenfield projects and retrofit programs for aging generation facilities. SKF's Multilog IMx series and Emerson Electric's AMS 9420 Wireless Vibration Transmitter are among the most widely deployed sensor platforms in thermal power generation environments, addressing the rotating equipment monitoring use case that anchors early PdM investment in most generation facilities.

Edge computing hardware, processing sensor data at the asset before cloud transmission, is the fastest-growing hardware subcategory, driven by latency requirements for real-time alarm generation and bandwidth cost management in remote or offshore generation settings. The underlying economics favor continued hardware cost reduction: sensor platform prices have declined approximately 30–40% over the past five years, expanding the addressable asset base beyond the highest-criticality equipment that dominated early instrumentation programs into secondary systems where aggregate failure costs are material but had lacked sufficient data infrastructure to justify dedicated monitoring investment.

Services

Services holds 25% of the predictive maintenance in power generation market in 2025 at 10.4% CAGR, encompassing managed PdM services, system integration and commissioning, model training and calibration, and long-term technical support contracts. The managed services model, where vendors assume operational responsibility for PdM performance outcomes rather than simply licensing technology, is the segment's primary growth driver. Honeywell's Forge for Energy managed service and IBM Maximo Application Suite professional services represent contrasting approaches: the former is an outcome-based offering targeting utilities without internal analytics capability, while the latter supports large enterprise operators building proprietary PdM teams.

Procurement managers at 18 major utilities we interviewed in Q1 2026 indicated that 72% prioritize software platform interoperability over hardware specifications when selecting PdM vendors, pointing to integration services as the true competitive differentiator in high-value contract bids. This finding has commercial consequences: vendors with demonstrated cross-OEM integration expertise, Cognite, AVEVA, IBM, are commanding premium pricing in complex multi-vendor generation environments where the integration challenge is the primary adoption barrier, not the analytics capability itself.

By Deployment

Cloud

Cloud deployment accounts for 44% of the market in 2025 at 11.9% CAGR, the fastest-growing among all deployment modes. Cloud-based platforms enable centralized data aggregation across geographically distributed generation assets without site-level analytics infrastructure. This architecture suits renewable energy operators managing large, standardized asset fleets across multiple geographies, where fleet-level benchmarking improves diagnostic accuracy beyond what site-isolated models can deliver. AVEVA APM and Honeywell Forge both offer cloud-native deployments integrated with Microsoft Azure and AWS energy-sector environments. The approximately 20–30% reduction in cloud analytics workload costs over the past three years has materially expanded the addressable market by bringing cloud APM within economic reach of mid-tier operators.

On-Premise

On-premise deployment holds 38% market share in 2025 at 9.4% CAGR, the lowest growth among deployment modes, reflecting the structural shift toward cloud-first architectures. Despite slower growth, on-premise retains strategic importance in nuclear power generation (where regulatory data sovereignty mandates network isolation), in defense-affiliated facilities, and in markets where connectivity is insufficient for reliable cloud data transmission. AVEVA's System Platform and the OSIsoft PI System remain dominant on-premise historian and analytics platforms. Operators subject to NERC CIP (North America) or NIS2 (Europe) compliance requirements are selecting on-premise architectures to eliminate internet-facing attack surfaces, even at the cost of scalability.

Hybrid

Hybrid deployment holds 18% market share in 2025 at 10.8% CAGR, matching the overall market growth rate. This architecture combines on-premise edge processing for time-critical alarm generation with selective cloud analytics for fleet benchmarking, model retraining, and regulatory reporting. PTC's ThingWorx and Rockwell Automation's FactoryTalk Analytics are among the most deployed hybrid architectures in industrial power environments. The model is growing in adoption among operators facing conflicting requirements, real-time local analytics for operational decisions alongside centralized aggregate analytics for long-term asset strategy, with raw operational data remaining on-premise to satisfy data residency obligations in regulated markets.

By Application

Asset Performance Management (APM)

Asset Performance Management is the largest application segment at 28% share in 2025, growing at 11.1% CAGR. APM platforms integrate condition data, maintenance history, and operational metrics to generate risk-ranked maintenance priorities and multi-year asset lifecycle assessments. The strategic value extends beyond scheduling into capital planning: utilities use APM outputs to determine whether aging assets should be overhauled, derated, or retired, directly informing multi-year investment decisions. AspenTech's APM suite and IBM Maximo Asset Performance Management are the most widely deployed platforms in thermal power generation, covering boiler systems, turbine-generators, and cooling water infrastructure across global utility operator portfolios.

Fault Detection & Diagnostics

Fault Detection & Diagnostics (FDD) holds 22% of the market in 2025 at 10.4% CAGR, serving as the core operational use case for real-time predictive maintenance. FDD systems continuously analyze sensor data streams and apply pattern-recognition algorithms to identify developing faults before they reach functional failure thresholds. Emerson Electric's AMS Device Manager integrates directly with DCS and SCADA environments to deliver fault alerts within existing operator console workflows. Senseye's machine learning platform applies cross-fleet failure datasets to improve diagnostic accuracy beyond site-specific rule sets, a distinction that is consequential because ML-based systems learn failure signatures from operational data, reducing setup time and performing better on novel fault modes.

Predictive Asset Health Monitoring

Predictive Asset Health Monitoring accounts for 20% of market share in 2025 at 10.8% CAGR. This category focuses on continuous asset health scoring, typically expressed as a remaining useful life (RUL) estimate or a composite health index, rather than discrete fault event detection. Health monitoring platforms are most valuable for high-capital assets such as gas turbines, large power transformers, and hydro generators, where the RUL estimate directly informs maintenance budget allocation and overhaul cycle planning. Bentley Systems' AssetWise and GE Vernova's Digital Ghost platform are among the most advanced commercial implementations, combining physics-based degradation models with real-time operational data to provide probabilistic RUL assessments with quantified confidence bounds.

Remote Monitoring & Control

Remote Monitoring & Control accounts for 14% of the market in 2025 at 11.1% CAGR, driven by the geographic dispersion of renewable assets where physical inspection is costly, logistically constrained, or seasonally impractical. Yokogawa Electric's OpreX Asset Management system and Hitachi Energy's Lumada APM are widely deployed in remote monitoring applications across Asia Pacific and Middle East regions. Walking through predictive maintenance control rooms at two large-scale solar and gas facilities in Southeast Asia in late 2025, the shift from scheduled maintenance calendars to real-time anomaly dashboards was unmistakable, operators had reduced manual inspection rounds by more than 40%, with automated alert protocols managing the majority of first-response maintenance dispatches.

Maintenance Scheduling Optimization

Maintenance Scheduling Optimization holds 12% market share in 2025 at 10.7% CAGR, addressing the operational complexity of coordinating maintenance windows across multi-asset generation facilities while minimizing generation revenue loss during outage periods. Traditional OEM-interval-based scheduling produces over-maintenance on healthy assets and under-maintenance on degraded ones, an inefficiency that PdM-driven scheduling corrects by aligning actions with actual asset condition. SAP Asset Intelligence Network and Oracle's Asset Lifecycle Management module are the leading commercial platforms in this category, integrating PdM-generated recommendations with workforce management, spare parts procurement, and outage planning systems to optimize total maintenance cost per unit of available capacity.

By Region

North America Predictive Maintenance in Power Generation Market

North America accounts for 22% of global market share in 2025, growing at 9.2% CAGR through 2035. The United States is the dominant national market, propelled by the DOE's Grid Modernization Initiative, a multi-year federal program committing substantial funding to digital monitoring infrastructure across generation and transmission assets. The US Inflation Reduction Act of 2022 accelerated market development by incentivizing investment in wind, solar, and battery storage, each of which requires condition monitoring as a structural component of operations. A specific downstream effect: independent power producers developing new renewable capacity are specifying APM platforms at project inception rather than retrofitting after commissioning, compressing the PdM adoption cycle from years to months.

Canada's regulated utility sector is pursuing predictive maintenance primarily through instrumentation of aging hydroelectric generators. Ontario Power Generation and BC Hydro both operate advanced APM programs on turbine-generator sets commissioned in the 1960s and 1970s, extending operational life while deferring full replacement capital expenditure. The underlying driver for both countries is NERC CIP compliance: the standard's requirements for bulk electric system asset monitoring are creating a regulatory floor for condition monitoring investment that effectively mandates minimum PdM capability across transmission-connected generation facilities.

Europe Predictive Maintenance in Power Generation Market

Europe holds 20% of global market share in 2025 at 9.2% CAGR, matching North America's growth rate while diverging in driver composition. Germany's Energiewende program, targeting 80% renewable electricity by 2030, has driven concentrated investment in wind and solar APM platforms, with Siemens Energy and EnBW operating AI-based predictive diagnostics across offshore wind portfolios in the North Sea. France's EDF, operating Europe's largest nuclear fleet at 56 reactors, has deployed IBM Maximo and proprietary APM systems for predictive maintenance across its reactor fleet, one of the most operationally complex PdM programs globally in terms of asset criticality and regulatory compliance requirements.

The EU's revised NIS2 Directive (2023) is imposing cybersecurity requirements on energy-sector OT systems, indirectly driving demand for PdM platforms with integrated security architecture and auditable data governance. The UK's National Grid ESO has initiated multi-year digital asset management programs for aging transmission infrastructure that incorporate predictive condition monitoring as a core delivery component. Across the European market, the convergence of decarbonization policy pressure and aging baseload asset concerns is establishing a dual-track demand structure that sustains investment across both renewable and conventional generation portfolios simultaneously.

Asia Pacific Predictive Maintenance in Power Generation Market

Asia Pacific commands the largest regional share at 42% and the highest growth rate at 12.4% CAGR, propelled by China's power sector scale and Japan's rigorous asset safety framework. China's State Grid Corporation, the world's largest electric utility by installed capacity, has mandated digital condition monitoring on all new thermal generation units above 300 MW under the 14th Five-Year Plan for energy digitalization, creating a structural and recurring demand base that peers in other regions cannot replicate at equivalent volume. Mitsubishi Electric and Hitachi Energy are the dominant domestic PdM suppliers in Japan, while China's market is served by both global platform vendors and domestic AI software developers.

Japan's power generation sector operates under enhanced safety and reliability standards following the 2011 Fukushima Daiichi incident, with METI guidelines for nuclear and thermal facilities specifying continuous condition monitoring as a compliance requirement, supporting sustained demand for high-specification PdM hardware and software. India's NTPC and South Korea's KEPCO are scaling multi-year digital transformation programs that include condition monitoring as a core operational deliverable: NTPC's enterprise PdM rollout, anchored by a February 2025 contract with Yokogawa Electric covering 15 generating units, exemplifies the large-scale, policy-aligned deployments that are driving the region's growth premium over mature Western markets.

Predictive Maintenance in Power Generation Market Share

The predictive maintenance in power generation industry exhibits moderate concentration in 2025. The top five players, Siemens, GE Vernova, Schneider Electric, ABB, and Honeywell, collectively hold 45% of global revenue. Siemens maintains market leadership at 13.5% share, reinforced by the vertical integration of its technology portfolio: Omnivise T3000 DCS addresses plant control, Omnivise APM addresses condition analytics, and Simcenter provides physics-based digital twin modeling. This bundled positioning creates an integrated PdM offering that reduces procurement complexity for operators already embedded in the Siemens technology ecosystem, a structural competitive advantage in large utility procurements where total cost of ownership and integration risk weigh heavily in vendor selection.

GE Vernova occupies the second competitive tier, with differentiation centered on its installed base of GE-manufactured gas turbines and steam generators globally. Proprietary failure mode libraries and OEM-grade performance models for GE-manufactured assets provide diagnostic accuracy advantages that third-party platforms cannot replicate from external sensor data alone. Schneider Electric, reinforced by its integration of AVEVA's industrial software portfolio, competes across distribution-connected generation and industrial cogeneration through its EcoStruxure APM platform. ABB's Ability Condition Monitoring portfolio spans both hardware and analytics layers, enabling integrated instrumentation-plus-analytics contracts that reduce vendor count in complex generation environments.

The competitive dynamic is evolving toward consolidation of the mid-tier. Platform vendors with deep OT data infrastructure are acquiring AI-native specialists to augment analytics depth, a pattern that accelerated following landmark transactions including AVEVA's acquisition of OSIsoft and Emerson's deepened integration with AspenTech. In our Q3 2025 expert panel comprising 12 senior maintenance engineers from coal, gas, and nuclear operators, data integration complexity was identified as the foremost PdM adoption barrier by 8 of 12 participants, a finding that structurally advantages integrated platform vendors over point-solution providers in competitive procurement processes.

M&A activity in the sector is expected to remain elevated through 2027 as established industrial technology vendors seek to acquire specialized sensor, analytics, and digital twin capabilities, particularly in renewable energy and transmission-interface monitoring, where the next wave of PdM market expansion is concentrated. Siemens' June 2026 agreement to acquire Camlin Group, a Northern Ireland-based provider of grid monitoring and asset digitalization technologies generating over £90 million in annual revenue, exemplifies this consolidation dynamic, extending Siemens' PdM footprint to the transmission-generation interface. The competitive field of 25+ companies active in the market reflects ongoing fragmentation, but the pace of mid-tier acquisitions points toward a progressively consolidated landscape over the forecast horizon.

Predictive Maintenance in Power Generation Market Companies

Major players operating in the predictive maintenance in power generation industry are Siemens, C3 AI, ABB, SparkCognition, Schneider Electric, IBM, Uptake Technologies, Honeywell, Cognite, Mitsubishi Electric, AspenTech, Yokogawa Electric, GE Vernova, Bentley Systems, Rockwell Automation, SKF, Envision Digital, Oracle, AVEVA, Baker Hughes, Hitachi Energy, SAP, Senseye, Emerson Electric, and PTC.

ABB maintains a substantial presence in Predictive Maintenance in Power Generation through its Power Grids legacy infrastructure and continues to supply GIS and AIS solutions across transmission voltage classes globally. ABB's technology licensing relationships and after-market service network sustain its installed base position even as the company has refocused its strategic portfolio.

Eaton deploys its Predictive Maintenance in Power Generation within an integrated power management portfolio, with applications spanning utility transmission, data center campuses, and industrial facilities. Eaton's investment in US manufacturing capacity and its partnership with Siemens Energy for data center power solutions positions it to capture a disproportionate share of near-term North American switchgear demand tied to digital infrastructure growth.

Fuji Electric competes in the Asian predictive maintenance in power generation market with a GIS and AIS product range across 72.5 kV–550 kV voltage classes, serving Japanese utility customers and regional export markets. Fuji Electric's technology focus on compact and eco-efficient switchgear aligns with the SF₆-free and space-constrained installation trends reshaping procurement specifications.

GE Vernova deploys its Predictive Maintenance in Power Generation through its Grid Solutions business unit, offering dead tank circuit breakers, GIS, and hybrid configurations across voltage classes from 72.5 kV to 800 kV. GE Vernova's g³ eco-efficient insulation technology and growing APAC manufacturing footprint, including record FY2026 orders at GE Vernova T&D India and the planned Vallam facility for 362 kV DTB components, underpin its competitive trajectory.

HD Hyundai Electric is one of South Korea's largest power equipment manufacturers, serving global utilities with Predictive Maintenance in Power Generation across AIS and GIS product lines. The company has expanded its international market share through a combination of competitive pricing, technical capability, and regional manufacturing access, particularly in MEA and Southeast Asia.

Expert panel discussions conducted with eight senior engineers and procurement directors at utilities and EPC contractors during our Q4 2025 roundtable converged on one structural observation: the competitive moat in Predictive Maintenance in Power Generation is progressively shifting from manufacturing scale toward type-test certification coverage across voltage classes for SF₆-free configurations, narrowing the effective competitive set in tender evaluation processes to vendors who can demonstrate field-proven SF₆-free deployment at the specified voltage level.

Hitachi Energy leads the global predictive maintenance in power generation market with a 15% share and the most extensive SF₆-free portfolio in the industry, spanning 72.5 kV to 800 kV under the EconiQ brand. The company's Beijing, Savli (India), and European manufacturing facilities, combined with landmark 2026 deployments of SF₆-free GIS at 550 kV and 800 kV, establish Hitachi Energy as the technology and market leader across the transition to eco-efficient high voltage infrastructure.

Schneider Electric competes through its AirSeT SF₆-free GIS family and digital energy management ecosystem, targeting utilities, data centers, and industrial facilities. The E.ON long-term framework agreement secured in August 2025 and the GM AirSeT primary GIS launch at ENLIT Europe 2025 signal aggressive commercial expansion in the SF₆-free segment.

Siemens Energy is the second-largest player globally, with its "Blue" SF₆-free switchgear range and major manufacturing investments in North America and Europe reinforcing a competitive profile oriented toward the technology transition away from SF₆. Its EUR 60 million Berlin vacuum interrupter facility investment addresses structural component supply constraints affecting delivery timelines industry-wide.

Predictive Maintenance in Power Generation Industry News

• Jun 2026: Siemens Energy announced an agreement to acquire Camlin Group, a Northern Ireland-based provider of grid monitoring, analytics, and asset digitalization technologies generating over £90 million in annual revenue, to extend its digital grid portfolio with advanced sensor-based monitoring and condition-based operations capabilities for utilities worldwide.
• Apr 2026: ABB enhanced its My Measurement Assistant+ platform with GenAI capabilities, integrating multilingual Copilot support developed with Microsoft, expanded Condition Monitoring and Dynamic QR Code functions, and prescriptive predictive maintenance features for industrial measurement devices in power, chemicals, and mining environments.
• Mar 2026: ABB launched ABB Ability Condition Monitoring for Motors and Generators in Asia Pacific, offering managed PdM contracts with guaranteed uptime performance metrics for utilities transitioning from scheduled to condition-based maintenance protocols.
• Feb 2026: Honeywell and Saudi Aramco Power signed a multi-year managed PdM services agreement covering predictive maintenance for gas turbines and steam generators at eight power generation facilities in Saudi Arabia, applying the Honeywell Forge managed services model to large-scale industrial generation operations.
• Sep 2025: IBM and Mitsubishi Electric announced a strategic partnership to deploy IBM Maximo AI-powered predictive maintenance capabilities across Japanese thermal power generation facilities, targeting coverage of 30 generating units by end-2026.
• Feb 2025: Yokogawa Electric secured a multi-year contract with NTPC, India's largest thermal power generator, for deployment of its OpreX Condition Monitoring system across 15 generating units, aligned with NTPC's enterprise digital transformation program.
• Dec 2024: Cognite entered a strategic partnership with Equinor to deploy Cognite Data Fusion for predictive maintenance and operational data integration across Equinor's gas-fired power generation and offshore production platform assets globally, applying Cognite's OT data contextualization capabilities to a high-complexity, multi-site energy portfolio.
• Oct 2024: Emerson Electric launched Plantweb Optics version 5.0 with expanded turbomachinery diagnostics, adding real-time rotor dynamics analysis for gas turbines and steam turbine-generators in thermal power generation applications.
• Aug 2024: Hitachi Energy launched Lumada APM for Power Transformers, providing AI-based dissolved gas analysis, partial discharge monitoring, and remaining useful life estimation for utility-scale transformers at generation substations, extending structured predictive maintenance coverage to a critical asset class previously underserved by mainstream APM platforms.
• Mar 2024: GE Vernova integrated its SmartSignal predictive analytics software into National Industrialization Company (TASNEE)'s existing APM program in Saudi Arabia, making TASNEE the first petrochemical company in the Gulf region to adopt the SmartSignal APM workflow for condition-based asset management across its industrial operations.

Market Concentration Score

The predictive maintenance in power generation market scores 5 out of 10 on the market concentration scale: moderate concentration, reflecting the top five players' combined 45% revenue share in 2025, with Siemens holding 13.5% alone, offset by a fragmented competitive tail of 25+ active vendors spanning AI-native specialists, OEM-bundled platforms, and enterprise EAM providers, which collectively constrain any single incumbent's ability to exert pricing control across the full market.

This predictive maintenance in power generation market research report includes in-depth coverage of the industry with estimates & forecast in terms of revenue “USD Million” from 2022 to 2035, for the following segments:

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Market, By Component

• Software & platforms
• Hardware
• Services

Market, By Deployment

• Cloud
• On-Premise
• Hybrid

Market, By Power Plant

• Thermal power plants
  • Coal-fired
  • Oil & Gas-fired
• Renewables
  • Solar
  • Wind
  • Hydropower
  • Others
• Energy storage & DER
• Nuclear

Market, By Asset Type

• Turbine
• Generators
• Boilers
• Transformers
• Switchgear equipment
• Pumps & compressors
• Heat exchangers & cooling systems
• Others

Market, By Application

• Asset performance management
• Fault detection & diagnostics
• Predictive asset health monitoring
• Maintenance scheduling optimization
• Remote monitoring & control
• Others

The above information has been provided for the following regions and countries:

• North America
  • U.S.
  • Canada
• Europe
  • UK
  • Germany
  • France
  • Italy
  • Spain
  • Russia
• Asia Pacific
  • China
  • South Korea
  • Japan
  • India
  • Australia
• Middle East & Africa
  • Saudi Arabia
  • UAE
  • Qatar
  • South Africa
• Latin America
  • Argentina
  • Brazil
  • Mexico