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市场调查报告书
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1641826

能源预测性维护：市场占有率分析、产业趋势与统计、成长预测（2025-2030 年）

Predictive Maintenance in the Energy - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2025 - 2030)
出版日期: | 出版商: Mordor Intelligence | 英文 100 Pages | 商品交期: 2-3个工作天内

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简介目录

能源领域的预测性维护市场规模在 2025 年估计为 22.5 亿美元，预计到 2030 年将达到 70.8 亿美元，预测期内（2025-2030 年）的复合年增长率为 25.77%。

能源市场的预测性维护 - IMG1

关键亮点

  • 最近，预测性维护（PdM）平台已成为市场发展的驱动力。 PdM 解决方案与新的或​​现有的机械基础设施集成，以评估机器的健康状况并检测即将发生故障的征兆。 PdM 整合使公司能够确保投资收益(ROI) 并实现全球远端机械监控，从而满足并超越其永续性目标。
  • 预测性维护大大帮助能源产业提高资产效率。巨量资料分析、物联网 (IoT) 和云端资料储存等新技术使工业设备和感测器能够将基于状态的资料发送到集中式伺服器，使故障检测 它已经成为。运作的增加、维护成本的降低、计划外故障和备件库存同时推动市场的发展并使其繁荣。此外，减少维修和大修时​​间对于预测性维护市场的成长至关重要。
  • 大多数能源公司都是资产密集型企业。确保这些资源正常运作并为消费者提供能源需要时间和精力。决定架构等机器学习技术可用于优化设备的运作并最终优化整个系统。类似地，类似的演算法可以将预防性维护程序自动化为预测性维护程序。它还可以实现边际定价、时间转换、资产利用以及能源生产和分配。
  • 预测性维护服务和解决方案在机器故障之前提供警报。整合业务资讯、感测器资料和企业资产管理 (EAM) 系统可以实现从被动维护到预测性维护服务和解决方案的快速转变。
  • 然而，高安装成本、环境问题、营运成本上升、消费者期望不断提高以及导致虚假索赔的资料误解等因素阻碍了预测性维护市场的成长。这些挑战推动了各种分析工具的采用，因为人们越来越需要更好地了解使用情况和性能模式以做出更好的决策。
  • COVID-19 对市场产生了重大影响。全球经济放缓对市场既有正面影响，也有负面影响。例如，能源消耗的下降是由于人们关门，这对市场造成了沉重打击。然而，疫情期间的劳动力短缺和供应链中断使得该行业的公司忙于运作机器的良好运作。

能源领域的预测性维护市场趋势

解决方案部门可望大幅成长

  • 能源产业对客製化工业预测性维护解决方案的需求日益增加，主要用于远端监控业务。巨量资料在分析流程、资产和重型机械方面也扮演着重要角色。
  • 包括 SAP、IBM 和 Microsoft 在内的多家供应商活跃于该市场，并根据组织的需求提供客製化的预测性维护解决方案和服务。这些解决方案可协助组织保护其关键资产并获得竞争生产力优势。
  • 人工智慧 (AI) 和机器学习 (ML) 使组织能够全面了解业务并获得有助于解决其行业中一些最具颠覆性的挑战的洞察力。由于能源领域的公司产生的巨量资料，有远见的公司正在投资监控和预测分析工具，以充分利用这些资料。根据 Gartner 预测，预测期内，该领域 40% 的新监控和控制系统将使用物联网 (IoT) 实现智慧操作。
  • 由于煤炭资源的枯竭，发电业正从煤炭转向太阳能和风力发电。由于气候条件的变化，大多数国家都对燃煤电厂进行了严格的监管。随着电力消耗量的增加，新兴国家正在投资先进技术和设备以扩大生产能力。
  • 透过采用预测性维护解决方案，有望帮助最终用户提高生产力，同时透过优化创新维护活动来最大限度地减少发电行业的故障。亚太新兴国家的发电产业要求更高的效率、更好的控制和更快的监控，以减少运作故障的可能性。
  • 对可再生能源发电的投资，尤其是风力发电机、离岸风力发电电场和太阳能发电厂的投资，正在推动中国和印度等国家预测性维护解决方案市场的成长。

北美占据主要市场占有率

  • 能源领域的预测性维护市场以北美为主，其次是欧洲。这是由于诸如众多服务供应商的存在、技术进步以及预防性维护知识的增加等基本因素所造成的。加拿大和美国等新兴经济体越来越重视技术进步的研发，推动了整个全部区域对预测性维护解决方案的需求。根据美国能源资讯署（US EIA）的数据，预计2020年至2040年间总能源消耗率将成长5％。
  • 为了保持盈利，企业必须提高能源效率并减少停机时间。这推动了公共产业和能源领域的资料分析市场的发展。日益增长的环境问题和对可持续能源的投资增加可能会影响市场成长。
  • 推动市场成长的其他因素包括增加对人工智慧 (AI) 和机器学习 (ML) 的投资以减少资产停机时间和维护成本，采用物联网 (IoT) 和机器学习技术以减少停机时间和维护成本。包括延长设备寿命和感测器整体寿命的需求、感测器价格的下降、感测器技术的进步以及高速网路技术的发展。此外，法规合规性是美国采用物联网 (IoT) 技术的关键驱动因素。在美国，随着《能源法案》（EA）的通过，追踪永续能源消耗的努力已经加速。
  • 能源产业是美国最大的产业之一，吸引了大量投资。例如，根据彭博新能源财经（BNEF）称，预计未来20年美国将在可再生能源产能方面投资约7,000亿美元。这些因素预计将推动能源领域预测性维护市场的成长。
  • 随着环境、社会和管治(ESG) 策略的加强，能源产业继续成为交易活动的温床。儘管公众投资者的兴趣仍然很高，但宏观经济压力可能对北美能源和公用事业公司带来各种估值挑战。例如，摩根大通以 78 亿美元收购了南泽西工业公司。同样，ArcLight Clean Energy Transition Corp 斥资 15 亿美元（15 亿澳元）收购了 OPAL Fuels LLC。这推动了北美预测性维护的成长。

能源预测性维护概述

由于国内外公司数量众多，能源市场预测性维护的竞争非常激烈。市场集中度适中，主要参与企业透过产品创新和併购等策略扩大市场力量。 IBM 公司、SAP SE、罗伯特博世有限公司、西门子股份公司等是市场的主要企业。

2022年6月，西门子收购了Senseye，该公司为工业公司提供预测性维护和资产智慧。透过收购 Senseye，西门子扩大了其创新的预测性维护和资产智慧产品组合。 Senseye 是一家以结果为导向的工业设备预测性维护解决方案製造商和供应商。 SenseEye 的预测性维护解决方案可将非计划机器停机时间减少 50%，并将维修人员的生产效率提高 30%。

日立有限公司于 2022 年 5 月推出了由日立能源和日立 Vantara 开发的“Lumada Inspection Insights”，以帮助企业实现资产检查自动化并推进永续性目标。这种新方法采用人工智慧 (AI) 和机器学习 (ML) 来评估资源、危险和各种影像类型，以解决导致故障的多种原因。

此外，2022 年 1 月，IBM 宣布收购环境绩效管理资料和分析软体供应商 Envizi。此次收购扩大了IBM 在人工智慧(AI) 软体方面的不断增长的投资，包括IBM Maximo 资产管理解决方案、IBM Environmental Intelligence Suite 和IBM Sterling 供应链解决方案，以帮助组织提高弹性和永续性。我们帮助您建立营运和供应链。

此外，此次收购扩大了公司的产品和服务范围。随着对云端基础的服务的需求不断增长，IBM Cloud 的广泛服务和专业知识将帮助全球更聪明的企业转变流程，吸收新技术和能力，并快速转向新的市场机会。

其他福利

  • Excel 格式的市场预测 (ME) 表
  • 3 个月的分析师支持

目录

第 1 章 简介

  • 研究假设和市场定义
  • 研究范围

第二章调查方法

第三章执行摘要

第四章 市场动态

  • 市场概况
  • 市场驱动因素
    • 增加对能源领域的投资
    • 提高自动化采用率
  • 市场问题
    • 实施成本高
  • 产业价值链分析
  • 产业吸引力-波特五力分析
    • 新进入者的威胁
    • 买家的议价能力
    • 供应商的议价能力
    • 替代品的威胁
    • 竞争对手之间的竞争强度
  • COVID-19 市场影响评估

第五章 市场区隔

  • 按产品
    • 解决方案
    • 按服务
  • 按部署模型
    • 本地
    • 云
  • 按地区
    • 北美洲
    • 欧洲
    • 亚太地区
    • 拉丁美洲
    • 中东和非洲

第六章 竞争格局

  • 公司简介
    • IBM Corporation
    • SAP SE
    • Siemens AG
    • Intel Corporation
    • Robert Bosch GmbH
    • Accenture PLC
    • ABB Ltd
    • Schneider Electric
    • Banner Engineering Corp.
    • GE Automation & Control

第七章投资分析

第八章 市场机会与未来趋势

简介目录
Product Code: 58744

The Predictive Maintenance in the Energy Market size is estimated at USD 2.25 billion in 2025, and is expected to reach USD 7.08 billion by 2030, at a CAGR of 25.77% during the forecast period (2025-2030).

Predictive Maintenance in the Energy - Market - IMG1

Key Highlights

  • The predictive maintenance (PdM) platform has recently gained market traction. PdM solutions are integrated with new or existing machinery infrastructure to assess machine health and detect signs of impending failure. PdM integration ensures return on investment (ROI) and enables organizations to meet and exceed sustainability goals by enabling global remote machine monitoring.
  • Predictive maintenance is significantly assisting the energy industry in improving asset efficiency. Emerging technologies such as big data analytics, the Internet of Things (IoT), and cloud data storage enable industrial equipment and sensors to send condition-based data to a centralized server, making fault detection more practical and direct. The increase in uptime, lower maintenance costs, unexpected failures, and spare part inventory have propelled and flourished the market simultaneously. Furthermore, reducing repair and overhaul times is critical for the predictive maintenance market's growth.
  • The majority of energy companies are asset-intensive businesses. It takes time and effort to ensure that these resources work correctly to provide energy to consumers. Machine learning techniques, such as decision trees, can be used to optimize the operation of the equipment and, by extension, the entire system. Similarly, comparable algorithms can automate the transformation of preventative maintenance programs into predictive ones. It also allows for marginal pricing, time shifting, and asset utilization, allowing energy to be generated and delivered.
  • Predictive maintenance services and solutions send out an alert before the machine fails. Integrating business information, sensor data, and enterprise asset management (EAM) systems allow for the rapid transition from reactive to predictive maintenance services and solutions.
  • However, factors such as high installation costs, environmental concerns, rising operating costs, rising consumer expectations, and data misinterpretation leading to false requests hinder predictive maintenance market growth. Because of the growing need for better insights into usage and performance patterns to help make better decisions, these challenges increase the adoption rate of various analytics tools.
  • COVID-19 significantly impacted the market. The global economic slowdown had both positive and negative consequences for the market. For example, the drop in energy consumption was caused by the lockdowns, which hurt the market. However, due to a lack of personnel and a disrupted supply chain during the outbreak, companies operating in the industry attempted to keep the machinery running in good condition.

Predictive Maintenance in the Energy Market Trends

Solutions Segment is Anticipated to Witness Significant Growth

  • In the energy sector, there has been an increase in demand for customized industrial predictive maintenance solutions, primarily for remote monitoring operations. Big data has also played an essential role in analyzing processes, assets, and heavy equipment.
  • Several vendors, including SAP, IBM, and Microsoft, are active in the market, offering customized predictive maintenance solutions and services based on the needs of organizations. These solutions can help organizations protect their critical equipment and gain a competitive advantage in productivity.
  • Artificial intelligence (AI) and machine learning (ML) enable organizations to gain complete visibility of their operations and generate insights that can aid in the resolution of some of the industry's most disruptive challenges. Because of the volume of big data generated by energy sector companies, forward-thinking businesses invest in monitoring and predictive analytics tools that help leverage this data to its full potential. According to Gartner, 40% of new monitoring and control systems in this sector will use Internet of Things (IoT) to enable intelligent operations by the forecasted period.
  • Due to the depletion of coal resources, the power generation industry is shifting away from coal and toward solar and wind energy. Because of changing climatic conditions, most countries strictly regulate coal power plants. As electricity consumption rises, developing countries invest in advanced technologies and equipment to expand their production capacities.
  • The deployment of predictive maintenance solutions is expected to empower end users to increase productivity while minimizing failures in the power generation industry by maximizing innovative maintenance activities. The power generation industry in the Asia-Pacific developing countries requires higher efficiency, better control, and faster monitoring to reduce the likelihood of operational failure.
  • Investments in renewable energy generation, particularly wind turbines, offshore wind farms, and solar farms, have fueled the predictive maintenance solutions market growth in countries such as China and India.

North America to Occupy a Significant Market Share

  • The predictive maintenance in the energy market is dominated by North America, followed by Europe. This is due to underlying factors such as the existence of many service providers, technological advancements, and increased knowledge of preventative maintenance. The growing emphasis on research & development (R&D) for technological advances in developed economies such as Canada and the United States has fueled demand for predictive maintenance solutions throughout the region. According to the United States Energy Information Administration (US EIA), the total energy consumption rate is expected to rise by 5% between 2020 and 2040.
  • Businesses must provide energy efficiency and reduce downtime to remain profitable. This drives the data analytics market in utilities and energy. Rising environmental concerns and increased investments in sustainable energy will impact market growth.
  • Other factors driving market growth include increased investment in artificial intelligence (AI) and machine learning (ML) to reduce asset downtime and maintenance costs, adoption of the Internet of things (IoT), the need to extend the overall lifespan of machinery and equipment, declining sensor prices, advancements in sensor technology, and the evolution of high-speed networking technologies. Furthermore, regulatory compliance has been a significant driver of the Internet of things (IoT) technology adoption in the United States. The passage of the Energy Act (EA) in the United States has sped up efforts to track sustainable energy consumption.
  • The energy industry, one of the largest in the United States, is attracting significant investment. For example, according to Bloomberg New Energy Finance (BNEF), the United States is expected to invest approximately USD 7,00,000 million in renewable energy capacity over the next 20 years. These factors are expected to boost the growth of the predictive maintenance market.
  • The energy sector remains a target for deal activity as environmental, social, and governance (ESG) strategies are strengthened. General investor interest remains high, although macroeconomic pressures could pose various valuation challenges for North American energy, power, and utility companies. For instance, J.P. Morgan paid USD 7.8 billion (USD 7,800 million) for South Jersey Industries. Similarly, ArcLight Clean Energy Transition Corp paid USD 1.5 billion (USD 1,500 million) to acquire OPAL Fuels LLC. This boosts the growth of predictive maintenance in North America.

Predictive Maintenance in the Energy Industry Overview

Numerous domestic and international firms make predictive maintenance in the energy market extremely competitive. The market is moderately concentrated, with significant players expanding their market dominance through strategies such as product innovation and mergers and acquisitions. IBM Corporation, SAP SE, Robert Bosch GmbH, and Siemens AG are some of the market's major players.

In June 2022, Siemens acquired Senseye, which provides industrial companies with predictive maintenance and asset intelligence. With the acquisition of Senseye, Siemens expanded its portfolio in innovative predictive maintenance and asset intelligence. Senseye is a manufacturer and industrial company that offers outcome-oriented predictive maintenance solutions. The predictive maintenance solution from Senseye allows for a 50% reduction in unplanned machine downtime and a 30% increase in maintenance staff productivity.

In May 2022, Hitachi Ltd. launched Lumada Inspection Insights, developed by Hitachi Energy and Hitachi Vantara, to help businesses automate asset inspection and advance sustainability goals. The new approach employs artificial intelligence (AI) and machine learning (ML) to evaluate resources, hazards, and various image types to address multiple reasons for failure.

Moreover, in January 2022, IBM announced the acquisition of Envizi, a data and analytics software provider for environmental performance management. This acquisition expands IBM's growing investments in artificial intelligence (AI)-powered software, such as IBM Maximo asset management solutions, IBM Environmental Intelligence Suite, and IBM Sterling supply chain solutions, to assist organizations in creating more resilient and sustainable operations and supply chains.

Furthermore, the acquisition broadens the company's product and service offerings. With rising demand for cloud-based services, IBM Cloud's broad range of services and expertise assist the world's smarter businesses to transform their processes, assimilate new technologies and capabilities, and pivot quickly to new market opportunities.

Additional Benefits:

  • The market estimate (ME) sheet in Excel format
  • 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

  • 1.1 Study Assumptions and Market Definition
  • 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET DYNAMICS

  • 4.1 Market Overview
  • 4.2 Market Drivers
    • 4.2.1 Increasing Investments in the Energy Sector
    • 4.2.2 Increasing Adoption of Automation
  • 4.3 Market Challenges
    • 4.3.1 Higher Deployment Cost
  • 4.4 Industry Value Chain Analysis
  • 4.5 Industry Attractiveness - Porter's Five Forces Analysis
    • 4.5.1 Threat of New Entrants
    • 4.5.2 Bargaining Power of Buyers
    • 4.5.3 Bargaining Power of Suppliers
    • 4.5.4 Threat of Substitute Products
    • 4.5.5 Intensity of Competitive Rivalry
  • 4.6 Assessment of COVID-19 impact on the Market

5 MARKET SEGMENTATION

  • 5.1 By Offering
    • 5.1.1 Solutions
    • 5.1.2 Services
  • 5.2 By Deployment Model
    • 5.2.1 On-premise
    • 5.2.2 Cloud
  • 5.3 By Region
    • 5.3.1 North America
    • 5.3.2 Europe
    • 5.3.3 Asia-Pacific
    • 5.3.4 Latin America
    • 5.3.5 Middle East & Africa

6 COMPETITIVE LANDSCAPE

  • 6.1 Company Profiles
    • 6.1.1 IBM Corporation
    • 6.1.2 SAP SE
    • 6.1.3 Siemens AG
    • 6.1.4 Intel Corporation
    • 6.1.5 Robert Bosch GmbH
    • 6.1.6 Accenture PLC
    • 6.1.7 ABB Ltd
    • 6.1.8 Schneider Electric
    • 6.1.9 Banner Engineering Corp.
    • 6.1.10 GE Automation & Control

7 INVESTMENT ANALYSIS

8 MARKET OPPORTUNITIES AND FUTURE TRENDS