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市场调查报告书
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1904734

预测性维护自动化市场预测至2032年：按组件、部署类型、技术、最终用户和地区分類的全球分析

Predictive Maintenance Automation Market Forecasts to 2032 - Global Analysis By Component (Software and Services), Deployment Mode, Technology, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 | 商品交期: 2-3个工作天内

价格

根据 Stratistics MRC 的一项研究，预计到 2025 年，全球预测性维护自动化市场规模将达到 33.7 亿美元，到 2032 年将达到 160.9 亿美元，在预测期内的复合年增长率为 25.0%。

自动化预测性维护是指利用自动化系统、进阶分析技术、感测器和人工智慧即时监测资产健康状况，并在潜在故障发生前进行预测。透过持续收集和分析振动、温度和压力等运作数据，企业可以仅在必要时安排维护。这种方法能够最大限度地减少非计划性停机时间，延长资产使用寿命，降低维护成本，并提高工业和製造环境中的营运效率。

从被动模式转向主动模式

自动化预测性维护使企业能够利用感测器、分析和机器学习演算法即时监测资产健康状况。透过识别磨损或故障的早期征兆，企业可以在故障发生前规划维护活动。这种转变显着减少了资产密集产业的非计划性停机时间、维修成本和生产损失。製造商优先考虑营运连续性和效率，以在瞬息万变的市场中保持竞争力。工业IoT平台的日益普及进一步加速了这一转变。随着数位化成熟度的提高，预防性维护模式正从可选项升级转变为策略必需品。

实施初期成本较高

实现精准的预测分析需要部署感测器、边缘设备、资料平台和进阶分析工具。与现有传统基础设施的整合往往会增加复杂性和部署时间。对于中小企业而言，由于短期收入的不确定性，证明资本投资的合理性是一项挑战。管理资料模型和解读预测结果也需要专业人员，从而增加营运成本。网路安全措施和数据管理方面的投资进一步加重了整体财务负担。这些高昂的初始成本可能会减缓技术的普及，尤其是在对成本敏感的行业。

与数位双胞胎的集成

数位双胞胎能够创建实体资产的虚拟副本，从而实现持续的模拟和效能分析。当与预测维修系统结合使用时，企业可以在虚拟环境中检验故障场景和维护策略。这种整合能够提高诊断准确性，并优化资产全生命週期的决策。製造业、能源和交通运输等行业正越来越多地利用数位双胞胎进行资产优化。实体系统和数位系统之间的即时同步能够改善维护计划和资源分配。随着数位双胞胎技术的应用日益广泛，预测性维护自动化解决方案的价值提案将进一步提升。

资料隐私与主权

对资料隐私和主权的担忧日益加剧，为预测性维护自动化市场带来了挑战。这些系统严重依赖从连网机器和工业网路中持续收集资料。敏感的运行资料通常储存或处理在云端环境中，引发了人们对未授权存取的担忧。诸如GDPR和特定地区的资料本地化法律等法规结构增加了合规的复杂性。跨境资料传输的潜在限制阻碍了全球维护平台的扩充性。网路安全风险，包括勒索软体和工业间谍活动，进一步加剧了最终用户的担忧。

新冠疫情的影响：

新冠疫情对预测性维护自动化技术的应用产生了重大影响。製造业营运中断凸显了人工被动维护模式的风险。旅行限制导致现场巡检受限，企业更加依赖远端监控和自动化诊断。许多企业加快了数位转型步伐，以确保在封锁期间资产的可视性。供应链中断凸显了在人力有限的情况下维持设备可靠性的重要性。疫情后的復苏策略优先考虑自动化，以提高营运韧性。因此，预测性维护解决方案在多个产业中得到了更广泛的应用。

预计在预测期内，软体领域将占据最大的市场份额。

预计在预测期内，软体领域将占据最大的市场份额。此领域涵盖分析平台、人工智慧演算法、状态监测应用和资产管理仪錶板。软体解决方案能够对各种类型的设备进行即时资料处理和预测建模。机器学习和云端运算的不断进步正在提升预测的准确性和扩充性。由于软体主导方案的柔软性和易于集成，企业更倾向于选择此类方案。订阅模式也有助于降低最终用户的长期拥有成本。

预计製造业板块在预测期内将实现最高的复合年增长率。

预计製造业将在预测期内实现最高成长率。製造商依赖复杂的机械设备，计划外停机会对生产效率和收入造成重大影响。预测维修系统有助于及早发现生产设备的故障。智慧工厂和工业4.0的倡议普及正在推动对自动化维护解决方案的需求。製造商正在利用数据驱动的洞察来提高资产利用率并优化维护计划。与製造执行系统 (MES) 的整合进一步提高了营运效率。

占比最大的地区：

由于先进工业自动化技术的广泛应用，预计北美地区在预测期内将占据最大的市场份额。主要解决方案供应商和技术创新者的强大实力也推动了市场成长。美国和加拿大的各行业正在大力投资人工智慧驱动的资产管理系统。政府所推行的智慧製造扶持政策进一步促进了相关技术的应用。人们对营运效率和成本优化的高度重视也增强了市场需求。

年复合成长率最高的地区：

预计亚太地区在预测期内将呈现最高的复合年增长率。快速的工业化进程和不断扩大的製造地正在推动全部区域的需求成长。中国、印度、日本和韩国等国家正大力投资数位转型计画。工业IoT和智慧工厂理念的日益普及正在推动市场成长。各国政府正在推广自动化，以提高生产力和全球竞争力。该地区製造业对资产优化意识的提高也进一步推动了自动化技术的应用。

免费客製化服务：

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  • 公司概况
    • 对其他市场公司（最多 3 家公司）进行全面分析
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    • 根据主要企业的产品系列、地理覆盖范围和策略联盟进行基准分析

目录

第一章执行摘要

第二章 前言

  • 摘要
  • 相关利益者
  • 调查范围
  • 调查方法
  • 研究材料

第三章 市场趋势分析

  • 司机
  • 抑制因素
  • 机会
  • 威胁
  • 技术分析
  • 终端用户分析
  • 新兴市场
  • 新冠疫情的感染疾病

第四章 波特五力分析

  • 供应商的议价能力
  • 买方的议价能力
  • 替代品的威胁
  • 新进入者的威胁
  • 竞争对手之间的竞争

5. 全球预测性维护自动化市场（按组件划分）

  • 软体
    • 预测性维护软体
    • 资产绩效管理 (APM) 软体
    • 状态监控软体
  • 服务
    • 咨询及实施服务
    • 支援和维护服务

6. 全球预测性维护自动化市场依部署类型划分

  • 本地部署
  • 云

7. 全球预测性维护自动化市场（依技术划分）

  • 物联网和感测器
  • 人工智慧（AI）和机器学习
  • 巨量资料分析
  • 数位双胞胎
  • 边缘运算
  • 其他技术

8. 全球预测性维护自动化市场（依最终用户划分）

  • 製造业
  • 能源与公共产业
  • 运输/物流
  • 车
  • 石油和天然气
  • 航太/国防
  • 化学品和製药
  • 其他最终用户

9. 全球预测性维护自动化市场（按地区划分）

  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 其他亚太地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 其他南美国家
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲地区

第十章：重大进展

  • 协议、伙伴关係、合作和合资企业
  • 併购
  • 新产品发布
  • 业务拓展
  • 其他关键策略

第十一章 企业概况

  • IBM Corporation
  • TIBCO Software Inc.
  • Microsoft Corporation
  • Uptake Technologies Inc.
  • SAP SE
  • C3.ai, Inc.
  • Siemens AG
  • Oracle Corporation
  • General Electric Company
  • ABB Ltd.
  • Schneider Electric SE
  • PTC Inc.
  • Hitachi, Ltd.
  • Honeywell International Inc.
  • Rockwell Automation, Inc.
Product Code: SMRC33143

According to Stratistics MRC, the Global Predictive Maintenance Automation Market is accounted for $3.37 billion in 2025 and is expected to reach $16.09 billion by 2032 growing at a CAGR of 25.0% during the forecast period. Predictive Maintenance Automation refers to the use of automated systems, advanced analytics, sensors, and artificial intelligence to monitor equipment conditions in real time and predict potential failures before they occur. By continuously collecting and analyzing operational data such as vibration, temperature, and pressure, it enables organizations to schedule maintenance only when needed. This approach minimizes unplanned downtime, extends asset lifespan, reduces maintenance costs, and improves overall operational efficiency across industrial and manufacturing environments.

Market Dynamics:

Driver:

Shift from reactive to proactive models

Predictive maintenance automation enables organizations to monitor asset health in real time using sensors, analytics, and machine learning algorithms. By identifying early signs of wear or malfunction, companies can schedule maintenance activities before breakdowns occur. This shift significantly reduces unplanned downtime, repair costs, and production losses across asset-intensive sectors. Manufacturers are prioritizing operational continuity and efficiency to remain competitive in dynamic markets. The growing availability of industrial IoT platforms is further accelerating this transition. As digital maturity improves, proactive maintenance models are becoming a strategic necessity rather than an optional upgrade.

Restraint:

High upfront implementation costs

Companies must deploy sensors, edge devices, data platforms, and advanced analytics tools to enable accurate predictive insights. Integration with existing legacy infrastructure often increases complexity and implementation timelines. Small and medium-sized enterprises face challenges in justifying capital expenditure due to uncertain short-term returns. Skilled personnel are also required to manage data models and interpret predictive outputs, adding to operational costs. Cybersecurity and data management investments further elevate the overall financial burden. These high upfront expenses can delay adoption, particularly in cost-sensitive industries.

Opportunity:

Integration with digital twins

Digital twins create virtual replicas of physical assets, enabling continuous simulation and performance analysis. When combined with predictive maintenance systems, organizations can test failure scenarios and maintenance strategies in a virtual environment. This integration enhances diagnostic accuracy and improves decision-making across asset lifecycles. Industries such as manufacturing, energy, and transportation are increasingly leveraging digital twins for asset optimization. Real-time synchronization between physical and digital systems improves maintenance planning and resource allocation. As digital twin adoption expands, it is expected to amplify the value proposition of predictive maintenance automation solutions.

Threat:

Data privacy & sovereignty

Data privacy and sovereignty concerns pose a growing challenge for the predictive maintenance automation market. These systems rely heavily on continuous data collection from connected machines and industrial networks. Sensitive operational data is often stored or processed in cloud environments, raising concerns about unauthorized access. Regulatory frameworks such as GDPR and region-specific data localization laws add compliance complexity. Cross-border data transfers can be restricted, limiting the scalability of global maintenance platforms. Cybersecurity risks, including ransomware and industrial espionage, further heighten apprehension among end users.

Covid-19 Impact:

The COVID-19 pandemic significantly influenced the adoption dynamics of predictive maintenance automation. Disruptions to manufacturing operations highlighted the risks associated with manual and reactive maintenance models. Travel restrictions limited on-site inspections, increasing reliance on remote monitoring and automated diagnostics. Many organizations accelerated digital transformation initiatives to ensure asset visibility during lockdowns. Supply chain interruptions emphasized the importance of maintaining equipment reliability with limited workforce availability. Post-pandemic recovery strategies have prioritized automation to enhance operational resilience. As a result, predictive maintenance solutions gained stronger acceptance across multiple industries.

The software segment is expected to be the largest during the forecast period

The software segment is expected to account for the largest market share during the forecast period. This segment includes analytics platforms, AI algorithms, condition monitoring applications, and asset management dashboards. Software solutions enable real-time data processing and predictive modeling across diverse equipment types. Continuous advancements in machine learning and cloud computing are enhancing prediction accuracy and scalability. Organizations prefer software-driven solutions due to their flexibility and ease of integration. Subscription-based models are also reducing long-term ownership costs for end users.

The manufacturing segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the manufacturing segment is predicted to witness the highest growth rate. Manufacturers rely on complex machinery where unplanned downtime can significantly impact productivity and revenue. Predictive maintenance systems help identify early-stage faults in production equipment. The growing adoption of smart factories and Industry 4.0 initiatives is driving demand for automated maintenance solutions. Manufacturers are increasingly using data-driven insights to optimize asset utilization and maintenance schedules. Integration with manufacturing execution systems further enhances operational efficiency.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, due to adoption of advanced industrial automation technologies. Strong presence of major solution providers and technology innovators supports market growth. Industries across the U.S. and Canada are investing heavily in AI-driven asset management systems. Favorable government initiatives promoting smart manufacturing further boost adoption. High awareness of operational efficiency and cost optimization strengthens demand.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR. Rapid industrialization and expanding manufacturing bases are driving demand across the region. Countries such as China, India, Japan, and South Korea are investing heavily in digital transformation initiatives. Increasing adoption of industrial IoT and smart factory concepts is accelerating market growth. Governments are promoting automation to enhance productivity and global competitiveness. Rising awareness of asset optimization among regional manufacturers is further supporting adoption.

Key players in the market

Some of the key players in Predictive Maintenance Automation Market include IBM Corporation, TIBCO Software, Microsoft, Uptake Technologies, SAP SE, C3.ai, Inc., Siemens AG, Oracle Corporation, General Electric, ABB Ltd., Schneider Electric, PTC Inc., Hitachi, Ltd, Honeywell, and Rockwell Automation.

Key Developments:

In December 2025, IBM and Confluent, Inc. announced they have entered into a definitive agreement under which IBM will acquire all of the issued and outstanding common shares of Confluent for $31 per share, representing an enterprise value of $11 billion. Confluent provides a leading open-source enterprise data streaming platform that connects, processes and governs reusable and reliable data and events in real time, foundational for the deployment of AI.

In October 2025, Oracle announced the latest capabilities added to Oracle Database@AWS to better support mission-critical enterprise workloads in the cloud. In addition, customers can now procure Oracle Database@AWS through qualified AWS and Oracle channel partners. This gives customers the flexibility to procure Oracle Database@AWS through their trusted partners and continue to innovate, modernize, and solve complex business problems in the cloud.

Components Covered:

  • Software
  • Services

Deployment Modes Covered:

  • On-Premises
  • Cloud

Technologies Covered:

  • IoT & Sensors
  • Artificial Intelligence & Machine Learning
  • Big Data Analytics
  • Digital Twin
  • Edge Computing
  • Other Technologies

End Users Covered:

  • Manufacturing
  • Energy & Utilities
  • Transportation & Logistics
  • Automotive
  • Oil & Gas
  • Aerospace & Defense
  • Chemicals & Pharmaceuticals
  • Other End Users

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

  • 2.1 Abstract
  • 2.2 Stake Holders
  • 2.3 Research Scope
  • 2.4 Research Methodology
    • 2.4.1 Data Mining
    • 2.4.2 Data Analysis
    • 2.4.3 Data Validation
    • 2.4.4 Research Approach
  • 2.5 Research Sources
    • 2.5.1 Primary Research Sources
    • 2.5.2 Secondary Research Sources
    • 2.5.3 Assumptions

3 Market Trend Analysis

  • 3.1 Introduction
  • 3.2 Drivers
  • 3.3 Restraints
  • 3.4 Opportunities
  • 3.5 Threats
  • 3.6 Technology Analysis
  • 3.7 End User Analysis
  • 3.8 Emerging Markets
  • 3.9 Impact of Covid-19

4 Porters Five Force Analysis

  • 4.1 Bargaining power of suppliers
  • 4.2 Bargaining power of buyers
  • 4.3 Threat of substitutes
  • 4.4 Threat of new entrants
  • 4.5 Competitive rivalry

5 Global Predictive Maintenance Automation Market, By Component

  • 5.1 Introduction
  • 5.2 Software
    • 5.2.1 Predictive Analytics Software
    • 5.2.2 Asset Performance Management (APM) Software
    • 5.2.3 Condition Monitoring Software
  • 5.3 Services
    • 5.3.1 Consulting & Implementation Services
    • 5.3.2 Support & Maintenance Services

6 Global Predictive Maintenance Automation Market, By Deployment Mode

  • 6.1 Introduction
  • 6.2 On-Premises
  • 6.3 Cloud

7 Global Predictive Maintenance Automation Market, By Technology

  • 7.1 Introduction
  • 7.2 IoT & Sensors
  • 7.3 Artificial Intelligence & Machine Learning
  • 7.4 Big Data Analytics
  • 7.5 Digital Twin
  • 7.6 Edge Computing
  • 7.7 Other Technologies

8 Global Predictive Maintenance Automation Market, By End User

  • 8.1 Introduction
  • 8.2 Manufacturing
  • 8.3 Energy & Utilities
  • 8.4 Transportation & Logistics
  • 8.5 Automotive
  • 8.6 Oil & Gas
  • 8.7 Aerospace & Defense
  • 8.8 Chemicals & Pharmaceuticals
  • 8.9 Other End Users

9 Global Predictive Maintenance Automation Market, By Geography

  • 9.1 Introduction
  • 9.2 North America
    • 9.2.1 US
    • 9.2.2 Canada
    • 9.2.3 Mexico
  • 9.3 Europe
    • 9.3.1 Germany
    • 9.3.2 UK
    • 9.3.3 Italy
    • 9.3.4 France
    • 9.3.5 Spain
    • 9.3.6 Rest of Europe
  • 9.4 Asia Pacific
    • 9.4.1 Japan
    • 9.4.2 China
    • 9.4.3 India
    • 9.4.4 Australia
    • 9.4.5 New Zealand
    • 9.4.6 South Korea
    • 9.4.7 Rest of Asia Pacific
  • 9.5 South America
    • 9.5.1 Argentina
    • 9.5.2 Brazil
    • 9.5.3 Chile
    • 9.5.4 Rest of South America
  • 9.6 Middle East & Africa
    • 9.6.1 Saudi Arabia
    • 9.6.2 UAE
    • 9.6.3 Qatar
    • 9.6.4 South Africa
    • 9.6.5 Rest of Middle East & Africa

10 Key Developments

  • 10.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 10.2 Acquisitions & Mergers
  • 10.3 New Product Launch
  • 10.4 Expansions
  • 10.5 Other Key Strategies

11 Company Profiling

  • 11.1 IBM Corporation
  • 11.2 TIBCO Software Inc.
  • 11.3 Microsoft Corporation
  • 11.4 Uptake Technologies Inc.
  • 11.5 SAP SE
  • 11.6 C3.ai, Inc.
  • 11.7 Siemens AG
  • 11.8 Oracle Corporation
  • 11.9 General Electric Company
  • 11.10 ABB Ltd.
  • 11.11 Schneider Electric SE
  • 11.12 PTC Inc.
  • 11.13 Hitachi, Ltd.
  • 11.14 Honeywell International Inc.
  • 11.15 Rockwell Automation, Inc.

List of Tables

  • Table 1 Global Predictive Maintenance Automation Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global Predictive Maintenance Automation Market Outlook, By Component (2024-2032) ($MN)
  • Table 3 Global Predictive Maintenance Automation Market Outlook, By Software (2024-2032) ($MN)
  • Table 4 Global Predictive Maintenance Automation Market Outlook, By Predictive Analytics Software (2024-2032) ($MN)
  • Table 5 Global Predictive Maintenance Automation Market Outlook, By Asset Performance Management (APM) Software (2024-2032) ($MN)
  • Table 6 Global Predictive Maintenance Automation Market Outlook, By Condition Monitoring Software (2024-2032) ($MN)
  • Table 7 Global Predictive Maintenance Automation Market Outlook, By Services (2024-2032) ($MN)
  • Table 8 Global Predictive Maintenance Automation Market Outlook, By Consulting & Implementation Services (2024-2032) ($MN)
  • Table 9 Global Predictive Maintenance Automation Market Outlook, By Support & Maintenance Services (2024-2032) ($MN)
  • Table 10 Global Predictive Maintenance Automation Market Outlook, By Deployment Mode (2024-2032) ($MN)
  • Table 11 Global Predictive Maintenance Automation Market Outlook, By On-Premises (2024-2032) ($MN)
  • Table 12 Global Predictive Maintenance Automation Market Outlook, By Cloud (2024-2032) ($MN)
  • Table 13 Global Predictive Maintenance Automation Market Outlook, By Technology (2024-2032) ($MN)
  • Table 14 Global Predictive Maintenance Automation Market Outlook, By IoT & Sensors (2024-2032) ($MN)
  • Table 15 Global Predictive Maintenance Automation Market Outlook, By Artificial Intelligence & Machine Learning (2024-2032) ($MN)
  • Table 16 Global Predictive Maintenance Automation Market Outlook, By Big Data Analytics (2024-2032) ($MN)
  • Table 17 Global Predictive Maintenance Automation Market Outlook, By Digital Twin (2024-2032) ($MN)
  • Table 18 Global Predictive Maintenance Automation Market Outlook, By Edge Computing (2024-2032) ($MN)
  • Table 19 Global Predictive Maintenance Automation Market Outlook, By Other Technologies (2024-2032) ($MN)
  • Table 20 Global Predictive Maintenance Automation Market Outlook, By End User (2024-2032) ($MN)
  • Table 21 Global Predictive Maintenance Automation Market Outlook, By Manufacturing (2024-2032) ($MN)
  • Table 22 Global Predictive Maintenance Automation Market Outlook, By Energy & Utilities (2024-2032) ($MN)
  • Table 23 Global Predictive Maintenance Automation Market Outlook, By Transportation & Logistics (2024-2032) ($MN)
  • Table 24 Global Predictive Maintenance Automation Market Outlook, By Automotive (2024-2032) ($MN)
  • Table 25 Global Predictive Maintenance Automation Market Outlook, By Oil & Gas (2024-2032) ($MN)
  • Table 26 Global Predictive Maintenance Automation Market Outlook, By Aerospace & Defense (2024-2032) ($MN)
  • Table 27 Global Predictive Maintenance Automation Market Outlook, By Chemicals & Pharmaceuticals (2024-2032) ($MN)
  • Table 28 Global Predictive Maintenance Automation Market Outlook, By Other End Users (2024-2032) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.