人工智慧预测性维护系统市场—全球产业规模、份额、趋势、机会和预测（按组件、按部署、按技术、按应用、按地区和竞争，2020-2030 年）

2024年，全球人工智慧预测性维护系统市场规模达7.7303亿美元，预计2030年将达到15.2887亿美元，预测期内复合年增长率为12.04%。该市场涵盖人工智慧驱动的解决方案，这些解决方案能够分析来自感测器、机械和控制系统的资料，从而预测设备故障。与传统的被动维护或定期维护不同，这些系统提供了一种主动的即时方法，可提高效率、最大限度地减少停机时间并延长资产使用寿命。人工智慧预测性维护广泛应用于製造业、能源业、交通运输业和医疗保健业等行业，由于工业自动化、物联网整合和即时分析的普及，其应用正在加速发展。随着云端运算和边缘人工智慧的发展，即使对于中型企业而言，部署也变得更加可扩展且易于存取。这些因素，加上对资产绩效和营运连续性的日益关注，正在推动该市场的快速成长。

关键市场驱动因素

工业自动化和智慧製造的蓬勃发展

主要市场挑战

跨遗留系统的资料孤岛与整合复杂性

主要市场趋势

整合数位孪生以实现即时资产模拟

目录

第 1 章：解决方案概述

• 市场定义
• 市场范围
  • 覆盖市场
  • 考虑学习的年限
  • 主要市场区隔

第二章：研究方法

第三章：执行摘要

第四章：顾客之声

第五章：全球人工智慧预测性维护系统市场展望

• 市场规模和预测
  • 按价值
• 市场占有率和预测
  • 按组件（硬体、软体、服务）
  • 按部署（本地、基于云端、混合）
  • 按技术（机器学习、深度学习、自然语言处理、电脑视觉、边缘人工智慧）
  • 按应用（状态监控、故障检测与诊断、资产绩效管理、能耗优化、其他）
  • 按地区（北美、欧洲、南美、中东和非洲、亚太地区）
• 按公司分类（2024）
• 市场地图

第六章：北美人工智慧预测性维护系统市场展望

• 市场规模和预测
• 市场占有率和预测
• 北美：国家分析
  • 美国
  • 加拿大
  • 墨西哥

第七章：欧洲人工智慧预测性维护系统市场展望

• 市场规模和预测
• 市场占有率和预测
• 欧洲：国家分析
  • 德国
  • 法国
  • 英国
  • 义大利
  • 西班牙

第八章：亚太地区人工智慧预测性维护系统市场展望

• 市场规模和预测
• 市场占有率和预测
• 亚太地区：国家分析
  • 中国
  • 印度
  • 日本
  • 韩国
  • 澳洲

第九章：中东和非洲人工智慧预测性维护系统市场展望

• 市场规模和预测
• 市场占有率和预测
• 中东和非洲：国家分析
  • 沙乌地阿拉伯
  • 阿联酋
  • 南非

第十章：南美人工智慧预测性维护系统市场展望

• 市场规模和预测
• 市场占有率和预测
• 南美洲：国家分析
  • 巴西
  • 哥伦比亚
  • 阿根廷

第 11 章：市场动态

• 驱动程式
• 挑战

第 12 章：市场趋势与发展

• 合併与收购（如有）
• 产品发布（如有）
• 最新动态

第十三章：公司简介

• IBM Corporation
• Microsoft Corporation
• SAP SE
• Siemens AG
• General Electric Company
• PTC Inc.
• Schneider Electric SE
• ABB Ltd.

第 14 章：策略建议

第15章调查会社について・免责事项

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.

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
    • 10.3.3.1. Market Size & Forecast
      • 10.3.3.1.1. By Value
    • 10.3.3.2. Market Share & Forecast
      • 10.3.3.2.1. By Component
      • 10.3.3.2.2. By Deployment
      • 10.3.3.2.3. By Technology
      • 10.3.3.2.4. By Application

11. Market Dynamics

• 11.1. Drivers
• 11.2. Challenges

12. Market Trends and Developments

• 12.1. Merger & Acquisition (If Any)
• 12.2. Product Launches (If Any)
• 12.3. Recent Developments

13. Company Profiles

• 13.1. IBM Corporation
  • 13.1.1. Business Overview
  • 13.1.2. Key Revenue and Financials
  • 13.1.3. Recent Developments
  • 13.1.4. Key Personnel
  • 13.1.5. Key Product/Services Offered
• 13.2. Microsoft Corporation
• 13.3. SAP SE
• 13.4. Siemens AG
• 13.5. General Electric Company
• 13.6. PTC Inc.
• 13.7. Schneider Electric SE
• 13.8. ABB Ltd.

14. Strategic Recommendations

15. About Us & Disclaimer