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能源領域預測性維護市場規模和份額分析 - 增長趨勢和預測（2023-2028）

Predictive Maintenance In The Energy Market Size & Share Analysis - Growth Trends & Forecasts (2023 - 2028)
出版日期: | 出版商: Mordor Intelligence | 英文 100 Pages | 商品交期: 2-3個工作天內

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簡介目錄

能源領域的預測性維護市場規模預計將從 2023 年的 14.2 億美元增長到 2028 年的 44.7 億美元，預測期內（2023-2028 年）複合年增長率為 25.77%。

主要亮點

  • 最近，預測維護 (PdM) 平台已成為市場驅動力。 PdM 解決方案與新的或現有的機械基礎設施集成，以評估機器健康狀況並檢測即將發生故障的跡象。 PdM 集成可確保投資回報 (ROI) 並實現全球遠程機器監控，使公司能夠滿足並超越其可持續發展目標。
  • 預測性維護極大地幫助能源行業提高資產效率。 大數據分析、物聯網（IoT）和雲數據存儲等新技術使工業設備和傳感器能夠將基於狀態的數據發送到集中式服務器，使故障檢測變得更加實用和直接。 正常運行時間的增加、維護成本的降低、計劃外故障和備件庫存同時推動和繁榮了市場。 此外，減少維修和大修時間對於預測性維護市場的增長至關重要。
  • 大多數能源公司都是資產密集型企業。 讓這些資源正常工作以向消費者提供能源需要時間和精力。 決策樹等機器學習技術可用於優化設備乃至整個系統的運行。 同樣，類似的算法可以將預防性維護程序自動化為預測性維護程序。 它還支持邊際定價、時移和資產利用，從而實現能源生產和供應。
  • 預測性維護服務和解決方案可在機器出現故障之前發出警報。 集成業務信息、傳感器數據和企業資產管理 (EAM) 系統可實現從被動式維護服務和解決方案快速轉變為預測性維護服務和解決方案。
  • 但是，安裝成本高、環境問題、運營成本上升、消費者期望上升以及數據誤解導致虛假需求等因素正在阻礙預測性維護市場的增長。 這些挑戰正在推動各種分析工具的採用，因為更好地洞察使用情況和性能模式以做出更好決策的需求不斷增加。
  • 新冠肺炎 (COVID-19) 對市場產生了重大影響。 全球經濟放緩對市場產生了積極和消極的影響。 例如，封鎖造成的能源消耗下降衝擊了市場。 然而，持續的人員短缺和供應鏈中斷迫使該行業的公司保持機器的良好運行狀態。

能源市場預測性維護的趨勢

解決方案領域預計將實現大幅增長

  • 能源行業對定制工業預測性維護解決方案（主要用於遠程監控操作）的需求不斷增加。 大數據在分析流程、資產和重型設備方面也發揮著不可或缺的作用。
  • SAP、IBM 和 Microsoft 等多家供應商活躍在這個市場中，根據組織的需求提供定制的預測性維護解決方案和服務。 這些解決方案可幫助組織保護其關鍵資產並獲得生產力方面的競爭優勢。
  • 人工智能 (AI) 和機器學習 (ML) 使組織能夠全面了解其運營情況並獲得見解，從而幫助解決行業中一些最具顛覆性的挑戰。 能源行業公司產生的大數據量如此之大，以至於有遠見的公司正在投資監控和預測分析工具，以充分利用這些數據。 據 Gartner 稱，到預測期內，該領域 40% 的新型監控和控制系統將利用物聯網 (IoT) 實現智能操作。
  • 由於煤炭資源枯竭，發電行業正在從煤炭轉向太陽能和風能。 由於氣候條件的變化，大多數國家都對燃煤電廠進行了嚴格監管。 隨著電力消耗的增加，發展中國家正在投資先進技術和設備以擴大產能。
  • 預測性維護解決方案的引入預計將使最終用戶能夠通過最大化創新維護活動來最大限度地減少故障並提高發電行業的生產力。 亞太新興國家的發電行業需要更高的效率、更好的控制和更快的監控，以減少運營故障的可能性。
  • 對可再生能源發電（尤其是風力渦輪機、海上風電場和太陽能發電廠）的投資正在推動中國和印度等國家預測性維護解決方案市場的增長。
能源市場的預測維護-IMG1

北美佔據主要市場份額

  • 能源領域的預測性維護市場以北美為主，其次是歐洲。 這是由於許多服務提供商的存在、技術進步以及預防性維護知識不斷增加等根本因素造成的。 加拿大和美國等新興經濟體越來越注重技術進步的研發 (R&D)，推動了該地區對預測性維護解決方案的需求。 據美國能源信息署（US EIA）預計，2020年至2040年間，能源消耗總量將增長5%。
  • 企業必須提高能源效率並減少停機時間才能保持盈利。 這推動了公用事業和能源行業的數據分析市場。 日益增長的環境問題和對可持續能源投資的增加預計將影響市場增長。
  • 推動市場增長的其他因素包括增加對人工智能(AI) 和機器學習(ML) 的投資以減少資產停機時間和維護成本、物聯網(IoT) 的採用以及延長機械和設備整體使用壽命的需要、傳感器價格下降、傳感器技術進步以及高速網絡技術進步。 此外，監管合規性一直是美國採用物聯網 (IoT) 技術的關鍵驅動力。 美國能源法案（EA）的通過加速了追蹤可持續能源消耗的努力。
  • 美國最大的能源行業之一吸引了大量投資。 例如，據彭博新能源財經（BNEF）報導，美國預計未來20年將在可再生能源產能上投資約7000億美元。 預計這些因素將推動預測性維護市場的增長。
  • 隨著環境、社會和治理 (ESG) 戰略的加強，能源行業繼續成為交易活動的目標。 儘管公眾投資者的興趣仍然很高，但宏觀經濟壓力可能會給北美的能源、電力和公用事業公司帶來一系列估值挑戰。
能源市場的預測維護-IMG2

能源行業預測性維護概述

由於國內外公司數量眾多，能源市場預測性維護的競爭異常激烈。 市場集中度中等，主要企業通過產品創新、併購等策略擴大市場勢力。 IBM公司、SAP SE、羅伯特博世有限公司、西門子公司是市場的主要參與者。

2022 年 6 月，西門子收購了 Senseye，這是一家為工業公司提供預測性維護和資產智能的提供商。 通過收購 Senseye，西門子擴大了其在創新預測維護和資產智能領域的產品組合。 Senseye 是一家以性能為導向的預測性維護解決方案製造商和工業設備製造商。 SenseEye 的預測性維護解決方案可將計劃外機器停機時間減少 50%，並將維護人員的工作效率提高 30%。

2022 年 5 月，日立將推出由 Hitachi Energy 和 Hitachi Vantara 開發的 Lumada Inspection Insights，幫助企業實現資產檢查自動化並推進其可持續發展目標。 這種新方法採用人工智能 (AI) 和機器學習 (ML) 來評估資源、危險和各種圖像類型，以解決失敗的多種原因。

此外，2022 年 1 月，IBM 宣布收購環境績效管理數據和分析軟件提供商 Envizi。 此次收購擴大了IBM 對人工智能(AI) 支持的軟件不斷增長的投資，包括IBM Maximo 資產管理解決方案、IBM 環境智能套件和IBM Sterling 供應鏈解決方案，以幫助組織變得更具彈性。我們幫助建立可持續運營和供應鏈在

此外，此次收購還擴大了公司的產品和服務範圍。 隨著對基於雲的服務的需求不斷增長，IBM Cloud 廣泛的服務和專業知識使世界上更智能的企業能夠轉變流程、吸收新技術和功能，並快速抓住新的市場機會。我們支持。

其他好處：

  • Excel 格式的市場預測 (ME) 表
  • 3 個月的分析師支持

目錄

第一章簡介

  • 研究假設和市場定義
  • 調查範圍

第二章研究方法

第 3 章執行摘要

第 4 章市場動態

  • 市場概覽
  • 市場驅動因素
    • 增加能源領域投資
    • 增加自動化實施
  • 市場挑戰
    • 實施成本高
  • 工業價值鏈分析
  • 行業吸引力 - 波特五力分析
    • 新進入者的威脅
    • 買方的議價能力
    • 供應商的議價能力
    • 替代品的威脅
    • 競爭公司之間敵對關係的強度
  • 評估新冠肺炎 (COVID-19) 對市場的影響

第五章市場細分

  • 按產品
    • 解決方案
    • 按服務
  • 按部署模型
    • 本地
    • 雲
  • 按地區
    • 北美
    • 歐洲
    • 亞太地區
    • 拉丁美洲
    • 中東/非洲

第六章競爭態勢

  • 公司簡介
    • 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 投資分析

第8章 市場機會與將來動向

簡介目錄
Product Code: 58744

The Predictive Maintenance in the Energy Market size is expected to grow from USD 1.42 billion in 2023 to USD 4.47 billion by 2028, at a CAGR of 25.77% during the forecast period (2023-2028).

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.
Predictive Maintenance In The Energy Market - IMG1

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 700 billion 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 7.8 billion) for South Jersey Industries. Similarly, ArcLight Clean Energy Transition Corp paid USD 1.5 billion (USD 1.5 billion) to acquire OPAL Fuels LLC. This boosts the growth of predictive maintenance in North America.
Predictive Maintenance In The Energy Market - IMG2

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