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市场调查报告书
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能源领域的预测性维护 - 市场占有率分析、产业趋势与统计、成长预测（2024 - 2029 年）

Predictive Maintenance in the Energy - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2024 - 2029)
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

能源市场预测维护规模预计到 2024 年为 17.9 亿美元，预计到 2029 年将达到 56.2 亿美元，预测期内（2024-2029 年）CAGR为 25.77%。

能源市场中的预测性维护

主要亮点

  • 预测性维护 (PdM) 平台最近获得了市场关注。 PdM 解决方案与新的或​​现有的机械基础设施集成，以评估机器健康状况并检测即将发生故障的迹象。 PdM 整合可确保投资回报 (ROI)，并使组织能够透过启用全球远端机器监控来实现并超越永续发展目标。
  • 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兴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伺服器，使故障检测更加实用和直接。正常运作时间的增加、维护成本的降低、意外故障和备件库存同时推动和繁荣了市场。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以向消费者提供能源需要时间和精力。机器学习技术（例如决策树）可用于优化设备的运行，进而优化整个系统的运作。同样，类似的演算法可以自动将预防性维护计划转变为预测性维护计划。它还允许边际定价、时移和资产利用，从而允许产生和交付能源。
  • 预测性维护服务和解决方案在机器故障之前发出警报。整合业务资讯、感测器资料和企业资产管理 (EAM) 系统可实现从反应性维护服务和解决方案快速过渡到预测性维护服务和解决方案。
  • 然而，高安装成本、环境问题、营运成本上升、消费者期望上升以及导致错误请求的资料误解等因素阻碍了预测性维护市场的成长。由于越来越需要更好地洞察使用情况和性能模式以帮助做出更好的决策，这些挑战提高了各种分析工具的采用率。
  • 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的影响，也有负面的影响。例如，能源消耗下降是由于封锁造成的，这损害了市场。然而，由于疫情期间人员短缺和供应链中断，该行业的企业试图保持机器的良好运作状态。

能源市场趋势中的预测性维护

解决方案领域预计将显着成长

  • 在能源领域，对客製化工业预测性维护解决方案的需求不断增加，主要用于远端监控操作。大资料在分析流程、资产和重型设备方面也发挥了重要作用。
  • SAP、IBM 和 Microsoft 等多家供应商活跃在市场中，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。这些解决方案可以帮助组织保护其关键设备并在生产力方面获得竞争优势。
  • 人工智慧 (AI) 和机器学习 (ML) 使组织能够全面了解其营运并产生见解，帮助解决行业中一些最具破坏性的挑战。由于能源产业公司产生大量巨量资料，具有前瞻性思维的企业投资于监控和预测分析工具，以帮助充分利用这些资料的潜力。据 Gartner 称，到预测期内，该行业 40% 的新型监控系统将使用物联网 (IoT) 来实现智慧操作。
  • 由于煤炭资源的枯竭，发电业正从煤炭转向太阳能和风能。由于气候条件的变化，大多数国家对燃煤电厂进行严格监管。随着电力消耗的增加，发展中国家投资先进技术和设备以扩大生产能力。
  • 预测性维护解决方案的部署预计将使最终用户能够提高生产力，同时透过最大化创新维护活动来最大限度地减少发电行业的故障。亚太发展中国家的发电产业需要更高的效率、更好的控制和更快的监控，以减少营运故障的可能性。
  • 对再生能源发电（特别是风力涡轮机、离岸风电场和太阳能发电场）的投资推动了中国和印度等国家预测性维护解决方案市场的成长。

北美将占据重要市场份额

  • 能源市场的预测性维护以北美为主，其次是欧洲。这是由于许多服务提供者的存在、技术进步以及预防性维护知识的增加等根本因素所造成的。加拿大和美国等已开发经济体日益重视技术进步的研发 (R&D)，刺激了整个地区对预测性维护解决方案的需求。根据美国能源资讯署（US EIA）预计，2020年至2040年间，能源消耗总量将成长5%。
  • 企业必须提高能源效率并减少停机时间才能保持获利。这推动了公用事业和能源领域的资料分析市场。日益严重的环境问题和对永续能源投资的增加将影响市场成长。
  • 其他推动市场成长的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、物联网（IoT）的采用、延长机械和设备整体使用寿命的需要、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，监管合规性一直是美国采用物联网 (IoT) 技术的重要动力。美国能源法案（EA）的通过加快了追踪永续能源消耗的脚步。
  • 能源产业是美国最大的产业之一，正在吸引大量投资。例如，根据彭博新能源财经（BNEF）报道，美国预计未来20年将在再生能源产能上投资约7,000亿美元。预计这些因素将推动预测性维护市场的成长。
  • 随着环境、社会和治理 (ESG) 策略的加强，能源产业仍然是交易活动的目标。儘管宏观经济压力可能会给北美能源、电力和公用事业公司带来各种估值挑战，但整体投资者兴趣仍然很高。例如，摩根大通斥资 78 亿美元（78 亿美元）收购了 South Jersey Industries。同样，ArcLight Clean Energy Transition Corp 斥资 15 亿美元（15 亿美元）收购了 OPAL Fuels LLC。这促进了北美预测性维护的成长。

能源产业的预测性维护概述

许多国内和国际公司使预测性维护在能源市场上极具竞争力。市场集中度中等，重要企业透过产品创新、併购等策略扩大市场主导地位。 IBM 公司、SAP SE、罗伯特博世有限公司和西门子公司是该市场的一些主要参与者。

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，西门子扩大了其在创新预测维护和资产智慧领域的产品组合。 Senseye 是一家提供以结果为导向的预测性维护解决方案的製造商和工业公司。 Senseye 的预测性维护解决方案可将非计划机器停机时间减少 50%，并将维修人员的工作效率提高 30%。

2022 年 5 月，日立有限公司推出了由 Hitachi Energy 和 Hitachi 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发展目标。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，以解决失败的多种原因。

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 对人工智慧(AI) 支援的软体不断增长的投资，例如IBM Maximo 资产管理解决方案、IBM Environmental Intelligence Suite 和IBM Sterling 供应链解决方案，以帮助组织创建更具弹性和可持续的营运和供应链。

此外，此次收购扩大了公司的产品和服务范围。随着对云端为基础的服务的需求不断增长，IBM Cloud 广泛的服务和专业知识可协助全球智慧企业转变流程、吸收新技术和功能，并快速抓住新的市场机会。

额外的好处：

  • Excel 格式的市场估算 (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 2 章：研究方法

第 3 章：执行摘要

第 4 章：市场动态

  • 市场概况
  • 市场驱动因素
    • 增加能源领域投资
    • 越来越多地采用自动化
  • 市场挑战
    • 部署成本较高
  • 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
    • 替代产品的威胁
    • 竞争激烈程度
  • 评估 COVID-19 对市场的影响

第 5 章：市场细分

  • 透过提供
    • 解决方案
    • 服务
  • 按部署模型
    • 本地部署
    • 云
  • 按地区
    • 北美洲
    • 欧洲
    • 亚太
    • 拉丁美洲
    • 中东和非洲

第 6 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 7 章：投资分析

第 8 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 1.79 billion in 2024, and is expected to reach USD 5.62 billion by 2029, growing at a CAGR of 25.77% during the forecast period (2024-2029).

Predictive Maintenance in the Energy - Market

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