AI-Powered Predictive Maintenance Systems Market - Global Industry Size, Share, Trends, Opportunity, and Forecast, By Component, By Deployment, By Technology, By Application, By Region & Competition, 2020-2030F
상품코드:1763867
리서치사:TechSci Research
발행일:2025년 06월
페이지 정보:영문 185 Pages
라이선스 & 가격 (부가세 별도)
ㅁ Add-on 가능: 고객의 요청에 따라 일정한 범위 내에서 Customization이 가능합니다. 자세한 사항은 문의해 주시기 바랍니다.
한글목차
세계의 AI 활용 예지보전 시스템 시장은 2024년에는 7억 7,303만 달러로 평가되었고, 2030년까지는 15억 2,887만 달러에 이를 것으로 예상되며, 예측 기간 중 CAGR은 12.04%를 나타낼 것으로 전망됩니다.
이 시장에는 센서, 기계, 제어 시스템의 데이터를 분석하고 장비 고장을 사전 예측하는 AI 주도형 솔루션이 포함되어 있습니다. 필드에서 널리 사용되는 AI 활용 예지보전의 도입은 산업 자동화, IoT 통합, 실시간 분석의 보급으로 가속화되고 있습니다. 또한 도입은 더욱 확장 가능해지고 중견기업에서도 이용하기 쉬워지고 있습니다.
시장 개요
예측 기간
2026-2030년
시장 규모 : 2024년
7억 7,303만 달러
시장 규모 : 2030년
15억 2,887만 달러
CAGR : 2025-2030년
12.04%
급성장 부문
컨디션 모니터링
최대 시장
북미
주요 시장 성장 촉진요인
산업 자동화 및 스마트 제조 급증
주요 시장 과제
레거시 시스템 간의 데이터 사일로와 통합의 복잡성
주요 시장 동향
실시간 자산 시뮬레이션을 위한 디지털 트윈 통합
목차
제1장 솔루션 개요
시장의 정의
시장 범위
대상 시장
조사 대상 연도
주요 시장 세분화
제2장 조사 방법
제3장 주요 요약
제4장 고객의 목소리
제5장 세계의 AI 활용 예지보전 시스템 시장 전망
시장 규모와 예측
금액별
시장 점유율·예측
구성 요소별(하드웨어, 소프트웨어, 서비스)
배포별(온프레미스, 클라우드 기반, 하이브리드)
기술별(머신러닝, 딥러닝, 자연 언어 처리, 컴퓨터 비전, 엣지 AI)
용도별(상태 모니터링, 장애 감지 및 진단, 자산 성능 관리, 에너지 소비 최적화, 기타)
지역별(북미, 유럽, 남미, 중동 및 아프리카, 아시아태평양)
기업별(2024년)
시장 맵
제6장 북미의 AI 활용 예지보전 시스템 시장 전망
시장 규모와 예측
시장 점유율·예측
북미 : 국가별 분석
미국
캐나다
멕시코
제7장 유럽의 AI 활용 예지보전 시스템 시장 전망
시장 규모와 예측
시장 점유율·예측
유럽 : 국가별 분석
독일
프랑스
영국
이탈리아
스페인
제8장 아시아태평양의 AI 활용 예지보전 시스템 시장 전망
시장 규모와 예측
시장 점유율·예측
아시아태평양 : 국가별 분석
중국
인도
일본
한국
호주
제9장 중동 및 아프리카의 AI 활용 예지보전 시스템 시장 전망
시장 규모와 예측
시장 점유율·예측
중동 및 아프리카 : 국가별 분석
사우디아라비아
아랍에미리트(UAE)
남아프리카
제10장 남미의 AI 활용 예지보전 시스템 시장 전망
시장 규모와 예측
시장 점유율·예측
남미 : 국가별 분석
브라질
콜롬비아
아르헨티나
제11장 시장 역학
성장 촉진요인
과제
제12장 시장 동향과 발전
합병과 인수
제품 출시
최근 동향
제13장 기업 프로파일
IBM Corporation
Microsoft Corporation
SAP SE
Siemens AG
General Electric Company
PTC Inc.
Schneider Electric SE
ABB Ltd.
제14장 전략적 제안
제15장 기업 소개와 면책사항
KTH
영문 목차
영문목차
The Global AI-Powered Predictive Maintenance Systems Market was valued at USD 773.03 million in 2024 and is projected to reach USD 1528.87 million by 2030, growing at a CAGR of 12.04% during the forecast period. This market encompasses AI-driven solutions that analyze data from sensors, machinery, and control systems to predict equipment failures before they happen. Unlike traditional reactive or scheduled maintenance, these systems offer a proactive, real-time approach that enhances efficiency, minimizes downtime, and extends asset lifespan. Widely used across sectors such as manufacturing, energy, transportation, and healthcare, the adoption of AI-powered predictive maintenance is accelerating due to the proliferation of industrial automation, IoT integration, and real-time analytics. With the evolution of cloud computing and edge AI, deployment has become more scalable and accessible, even for mid-sized enterprises. These factors, combined with the increasing focus on asset performance and operational continuity, are driving the rapid growth of this market.
Market Overview
Forecast Period
2026-2030
Market Size 2024
USD 773.03 Million
Market Size 2030
USD 1528.87 Million
CAGR 2025-2030
12.04%
Fastest Growing Segment
Condition Monitoring
Largest Market
North America
Key Market Drivers
Surge in Industrial Automation and Smart Manufacturing
The expansion of Industry 4.0 has led to a widespread implementation of connected systems and automation in sectors like manufacturing, oil & gas, and logistics. As operational uptime becomes a critical success factor, AI-powered predictive maintenance systems are enabling industries to proactively manage equipment performance and minimize unplanned outages. Smart factories are embedding sensors and AI algorithms to capture and interpret real-time machine data, facilitating early anomaly detection and effective maintenance scheduling. This capability not only ensures continuous operation of complex equipment but also improves planning and resource allocation. As enterprises become increasingly reliant on data-driven decision-making, predictive maintenance is emerging as a core strategy for sustaining asset performance. According to the International Federation of Robotics (IFR), global industrial robot installations reached 553,052 units in 2022, underscoring the growing demand for predictive maintenance tools to support automated infrastructure worldwide.
Key Market Challenges
Data Silos and Integration Complexity Across Legacy Systems
A significant obstacle in deploying AI-powered predictive maintenance systems lies in the difficulty of integrating data from legacy equipment and outdated enterprise infrastructures. Many industrial operations still depend on machinery that lacks modern sensors or standardized data protocols, which complicates the process of collecting consistent, high-quality machine data. These fragmented data environments hinder the performance of AI models by limiting access to comprehensive operational insights needed for accurate failure prediction. Without integrated, real-time data streams, predictive algorithms struggle to detect meaningful patterns or anomalies, diminishing the effectiveness and reliability of the system. Consequently, this challenge can limit ROI and hinder large-scale adoption, especially in sectors with extensive legacy infrastructure.
Key Market Trends
Integration of Digital Twins for Real-Time Asset Simulation
One of the emerging trends in the AI-powered predictive maintenance systems market is the incorporation of digital twin technology. A digital twin serves as a dynamic, virtual replica of a physical asset, continuously updated using sensor data and AI analytics to simulate real-time performance and conditions. This integration enhances predictive accuracy by allowing companies to virtually test operating scenarios and detect potential faults before they affect physical systems. Industries such as aerospace, automotive, and energy are increasingly leveraging digital twins to improve asset lifecycle management, perform remote monitoring, and support faster diagnostics. As AI models become more refined, digital twins are playing a vital role in delivering context-rich, actionable insights. They are also valuable for training maintenance personnel, evaluating failure risks, and ensuring business continuity, making them a foundational tool in the predictive maintenance ecosystem.
Key Market Players
IBM Corporation
Microsoft Corporation
SAP SE
Siemens AG
General Electric Company
PTC Inc.
Schneider Electric SE
ABB Ltd.
Report Scope:
In this report, the Global AI-Powered Predictive Maintenance Systems Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:
AI-Powered Predictive Maintenance Systems Market, By Component:
Hardware
Software
Services
AI-Powered Predictive Maintenance Systems Market, By Deployment:
On-Premises
Cloud-Based
Hybrid
AI-Powered Predictive Maintenance Systems Market, By Technology:
Machine Learning
Deep Learning
Natural Language Processing
Computer Vision
Edge AI
AI-Powered Predictive Maintenance Systems Market, By Application:
Condition Monitoring
Failure Detection & Diagnosis
Asset Performance Management
Energy Consumption Optimization
Others
AI-Powered Predictive Maintenance Systems Market, By Region:
North America
United States
Canada
Mexico
Europe
Germany
France
United Kingdom
Italy
Spain
Asia Pacific
China
India
Japan
South Korea
Australia
Middle East & Africa
Saudi Arabia
UAE
South Africa
South America
Brazil
Colombia
Argentina
Competitive Landscape
Company Profiles: Detailed analysis of the major companies present in the Global AI-Powered Predictive Maintenance Systems Market.
Available Customizations:
Global AI-Powered Predictive Maintenance Systems Market report with the given market data, TechSci Research offers customizations according to a company's specific needs. The following customization options are available for the report:
Company Information
Detailed analysis and profiling of additional market players (up to five).
Table of Contents
1. Solution Overview
1.1. Market Definition
1.2. Scope of the Market
1.2.1. Markets Covered
1.2.2. Years Considered for Study
1.2.3. Key Market Segmentations
2. Research Methodology
2.1. Objective of the Study
2.2. Baseline Methodology
2.3. Key Industry Partners
2.4. Major Association and Secondary Sources
2.5. Forecasting Methodology
2.6. Data Triangulation & Validation
2.7. Assumptions and Limitations
3. Executive Summary
3.1. Overview of the Market
3.2. Overview of Key Market Segmentations
3.3. Overview of Key Market Players
3.4. Overview of Key Regions/Countries
3.5. Overview of Market Drivers, Challenges, and Trends
4. Voice of Customer
5. Global AI-Powered Predictive Maintenance Systems Market Outlook
5.1. Market Size & Forecast
5.1.1. By Value
5.2. Market Share & Forecast
5.2.1. By Component (Hardware, Software, Services)
5.2.2. By Deployment (On-Premises, Cloud-Based, Hybrid)
5.2.3. By Technology (Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Edge AI)
5.2.4. By Application (Condition Monitoring, Failure Detection & Diagnosis, Asset Performance Management, Energy Consumption Optimization, Others)
5.2.5. By Region (North America, Europe, South America, Middle East & Africa, Asia Pacific)
5.3. By Company (2024)
5.4. Market Map
6. North America AI-Powered Predictive Maintenance Systems Market Outlook
6.1. Market Size & Forecast
6.1.1. By Value
6.2. Market Share & Forecast
6.2.1. By Component
6.2.2. By Deployment
6.2.3. By Technology
6.2.4. By Application
6.2.5. By Country
6.3. North America: Country Analysis
6.3.1. United States AI-Powered Predictive Maintenance Systems Market Outlook
6.3.1.1. Market Size & Forecast
6.3.1.1.1. By Value
6.3.1.2. Market Share & Forecast
6.3.1.2.1. By Component
6.3.1.2.2. By Deployment
6.3.1.2.3. By Technology
6.3.1.2.4. By Application
6.3.2. Canada AI-Powered Predictive Maintenance Systems Market Outlook
6.3.2.1. Market Size & Forecast
6.3.2.1.1. By Value
6.3.2.2. Market Share & Forecast
6.3.2.2.1. By Component
6.3.2.2.2. By Deployment
6.3.2.2.3. By Technology
6.3.2.2.4. By Application
6.3.3. Mexico AI-Powered Predictive Maintenance Systems Market Outlook
6.3.3.1. Market Size & Forecast
6.3.3.1.1. By Value
6.3.3.2. Market Share & Forecast
6.3.3.2.1. By Component
6.3.3.2.2. By Deployment
6.3.3.2.3. By Technology
6.3.3.2.4. By Application
7. Europe AI-Powered Predictive Maintenance Systems Market Outlook
7.1. Market Size & Forecast
7.1.1. By Value
7.2. Market Share & Forecast
7.2.1. By Component
7.2.2. By Deployment
7.2.3. By Technology
7.2.4. By Application
7.2.5. By Country
7.3. Europe: Country Analysis
7.3.1. Germany AI-Powered Predictive Maintenance Systems Market Outlook
7.3.1.1. Market Size & Forecast
7.3.1.1.1. By Value
7.3.1.2. Market Share & Forecast
7.3.1.2.1. By Component
7.3.1.2.2. By Deployment
7.3.1.2.3. By Technology
7.3.1.2.4. By Application
7.3.2. France AI-Powered Predictive Maintenance Systems Market Outlook
7.3.2.1. Market Size & Forecast
7.3.2.1.1. By Value
7.3.2.2. Market Share & Forecast
7.3.2.2.1. By Component
7.3.2.2.2. By Deployment
7.3.2.2.3. By Technology
7.3.2.2.4. By Application
7.3.3. United Kingdom AI-Powered Predictive Maintenance Systems Market Outlook
7.3.3.1. Market Size & Forecast
7.3.3.1.1. By Value
7.3.3.2. Market Share & Forecast
7.3.3.2.1. By Component
7.3.3.2.2. By Deployment
7.3.3.2.3. By Technology
7.3.3.2.4. By Application
7.3.4. Italy AI-Powered Predictive Maintenance Systems Market Outlook
7.3.4.1. Market Size & Forecast
7.3.4.1.1. By Value
7.3.4.2. Market Share & Forecast
7.3.4.2.1. By Component
7.3.4.2.2. By Deployment
7.3.4.2.3. By Technology
7.3.4.2.4. By Application
7.3.5. Spain AI-Powered Predictive Maintenance Systems Market Outlook
7.3.5.1. Market Size & Forecast
7.3.5.1.1. By Value
7.3.5.2. Market Share & Forecast
7.3.5.2.1. By Component
7.3.5.2.2. By Deployment
7.3.5.2.3. By Technology
7.3.5.2.4. By Application
8. Asia Pacific AI-Powered Predictive Maintenance Systems Market Outlook
8.1. Market Size & Forecast
8.1.1. By Value
8.2. Market Share & Forecast
8.2.1. By Component
8.2.2. By Deployment
8.2.3. By Technology
8.2.4. By Application
8.2.5. By Country
8.3. Asia Pacific: Country Analysis
8.3.1. China AI-Powered Predictive Maintenance Systems Market Outlook
8.3.1.1. Market Size & Forecast
8.3.1.1.1. By Value
8.3.1.2. Market Share & Forecast
8.3.1.2.1. By Component
8.3.1.2.2. By Deployment
8.3.1.2.3. By Technology
8.3.1.2.4. By Application
8.3.2. India AI-Powered Predictive Maintenance Systems Market Outlook
8.3.2.1. Market Size & Forecast
8.3.2.1.1. By Value
8.3.2.2. Market Share & Forecast
8.3.2.2.1. By Component
8.3.2.2.2. By Deployment
8.3.2.2.3. By Technology
8.3.2.2.4. By Application
8.3.3. Japan AI-Powered Predictive Maintenance Systems Market Outlook
8.3.3.1. Market Size & Forecast
8.3.3.1.1. By Value
8.3.3.2. Market Share & Forecast
8.3.3.2.1. By Component
8.3.3.2.2. By Deployment
8.3.3.2.3. By Technology
8.3.3.2.4. By Application
8.3.4. South Korea AI-Powered Predictive Maintenance Systems Market Outlook
8.3.4.1. Market Size & Forecast
8.3.4.1.1. By Value
8.3.4.2. Market Share & Forecast
8.3.4.2.1. By Component
8.3.4.2.2. By Deployment
8.3.4.2.3. By Technology
8.3.4.2.4. By Application
8.3.5. Australia AI-Powered Predictive Maintenance Systems Market Outlook
8.3.5.1. Market Size & Forecast
8.3.5.1.1. By Value
8.3.5.2. Market Share & Forecast
8.3.5.2.1. By Component
8.3.5.2.2. By Deployment
8.3.5.2.3. By Technology
8.3.5.2.4. By Application
9. Middle East & Africa AI-Powered Predictive Maintenance Systems Market Outlook
9.1. Market Size & Forecast
9.1.1. By Value
9.2. Market Share & Forecast
9.2.1. By Component
9.2.2. By Deployment
9.2.3. By Technology
9.2.4. By Application
9.2.5. By Country
9.3. Middle East & Africa: Country Analysis
9.3.1. Saudi Arabia AI-Powered Predictive Maintenance Systems Market Outlook
9.3.1.1. Market Size & Forecast
9.3.1.1.1. By Value
9.3.1.2. Market Share & Forecast
9.3.1.2.1. By Component
9.3.1.2.2. By Deployment
9.3.1.2.3. By Technology
9.3.1.2.4. By Application
9.3.2. UAE AI-Powered Predictive Maintenance Systems Market Outlook
9.3.2.1. Market Size & Forecast
9.3.2.1.1. By Value
9.3.2.2. Market Share & Forecast
9.3.2.2.1. By Component
9.3.2.2.2. By Deployment
9.3.2.2.3. By Technology
9.3.2.2.4. By Application
9.3.3. South Africa AI-Powered Predictive Maintenance Systems Market Outlook
9.3.3.1. Market Size & Forecast
9.3.3.1.1. By Value
9.3.3.2. Market Share & Forecast
9.3.3.2.1. By Component
9.3.3.2.2. By Deployment
9.3.3.2.3. By Technology
9.3.3.2.4. By Application
10. South America AI-Powered Predictive Maintenance Systems Market Outlook
10.1. Market Size & Forecast
10.1.1. By Value
10.2. Market Share & Forecast
10.2.1. By Component
10.2.2. By Deployment
10.2.3. By Technology
10.2.4. By Application
10.2.5. By Country
10.3. South America: Country Analysis
10.3.1. Brazil AI-Powered Predictive Maintenance Systems Market Outlook
10.3.1.1. Market Size & Forecast
10.3.1.1.1. By Value
10.3.1.2. Market Share & Forecast
10.3.1.2.1. By Component
10.3.1.2.2. By Deployment
10.3.1.2.3. By Technology
10.3.1.2.4. By Application
10.3.2. Colombia AI-Powered Predictive Maintenance Systems Market Outlook
10.3.2.1. Market Size & Forecast
10.3.2.1.1. By Value
10.3.2.2. Market Share & Forecast
10.3.2.2.1. By Component
10.3.2.2.2. By Deployment
10.3.2.2.3. By Technology
10.3.2.2.4. By Application
10.3.3. Argentina AI-Powered Predictive Maintenance Systems Market Outlook
