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商品編碼

2083111

電力產業預測性維護：市場機會、成長要素、產業趨勢分析及2026-2035年預測

Predictive Maintenance in Power Generation Market Opportunity, Growth Drivers, Industry Trend Analysis, and Forecast 2026 - 2035

出版日期: 2026年06月26日 | 出版商:

Global Market Insights Inc. | 英文 160 Pages | 商品交期: 2-3個工作天內

價格

簡介目錄

預計到 2025 年，全球發電產業預測性維護市場規模將達到 20 億美元，並有望以 10.8% 的複合年成長率成長，到 2035 年達到 56 億美元。

全球發電產業預測性維護市場的成長主要受以下因素驅動：人工智慧診斷技術的日益普及、設備意外運作導致的營運成本不斷上升，以及發電資產數位化轉型加速推進。電力公司正擴大部署狀態監測系統、物聯網感測器和雲端分析平台，以提高其火力發電、可再生和核能發電基礎設施的資產可靠性。發電設備的老化以及地理位置分散的可再生能源資產的快速擴張，進一步增加了對先進維護解決方案的需求。風能和太陽能發電廠的運作環境變化劇烈且難以檢查，傳統的維護策略不足以預防故障，因此凸顯了預測性維護方法日益成長的需求。此外，諸如SCADA現代化、OT/IT整合和企業資源規劃（ERP）升級等數位轉型舉措，實現了從運作中設備到分析系統的無縫資料流。這種互聯互通的基礎設施使人工智慧驅動的平台能夠處理各種發電資產的即時運行數據，並大規模產生可操作的維護洞察，從而擴大了預測性維護解決方案的市場。

市場範圍
開始年份	2025
預測期	2026-2035
上市時的市場規模	20億美元
預測金額	56億美元
複合年成長率	10.8%

到2025年，軟體和平台領域將佔42%的市場佔有率。該領域包括基於人工智慧的診斷工具、基於雲端的資產性能管理系統、數位孿生解決方案以及連接營運技術（OT）數據和分析平台的中間件。該領域的成長主要得益於基於軟體的部署模式的擴充性，這種模式允許解決方案跨多個資產部署，同時最大限度地降低初始部署後的額外成本。人工智慧驅動的資產監控平台日益普及，持續提升了發電設施的預測的準確性和營運效率。

預計到2025年，雲端採用領域將佔據44%的市場佔有率，複合年成長率（CAGR）為11.9%。基於雲端的系統能夠集中聚合和分析分散式發電資產的數據，而無需大規模的現場基礎設施。這種模式尤其適用於管理跨多個地點的大規模可再生能源設施的營運商，因為聚合的數據可以提高整個資產基礎的預測準確性和營運基準。

預計到2025年，北美發電行業的預測性維護市場佔有率將達到22%，並在2035年之前以9.2%的複合年成長率成長。該地區的成長主要得益於對電網現代化改造的持續投資，以及整個發電和輸電基礎設施中數位化監控技術的廣泛應用。此外，先進分析技術和互聯資產管理系統的整合也進一步加速了預測性維護在該地區電力產業的普及。

產業生態系分析
影響產業的因素
- 促進因素
- 產業潛在風險與挑戰
成長潛力分析
監理情勢
波特的分析
PESTLE分析
技術與創新展望
- 人工智慧和機器學習在預測性維護的應用
- 基於物聯網的電廠狀態監測
- 數位孿生技術在發電設施的應用
- 邊緣運算和即時分析
- 基於雲端的預測性維護平台
價格分析
- 按組件（軟體、硬體、服務）分類的價格趨勢
- 按供應商類型分類的定價策略（OEM、軟體供應商、服務供應商）
人工智慧和生成式人工智慧對市場的影響
- 人工智慧驅動的預測性維護和故障檢測
- 生成式人工智慧在維護計畫和資產管理的應用案例
- 自主維護與決策智慧
- 風險、限制與網路安全挑戰
新機會與趨勢
投資分析及未來展望

第4章競爭情勢

介紹
企業市佔率分析：按地區分類
- 北美洲
- 歐洲
- 亞太地區
- 中東和非洲
- 拉丁美洲
競爭定位矩陣
主要進展
- 併購
- 夥伴關係和聯盟
- 新產品發布
- 業務拓展計劃及資金籌措

第5章市場規模及預測：依組件分類，2022-2035年

軟體平台
硬體
服務

第6章市場規模及預測：依部署方式分類，2022-2035年

雲
現場
混合

第7章市場規模及預測：依發電廠分類，2022-2035年

火力發電廠
- 燃煤發電
- 石油和天然氣火力發電廠
可再生能源
- 太陽的
- 風力
- 水力
- 其他
儲能和分散式能源（DER）
核能

第8章市場規模及預測：依資產類型分類，2022-2035年

渦輪
發電機
鍋爐
變壓器
切換裝置
泵浦和壓縮機
熱交換器和冷卻系統
其他

第9章市場規模及預測：依應用領域分類，2022-2035年

資產績效管理
故障檢測與診斷
透過預測性維護進行資產狀態監測。
最佳化維護計劃
遠端監控和控制
其他

第10章市場規模及預測：依地區分類，2022-2035年

北美洲
- 美國
- 加拿大
歐洲
- 英國
- 德國
- 法國
- 義大利
- 西班牙
- 俄羅斯
亞太地區
- 中國
- 韓國
- 日本
- 印度
- 澳洲
中東和非洲
- 沙烏地阿拉伯
- UAE
- 卡達
- 南非
拉丁美洲
- 阿根廷
- 巴西
- 墨西哥

第11章：公司簡介

ABB
AspenTech
AVEVA
Baker Hughes
Bentley Systems
C3 AI
Cognite
Emerson Electric
Envision Digital
GE Vernova
Hitachi Energy
Honeywell
IBM
Mitsubishi Electric
Oracle
PTC
Rockwell Automation
SAP
Schneider Electric
Senseye
Siemens
SKF
SparkCognition
Uptake Technologies
Yokogawa Electric

簡介目錄

Product Code: 16125

The Global Predictive Maintenance in Power Generation Market was valued at USD 2 billion in 2025 and is estimated to grow at a CAGR of 10.8% to reach USD 5.6 billion by 2035.

Predictive Maintenance in Power Generation Market - IMG1

Growth in the global predictive maintenance in power generation market is driven by increasing adoption of AI-powered diagnostics, rising operational costs associated with unplanned equipment downtime, and accelerating digital transformation across power generation assets. Utilities are increasingly deploying condition monitoring systems, IoT-enabled sensors, and cloud-based analytics platforms to improve asset reliability across thermal, renewable, and nuclear power infrastructure. Aging power generation fleets and the rapid expansion of geographically distributed renewable energy assets are further intensifying demand for advanced maintenance solutions. As wind and solar installations operate under highly variable and hard-to-inspect conditions, traditional maintenance strategies are proving insufficient to prevent failures, strengthening the case for predictive approaches. Additionally, digital transformation initiatives such as SCADA modernization, OT/IT integration, and enterprise resource planning upgrades are enabling seamless data flow from operational equipment to analytics systems. This connected infrastructure is expanding the addressable market for predictive maintenance solutions by allowing AI-driven platforms to process real-time operational data and generate actionable maintenance insights at scale across diverse generation assets.

Market Scope
Start Year	2025
Forecast Year	2026-2035
Start Value	$2 Billion
Forecast Value	$5.6 Billion
CAGR	10.8%

The software and platforms segment accounted for 42% share in 2025. This segment includes AI-based diagnostic tools, cloud-based asset performance management systems, digital twin solutions, and middleware that connects operational technology data with analytics platforms. Growth in this category is driven by the scalability of software-based deployment models, where solutions can be replicated across multiple assets with minimal incremental cost after initial implementation. Increasing adoption of AI-powered asset monitoring platforms continues to enhance predictive accuracy and operational efficiency across power generation facilities.

The cloud deployment segment held a 44% share in 2025 and is projected to grow at a CAGR of 11.9%. Cloud-based systems enable centralized data aggregation and analysis across distributed power generation assets without requiring extensive on-site infrastructure. This model is particularly well suited for operators managing large renewable energy fleets across multiple locations, where aggregated data improves predictive accuracy and operational benchmarking across assets.

North America Predictive Maintenance in Power Generation Market accounted for 22% share in 2025 and is expected to grow at a CAGR of 9.2% through 2035. The region's growth is supported by ongoing investments in grid modernization initiatives and the widespread adoption of digital monitoring technologies across generation and transmission infrastructure. Continued integration of advanced analytics and connected asset management systems is strengthening predictive maintenance adoption across the power sector in the region.

Major companies operating in the global predictive maintenance in power generation market include Siemens, GE Vernova, Schneider Electric, ABB, Honeywell, IBM, Oracle, Mitsubishi Electric, AspenTech, Yokogawa Electric, Rockwell Automation, SAP, Emerson Electric, Baker Hughes, Hitachi Energy, AVEVA, Bentley Systems, C3 AI, SparkCognition, Uptake Technologies, Cognite, SKF, Envision Digital, PTC, and Senseye. Companies operating in the predictive maintenance in power generation market are strengthening their market position by investing in advanced AI analytics, expanding cloud-based platform capabilities, and enhancing real-time asset monitoring solutions. Market participants are focusing on integrating IoT sensors, digital twin models, and machine learning algorithms to improve predictive accuracy and reduce equipment downtime. Strategic collaborations with utilities, energy operators, and technology providers are helping companies expand deployment across diverse power generation assets. Businesses are also prioritizing platform scalability, interoperability with existing OT and IT systems, and cybersecurity enhancements to support large-scale adoption.

Chapter 1 Methodology & Scope

1.1 Research approach
1.2 Quality commitment
- 1.2.1 GMI AI policy & data integrity commitment
  - 1.2.1.1 Source consistency protocol
1.3 Research Trail & Confidence Scoring
- 1.3.1 Research Trail Components
- 1.3.2 Scoring Components
1.4 Data Collection
- 1.4.1 Partial list of primary sources
1.5 Data mining sources
- 1.5.1 Paid sources
  - 1.5.1.1 Sources, by region
1.6 Base estimates and calculations
- 1.6.1 Base year calculation for any one approach
1.7 Forecast model
1.8 Research transparency addendum
- 1.8.1 Source attribution framework
- 1.8.2 Quality assurance metrics
- 1.8.3 Our commitment to trust
  - 1.8.3.1 Market definitions

Chapter 2 Executive Summary

2.1 Industry synopsis, 2022 - 2035
2.2 Business trends
2.3 Component trends
2.4 Deployment trends
2.5 Power plant trends
2.6 Asset type trends
2.7 Application trends
2.8 Regional trends

Chapter 3 Industry Insights

3.1 Industry ecosystem analysis
3.2 Industry impact forces
- 3.2.1 Growth drivers
- 3.2.2 Industry pitfalls & challenges
3.3 Growth potential analysis
3.4 Regulatory landscape
3.5 Porter's analysis
- 3.5.1 Bargaining power of suppliers
- 3.5.2 Bargaining power of buyers
- 3.5.3 Threat of new entrants
- 3.5.4 Threat of substitutes
3.6 PESTEL analysis
3.7 Technology & Innovation Landscape
- 3.7.1 AI & machine learning in predictive maintenance
- 3.7.2 IoT-enabled condition monitoring in power plants
- 3.7.3 Digital twin applications in power generation assets
- 3.7.4 Edge computing & real-time analytics
- 3.7.5 Cloud-based predictive maintenance platforms
3.8 Pricing Analysis (Driven by Primary Research)
- 3.8.1 Pricing trends by Component (Software, Hardware, Services)
- 3.8.2 Pricing strategy by vendor type (OEMs vs Software Providers vs Service Providers)
3.9 Impact of AI & generative AI on the market
- 3.9.1 AI-driven Predictive Maintenance & Fault Detection
- 3.9.2 GenAI use cases in maintenance planning & asset management
- 3.9.3 Autonomous maintenance & decision intelligence
- 3.9.4 Risks, limitations & cybersecurity challenges
3.10 Emerging opportunities & trends
3.11 Investment analysis & future outlook

Chapter 4 Competitive Landscape, 2026

4.1 Introduction
4.2 Company market share analysis, by region, 2025
- 4.2.1 North America
- 4.2.2 Europe
- 4.2.3 Asia Pacific
- 4.2.4 Middle East & Africa
- 4.2.5 Latin America
4.3 Competitive positioning matrix
4.4 Key Developments
- 4.4.1 Mergers & acquisitions
- 4.4.2 Partnerships & collaborations
- 4.4.3 New product launches
- 4.4.4 Expansion plans and funding

Chapter 5 Market Size and Forecast, By Component, 2022 - 2035 (USD Million)

5.1 Key trends
5.2 Software & platforms
5.3 Hardware
5.4 Services

Chapter 6 Market Size and Forecast, By Deployment, 2022 - 2035 (USD Million)

6.1 Key trends
6.2 Cloud
6.3 On-Premise
6.4 Hybrid

Chapter 7 Market Size and Forecast, By Power Plant, 2022 - 2035 (USD Million)

7.1 Key trends
7.2 Thermal power plants
- 7.2.1 Coal-fired
- 7.2.2 Oil & Gas-fired
7.3 Renewables
- 7.3.1 Solar
- 7.3.2 Wind
- 7.3.3 Hydropower
- 7.3.4 Others
7.4 Energy storage & DER
7.5 Nuclear

Chapter 8 Market Size and Forecast, By Asset Type, 2022 - 2035 (USD Million)

8.1 Key trends
8.2 Turbine
8.3 Generators
8.4 Boilers
8.5 Transformers
8.6 Switchgear equipment
8.7 Pumps & compressors
8.8 Heat exchangers & cooling systems
8.9 Others

Chapter 9 Market Size and Forecast, By Application, 2022 - 2035 (USD Million)

9.1 Key trends
9.2 Asset performance management
9.3 Fault detection & diagnostics
9.4 Predictive asset health monitoring
9.5 Maintenance scheduling optimization
9.6 Remote monitoring & control
9.7 Others

Chapter 10 Market Size and Forecast, By Region, 2022 - 2035 (USD Million)

10.1 Key trends
10.2 North America
- 10.2.1 U.S.
- 10.2.2 Canada
10.3 Europe
- 10.3.1 UK
- 10.3.2 Germany
- 10.3.3 France
- 10.3.4 Italy
- 10.3.5 Spain
- 10.3.6 Russia
10.4 Asia Pacific
- 10.4.1 China
- 10.4.2 South Korea
- 10.4.3 Japan
- 10.4.4 India
- 10.4.5 Australia
10.5 Middle East & Africa
- 10.5.1 Saudi Arabia
- 10.5.2 UAE
- 10.5.3 Qatar
- 10.5.4 South Africa
10.6 Latin America
- 10.6.1 Argentina
- 10.6.2 Brazil
- 10.6.3 Mexico