세계의 예지보전 및 자산 성과 시장(2023-2028년)

Predictive Maintenance & Asset Performance Market Report 2023-2028

발행일: | 리서치사: IoT Analytics GmbH | 페이지 정보: 영문 295 Pages | 배송안내 : 1-2일 (영업일 기준)

※ 본 상품은 영문 자료로 한글과 영문 목차에 불일치하는 내용이 있을 경우 영문을 우선합니다. 정확한 검토를 위해 영문 목차를 참고해주시기 바랍니다.

세계 예지보전 및 자산 성과 시장을 조사했으며, 예지보전(PdM), 상태기반보전(CbM), 자산 성과 관리(APM)의 정의, 기술 및 프로세스 도입 개요, 도입 촉진요인, 시장 규모 추이 및 예측, 각 부문별/지역별 상세 분석, 경쟁 구도, 주요 동향 및 사례 등의 정보를 전해드립니다. 주목할 만한 동향과 과제, 사례 연구 등을 정리하여 전해드립니다.


예지보전(PdM), 상태기반보전(CbM), 자산성과관리(APM) 시장 분석 및 예측:

  • 기술 스택별(커넥티비티, 하드웨어, 서비스, 소프트웨어)
  • 호스팅 유형별(프라이빗 클라우드/온프레미스/퍼블릭 클라우드)
  • 부문별(1차산업, 의료, 운송, 건설&부동산, 기타)
  • 산업별(개별 제조, 하이브리드 제조, 공정 제조)
  • 지역별(사하라 이남 아프리카, 중동&북아프리카, 북아프리카, 남아시아, 라틴아메리카&카리브해, 북미, 동아시아&태평양, 유럽&중앙아시아)
  • 국가별(싱가포르, 호주, 한국, 일본, 중국, 벨기에, 폴란드, 네덜란드, 스위스, 스페인, 이탈리아, 프랑스, 영국, 독일, 캐나다, 미국, 기타)

*APM 내역은 포함되지 않았습니다.

소개된 기업

  • ABB
  • AWS
  • Arundo
  • AspenTech
  • Augury
  • Baker Hughes
  • Cognite
  • Falkonry
  • GE
  • I-care
  • IBM
  • MachineMetrics
  • MathWorks
  • Microsoft
  • Novity
  • Rockwell Automation
  • SKF
  • Siemens
  • Telit Cinterion


제1장 주요 요약

제2장 서론

  • 예지보전을 검토하는 3개 방법
  • PdM, CbM, APM 정의
  • 자산 성과 관리 주요 컴포넌트
  • PdM과 기타 접근 비교
  • PdM의 전형적인 유형 자산/용도 영역
  • PdM의 주요 이점

제3장 기술 개요

  • PdM 도입 프로세스
  • 상세 : PdM 솔루션 구입과 구축
  • 상세 : 센싱 기술
  • 상세 : PdM 데이터 분석
  • 상세 : PdM 소프트웨어
  • 상세 : APM 소프트웨어 동작

제4장 시장 규모·전망

  • 세계의 스마트 메인터넌스 시장 개요
  • 세계의 PdM 및 CbM 시장
    • 세계의 PdM 및 CbM 시장 : 자산 및 센서 유형별
    • 세계의 PdM 및 CbM 시장 : 기술 스택별
    • 세계의 PdM 및 CbM 시장 : 호스팅 유형별
    • 세계의 PdM 및 CbM 시장 : 부문별
    • 세계의 PdM 및 CbM 시장 : 지역별
  • 세계의 APM 시장

제5장 경쟁 구도

  • 기업 상황
  • 최대 PdM 벤더 10개사
  • 최대 CbM 벤더 10개사
  • 상세 : PdM 기업 개요
  • PdM 경쟁 구도에 영향을 미치는 주목 최근 뉴스
  • PdM 스타트업
  • 인수합병(M&A) 활동에서의 머신 비전 특허
  • 특허 분석

제6장 사례 연구

제7장 최종사용자 통찰

  • 디지털화 조사
  • 유지보수 및 신뢰성 조사

제8장 동향과 과제

제9장 조사 방법과 시장 정의

제10장 IoT Analytics에 대해


LSH 23.11.10

A 295-page report detailing the market for next-generation maintenance, including detailed definitions, adoption drivers, market projections, competitive landscape, end-user insights, notable trends, and case studies.

The “Predictive Maintenance Market Report 2023-2028” constitutes the 4th update of IoT Analytics' ongoing coverage of predictive maintenance and is part of IoT Analytics' ongoing coverage of industrial and software/analytics topics. The content presented in this report is based on a compilation of primary research, including surveys and interviews with 35+ industry experts from predictive maintenance vendors and end users conducted between March and October 2023.

The report encompasses a holistic overview of the current state of the predictive maintenance market and adjacent markets such as condition-based maintenance and asset performance management, including market projections, factors driving adoption, competitive landscape, technology and process implementation overview, notable trends and challenges, and insightful case studies.

The primary objective of this document is to provide our readers with a comprehensive understanding of the current predictive maintenance market landscape, offering in-depth analysis, market sizing, and valuable insights to facilitate informed decision-making and strategic planning.


What is predictive maintenance (PdM)?

  • A set of techniques to accurately monitor the current condition of machines or any type of industrial equipment
  • ... using either on-premises or cloud analytics solutions
  • ... with the goal of predicting upcoming machine failure by using statistical methods and supervised/unsupervised ML.

Among other benefits, this approach promises cost savings over routine or time-based preventive maintenance because tasks are performed only when warranted.

What is asset performance management (APM)?

  • A strategic equipment management approach that helps optimize the performance and maintenance efficiency of individual assets and of entire plants or fleets.

APM aims to improve the efficiency, availability, reliability, maintainability, and overall life cycle value of assets. This concept includes elements of CbM and PdM but goes beyond them.

What is condition-based maintenance (CbM)?

  • A maintenance approach that monitors the actual condition of an asset to determine what maintenance needs to be done.

It does not involve further analytics, such as predicting the remaining useful life (RUL) or the overall health of the machine.


The “ Predictive Maintenance Market Report 2023-2028” analyzes the predictive maintenance (PdM), condition-based maintenance (CbM), and asset performance management (APM)* market from 2021 to 2028. It provides detailed data and forecasts for the market size:

  • by tech stack (connectivity, hardware, services, software)
  • by hosting type (Private cloud/on-premises, public cloud)
  • by segment (primary sector, health care, transportation, contruction & real estate, other, hybrid manufacturing, process manufacturing, discrete manufacturing)
  • by industry (discrete manufacturing, hybrid manufacturing, process manufacturing)
  • by region (Sub-Saharan Africa, Middle East & North Africa, South Asia, Latin America & Caribbean, North America, East Asia & Pacific, Europe & Central Asia)
  • by country (East Asia & Pacific: Singapore, Australia, South Korea, Japan, China, Other; Europe and Central Asia: Belgium, Poland, Netherlands, Switzerland, Spain, Italy, France, United Kingdom, Germany; North America: Canada, United States)

*no breakdowns included for APM.


Questions answered:

  • What is predictive maintenance, condition-based maintenance, and asset performance management?
  • What role does predictive maintenance play in the overall maintenance space?
  • What are the key features, functionalities, and components of predictive maintenance solutions? What are the key components of asset performance management solutions?
  • What is the current market size and projected growth of the predictive maintenance market?
  • How does the predictive maintenance market split by tech stack, segment, hosting type, asset type, sensor type and region?
  • What does the competitive landscape for predictive maintenance look like, who are the key players, and what is their market share?
  • What are the emerging predictive maintenance trends and challenges?
  • What are some successful case studies demonstrating the benefits of predictive maintenance in various applications?

Companies mentioned:

A selection of companies mentioned in the report.

  • ABB
  • AWS
  • Arundo
  • AspenTech
  • Augury
  • Baker Hughes
  • Cognite
  • Falkonry
  • GE
  • I-care
  • IBM
  • MachineMetrics
  • MathWorks
  • Microsoft
  • Novity
  • Rockwell Automation
  • SKF
  • Siemens
  • Telit Cinterion

Table of Contents

1. Executive Summary

2. Introduction

  • 2.1. Three ways to look at predictive maintenance
  • 2.2. Definition of PdM, CbM, and APM
  • 2.3. Asset performance management key components
  • 2.4. Comparison of PdM with other approaches
  • 2.5. PdM typical types of assets/application areas
  • 2.6. PdM key benefits

3. Technology Overview

  • 3.1. PdM implementation process
  • 3.2. Deep dive: buying vs. building the PdM solution
  • 3.3. Deep dive: sensing techniques
  • 3.4. Deep dive: PdM data analysis
  • 3.5. Deep dive: PdM software
  • 3.6. Deep dive: APM software in action

4. Market size & outlook

  • 4.1. Overview of the global smart maintenance market
  • 4.2. Global PdM and CbM Market
    • 4.2.1. Global PdM and CbM Market in 2022, by Asset and Sensor Type
    • 4.2.2. Global PdM and CbM Market, by Tech Stack
    • 4.2.3. Global PdM and CbM Market, by Hosting Type
    • 4.2.4. Global PdM and CbM Market, by Segment
    • 4.2.5. Global PdM and CbM Market, by Region
      • Market regional deep dive: East & Pacific Asia, Europe & Central Asia, and North Amercia
  • 4.3. Global APM Market

5. Competitive landscape

  • 5.1. Company landscape
  • 5.2. The 10 largest PdM vendors
  • 5.3. The 10 largest CbM vendors
  • 5.4. Deep dive: top five PdM company profiles
  • 5.5. Notable recent news with effect on the PdM competitive landscape
  • 5.6. PdM start-ups
  • 5.7. Mergers and acquisitions (M&A) activity machine vision patents
  • 5.8. Patent analysis

6. Case Studies

  • 6.1. Case studies overview
  • 6.2. Case studies

7. End User Insights

  • 7.1. Digitization Survey
  • 7.2. Maintenance and Reliability Survey

8. Trends & Challenges

  • 8.1. Trends
  • 8.2. Challenges

9. Methodology and market definitions

10. About IoT Analytics


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