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市场调查报告书
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1859730

全球人工智慧驱动的预测性维护平台市场：未来预测（至2032年）—按组件、部署模式、技术、应用、最终用户和地区进行分析

AI-Powered Predictive Maintenance Platforms Market Forecasts to 2032 - Global Analysis By Component (Hardware, Software and Services), Deployment Mode, Technology, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3个工作天内

价格

根据 Stratistics MRC 的数据，全球人工智慧驱动的预测性维护平台市场预计到 2025 年将达到 8.7418 亿美元，到 2032 年将达到 20.4391 亿美元，预测期内复合年增长率为 12.9%。

人工智慧驱动的预测性维护平台利用先进的人工智慧、机器学习和物联网整合技术，在设备故障发生前进行预测。这些系统处理海量感测器产生的数据，以检测机器异常、性能偏差和磨损模式。透过预测分析，企业可以更有效地规划维护，减少非计画故障，并延长资产寿命。製造业、能源、物流和医疗保健等行业正日益依赖这些平台来提高生产力并降低营运成本。透过支援数据主导策略，这些人工智慧工具将维护从被动或预防性流程转变为预测性流程，从而提高可靠性、安全性和整体效率。

根据欧盟委员会的《人工智慧在製造业应用概况介绍》，预测性维护是欧洲工业界人工智慧应用的三大主要案例之一，截至 2023 年，超过 50% 的大型製造商正在试行或部署基于人工智慧的维护系统。

工业IoT和智慧感测器应用日益普及

工业IoT和智慧感测技术的扩展正显着推动人工智慧驱动的预测性维护平台的发展。这些设备能够撷取即时机器数据，人工智慧模型可以解读这些数据，从而主动识别潜在的故障和维护需求。持续的数据监控提高了磨损和性能下降检测的准确性，使企业能够预防代价高昂的设备故障。随着工业4.0的日益普及，工业界正迅速向数据主导的维护实践转型。智慧感测器助力企业优化流程、最大限度地延长运作并实现卓越运营，从而推动预测性维护平台在製造业、公共产业、交通运输和工业自动化等行业的广泛应用。

高昂的实施和整合成本

部署和整合人工智慧驱动的预测性维护平台的高昂成本是限制市场发展的主要因素。这些系统需要对先进的人工智慧工具、感测器网路、资料管理基础设施和技术专长进行大量投资。小规模的企业很难承担如此前期投资。此外，与过时的旧有系统整合通常涉及复杂的客製化和较长的部署时间。持续的系统升级和维护也会推高整体成本。虽然预测性维护能够带来长期的效率提升和营运成本节约，但最初的财务和技术障碍阻碍了许多公司，尤其是那些对成本敏感的部门，大规模采用此类先进的维护技术。

云端运算和边缘运算的日益普及

云端运算和边缘运算技术的日益普及，为人工智慧驱动的预测性维护平台开闢了新的成长途径。云端运算使企业能够处理和储存海量资料集，并随时随地即时存取人工智慧主导的洞察。边缘运算则透过实现靠近装置的快速本地化资料分析，进一步增强了云端运算的功能，从而降低了延迟并加快了响应速度。这种混合架构提高了预测维修系统的运作灵活性、可靠性和扩充性。随着企业采用分散式运算环境，云端运算和边缘框架与人工智慧的整合预计将加速发展，从而在各行各业实现更高的灵活性、成本节约和效能优化。

高度依赖数据品质和可用性

人工智慧驱动的预测性维护平台对资料品质和可存取性的高度依赖对其效能构成重大威胁。如果输入资料不准确、不完整或不一致，预测演算法将产生不可靠的结果，导致代价高昂的维护错误。许多行业难以从感测器和旧有系统等各种来源收集统一的数据。部署阶段资料量的不足也会限制模型的训练和准确性。此外，资料集中的杂讯和不一致性会损害系统的可靠性和决策能力。这种对高品质数据的严重依赖持续挑战工业环境中预测性维护解决方案的准确性和可靠性。

新冠疫情的影响：

新冠疫情为人工智慧驱动的预测性维护平台市场带来了挑战和机会。初期，工业停工、供应链问题和劳动力减少阻碍了系统部署和新投资。然而，疫情最终加速了数位转型，企业纷纷采用基于人工智慧和物联网的预测性维护技术来远端监控设备并减少人工干预。事实证明，这些技术对于在疫情衝击下维持生产效率和营运可靠性至关重要。疫情过后，许多企业持续整合人工智慧驱动的维护系统，以增强韧性、提高成本效益，并在一个更互联、技术依赖的产业环境中支援主导营运。

预计在预测期内，软体板块将成为最大的板块。

预计在预测期内，软体领域将占据最大的市场份额，因为它为智慧分析、机器学习和预测洞察提供了坚实的基础。这些软体工具能够分析大量的机器和感测器数据，从而识别异常情况、预测潜在故障并制定及时的维护措施。基于云端和主导的软体平台增强了扩充性和连接性，使企业能够有效率地即时管理资产。与现有企业系统的整合可实现流畅的资料流和明智的维护决策。随着自动化和数位化优化的日益普及，各行业正在增加对预测性维护软体的投资，从而巩固其市场主导地位。

预计能源与公共产业产业在预测期内将实现最高的复合年增长率。

预计能源与公共产业产业在预测期内将呈现最高的成长率。该行业正日益广泛地采用人工智慧技术来监控和维护涡轮机、变压器和电网等关键资产。预测性维护支援持续监控、及早发现问题并提升设备效能，从而降低营运风险和停机时间。随着产业拥抱数位化并向可再生能源和智慧电网系统转型，基于人工智慧的预测工具已成为优化能源分配和可靠性的关键。物联网、数据分析和人工智慧的结合提高了资产效率，推动了这些技术在能源与公共产业的快速应用。

比最大的地区

在预测期内，北美预计将占据最大的市场份额，这主要得益于技术的快速发展以及人工智慧和物联网在工业领域的广泛应用。该地区成熟的基础设施以及在製造业、能源和航太等领域的巨额投资，正推动预测维修系统的显着普及。总部位于该地区的领先技术供应商和解决方案开发商正致力于技术创新和大规模部署。政府鼓励广泛自动化、数据主导营运和数位转型的措施也推动了市场成长。凭藉其强大的先进产业生态系统和尖端的分析能力，北美透过以效率为导向的工业现代化，继续在全球预测性维护领域占据主导地位。

复合年增长率最高的地区：

在预测期内，亚太地区预计将呈现最高的复合年增长率，这主要得益于工业发展的加速和对自动化的高度重视。包括中国、日本、韩国和印度在内的多个国家正积极投资人工智慧主导的维护系统，以提高效率并减少计划外设备故障。能源、汽车和电子等产业的智慧製造项目和数位转型倡议的扩展是关键的成长驱动力。政府鼓励采用工业4.0的支持性政策进一步提升了该地区的市场潜力。凭藉其不断扩展的工业生态系统以及物联网和分析技术的日益融合，亚太地区有望引领未来的成长。

免费客製化服务：

订阅本报告的用户可享有以下免费客製化服务之一：

  • 公司简介
    • 对其他市场公司（最多 3 家公司）进行全面分析
    • 对主要企业进行SWOT分析（最多3家公司）
  • 区域细分
    • 根据客户兴趣对主要国家进行市场估算、预测和复合年增长率分析（註：基于可行性检查）
  • 竞争基准化分析
    • 基于产品系列、地域覆盖和策略联盟对主要企业基准化分析

目录

第一章执行摘要

第二章 前言

  • 概述
  • 相关利益者
  • 调查范围
  • 调查方法
    • 资料探勘
    • 数据分析
    • 数据检验
    • 研究途径
  • 研究材料
    • 原始研究资料
    • 次级研究资讯来源
    • 先决条件

第三章 市场趋势分析

  • 司机
  • 抑制因素
  • 机会
  • 威胁
  • 技术分析
  • 应用分析
  • 终端用户分析
  • 新兴市场
  • 新冠疫情的影响

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. 全球人工智慧驱动型预测性维护平台市场（按组件划分）

  • 硬体
  • 软体
  • 服务

6. 全球人工智慧驱动的预测性维护平台市场（按部署模式划分）

  • 本地部署
  • 云端基础的
  • 杂交种

7. 全球人工智慧驱动的预测性维护平台市场（依技术划分）

  • 机器学习
  • 自然语言处理
  • 电脑视觉
  • 边缘人工智慧

8. 全球人工智慧驱动的预测性维护平台市场（按应用划分）

  • 状态监测
  • 故障检测与诊断
  • 剩余使用寿命（RUL）估算
  • 优化能源效率
  • 自动化维护计划
  • 停机风险预测

9. 全球人工智慧驱动的预测性维护平台市场（按最终用户划分）

  • 离散製造
  • 工艺製造
  • 运输/物流
  • 航太与国防
  • 医疗机构
  • 能源与公共产业
  • 石油和天然气
  • 通讯
  • 采矿和重型设备
  • 智慧农业

第十章：全球人工智慧驱动的预测性维护平台市场（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
    • 南美洲其他地区
  • 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

第十一章 重大进展

  • 协议、伙伴关係、合作和合资企业
  • 收购与併购
  • 新产品上市
  • 业务拓展
  • 其他关键策略

第十二章 企业概况

  • IBM
  • GE Digital
  • Siemens
  • C3.ai
  • Hitachi Vantara
  • ABB
  • SAP
  • Uptake
  • PTC
  • OpenText
  • Dassault Systemes
  • Rapid Innovation
  • Schneider Electric
  • Microsoft
  • Honeywell
Product Code: SMRC31954

According to Stratistics MRC, the Global AI-Powered Predictive Maintenance Platforms Market is accounted for $874.18 million in 2025 and is expected to reach $2043.91 million by 2032 growing at a CAGR of 12.9% during the forecast period. AI-powered predictive maintenance platforms utilize advanced artificial intelligence, machine learning, and IoT integration to anticipate equipment malfunctions before they happen. These systems process vast amounts of sensor-generated data to detect irregularities, performance drifts, and wear patterns in machines. Through predictive analytics, organizations can schedule maintenance more effectively, reduce unexpected breakdowns, and extend the service life of assets. Sectors like manufacturing, energy, logistics, and healthcare increasingly rely on such platforms for improved productivity and reduced operational costs. By enabling data-driven strategies, these AI tools transform maintenance from a reactive or preventive process into a predictive one, enhancing reliability, safety, and overall efficiency.

According to the European Commission's Fact Sheet on AI in Manufacturing, predictive maintenance is one of the top three AI use cases in European industry, with over 50% of large manufacturers piloting or deploying AI-based maintenance systems as of 2023.

Market Dynamics:

Driver:

Growing adoption of industrial IoT and smart sensors

The expansion of Industrial IoT and smart sensing technologies is significantly fueling the growth of AI-powered predictive maintenance platforms. These devices capture real-time machine data, which AI models interpret to identify potential faults and maintenance needs in advance. Continuous data monitoring enhances precision in detecting wear or performance decline, allowing companies to prevent costly equipment failures. With Industry 4.0 adoption increasing, industries are rapidly transitioning toward data-driven maintenance practices. Smart sensors empower organizations to optimize processes, maximize uptime, and achieve operational excellence, driving the widespread implementation of predictive maintenance platforms across sectors such as manufacturing, utilities, transportation, and industrial automation.

Restraint:

High implementation and integration costs

The substantial expenses associated with implementing and integrating AI-powered predictive maintenance platforms act as a key market restraint. These systems require heavy investment in advanced AI tools, sensor networks, data management infrastructure, and technical expertise. Smaller organizations find it challenging to justify these upfront expenditures. Moreover, integration with outdated legacy systems often involves complex customization and extended deployment periods. Continuous system upgrades and maintenance also add to total costs. Although predictive maintenance provides long-term efficiency and operational savings, the initial financial and technical barriers discourage many enterprises-particularly in cost-sensitive sectors-from adopting these advanced maintenance technologies at scale.

Opportunity:

Rising adoption of cloud and edge computing

The growing deployment of cloud and edge computing technologies is opening new growth avenues for AI-powered predictive maintenance platforms. Cloud computing allows enterprises to process and store massive datasets while accessing AI-driven insights from anywhere in real time. Edge computing complements this by enabling rapid, localized data analysis close to the equipment, ensuring low latency and faster responses. This hybrid architecture enhances operational agility, reliability, and scalability for predictive maintenance systems. As organizations embrace distributed computing environments, the integration of AI with cloud and edge frameworks is expected to accelerate, supporting greater flexibility, cost reduction, and performance optimization across industries.

Threat:

High dependency on data quality and availability

AI-powered predictive maintenance platforms are highly reliant on data quality and accessibility, posing a significant threat to their performance. When input data is inaccurate, incomplete, or inconsistent, the predictive algorithms generate unreliable results, leading to costly maintenance errors. Many industries struggle to gather uniform data from varied sources such as sensors and legacy systems. Limited data during deployment phases also restricts model training and precision. Moreover, noise or discrepancies in datasets can compromise system reliability and decision-making. This strong dependence on high-quality data continues to challenge the accuracy and credibility of predictive maintenance solutions across industrial environments.

Covid-19 Impact:

The outbreak of COVID-19 created both challenges and opportunities for the AI-powered predictive maintenance platforms market. In the early stages, industrial shutdowns, supply chain issues, and reduced workforce capacity hindered system deployment and new investments. Yet, the pandemic ultimately accelerated digital transformation as companies adopted AI and IoT-based predictive maintenance for remote equipment monitoring and reduced manual intervention. These technologies proved essential for maintaining production efficiency and operational reliability amid disruptions. Following the pandemic, many organizations continued integrating AI-powered maintenance systems to strengthen resilience, improve cost efficiency, and support automation-driven operations in a more connected and technology-dependent industrial landscape.

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, as it serves as the backbone for intelligent analytics, machine learning, and predictive insights. These software tools analyze extensive machine and sensor data to identify irregularities, predict potential breakdowns, and plan timely maintenance actions. Cloud-enabled and AI-driven software platforms offer enhanced scalability and connectivity, enabling organizations to manage assets efficiently in real time. Integration with existing enterprise systems allows for smooth data flow and informed maintenance decisions. With the growing emphasis on automation and digital optimization, industries are increasingly investing in predictive maintenance software, reinforcing its leading position within the market.

The energy & utilities segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the energy & utilities segment is predicted to witness the highest growth rate. This sector increasingly employs AI technologies to oversee and maintain essential assets like turbines, transformers, and power grids. Predictive maintenance supports continuous monitoring, early issue detection, and improved equipment performance, reducing operational risks and downtime. As the industry embraces digitalization and shifts toward renewable energy and smart grid systems, AI-based predictive tools are becoming essential for optimizing energy distribution and reliability. The combination of IoT, data analytics, and AI enhances asset efficiency, driving rapid adoption within the energy and utilities segment.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, supported by rapid technological advancements and strong industrial adoption of AI and IoT. The region's mature infrastructure and high investment in sectors such as manufacturing, energy, and aerospace have driven significant implementation of predictive maintenance systems. Major technology providers and solution developers headquartered in the region contribute to innovation and large-scale deployment. Widespread automation, data-driven operations, and government initiatives encouraging digital transformation also enhance market growth. With its strong ecosystem of advanced industries and cutting-edge analytics capabilities, North America continues to dominate the global predictive maintenance landscape through efficiency-focused industrial modernization.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR due to its accelerating industrial development and strong focus on automation. Nations including China, Japan, South Korea, and India are actively investing in AI-driven maintenance systems to improve efficiency and reduce unexpected equipment failures. Expanding smart manufacturing programs and digital transformation initiatives across industries such as energy, automotive, and electronics are key growth drivers. Supportive government policies encouraging Industry 4.0 adoption are further enhancing regional market potential. With its growing industrial ecosystem and increasing integration of IoT and analytics, Asia-Pacific is set to lead future growth.

Key players in the market

Some of the key players in AI-Powered Predictive Maintenance Platforms Market include IBM, GE Digital, Siemens, C3.ai, Hitachi Vantara, ABB, SAP, Uptake, PTC, OpenText, Dassault Systemes, Rapid Innovation, Schneider Electric, Microsoft and Honeywell.

Key Developments:

In October 2025, IBM announced that it has signed a definitive agreement to acquire Cognitus with industry-specific, AI-powered solutions. Cognitus will bring mission-critical SAP skills, including in RISE and GROW with SAP, as well as an extensive portfolio of software assets. This combination of services, software and industry expertise, aligns with IBM's asset-based approach to digital transformation, driving increased productivity and operational efficiency for clients around the world.

In October 2025, Hitachi Vantara and Supermicro have announced collaboration aimed at helping enterprises in Southeast Asia modernise their AI infrastructure by integrating their respective data storage and compute solutions. The partnership seeks to address the increasing challenges faced by organisations deploying AI and generative AI workloads, particularly those related to data fragmentation and infrastructure bottlenecks.

In May 2025, C3 AI announced a multi-year renewal and expansion of their joint venture agreement through June 2028. Under the terms of the agreement, C3 AI and Baker Hughes will continue to develop, deliver, and market Enterprise AI solutions to the oil and gas and chemical industries. C3 AI will also continue to deliver Enterprise AI solutions for internal use at Baker Hughes, who will expand deployments of C3 AI Sourcing Optimization, C3 AI Inventory Optimization, and the C3 AI Sustainability Suite.

Components Covered:

  • Hardware
  • Software
  • Services

Deployment Modes Covered:

  • On-Premises
  • Cloud-Based
  • Hybrid

Technologies Covered:

  • Machine Learning
  • Natural Language Processing
  • Computer Vision
  • Edge AI

Applications Covered:

  • Condition Monitoring
  • Failure Detection & Diagnosis
  • Remaining Useful Life (RUL) Estimation
  • Energy Efficiency Optimization
  • Maintenance Scheduling Automation
  • Downtime Risk Forecasting

End Users Covered:

  • Discrete Manufacturing
  • Process Manufacturing
  • Transportation & Logistics
  • Aerospace & Defense
  • Healthcare Facilities
  • Energy & Utilities
  • Oil & Gas
  • Telecommunications
  • Mining & Heavy Equipment
  • Smart Agriculture

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 Application Analysis
  • 3.8 End User Analysis
  • 3.9 Emerging Markets
  • 3.10 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 AI-Powered Predictive Maintenance Platforms Market, By Component

  • 5.1 Introduction
  • 5.2 Hardware
  • 5.3 Software
  • 5.4 Services

6 Global AI-Powered Predictive Maintenance Platforms Market, By Deployment Mode

  • 6.1 Introduction
  • 6.2 On-Premises
  • 6.3 Cloud-Based
  • 6.4 Hybrid

7 Global AI-Powered Predictive Maintenance Platforms Market, By Technology

  • 7.1 Introduction
  • 7.2 Machine Learning
  • 7.3 Natural Language Processing
  • 7.4 Computer Vision
  • 7.5 Edge AI

8 Global AI-Powered Predictive Maintenance Platforms Market, By Application

  • 8.1 Introduction
  • 8.2 Condition Monitoring
  • 8.3 Failure Detection & Diagnosis
  • 8.4 Remaining Useful Life (RUL) Estimation
  • 8.5 Energy Efficiency Optimization
  • 8.6 Maintenance Scheduling Automation
  • 8.7 Downtime Risk Forecasting

9 Global AI-Powered Predictive Maintenance Platforms Market, By End User

  • 9.1 Introduction
  • 9.2 Discrete Manufacturing
  • 9.3 Process Manufacturing
  • 9.4 Transportation & Logistics
  • 9.5 Aerospace & Defense
  • 9.6 Healthcare Facilities
  • 9.7 Energy & Utilities
  • 9.8 Oil & Gas
  • 9.9 Telecommunications
  • 9.10 Mining & Heavy Equipment
  • 9.11 Smart Agriculture

10 Global AI-Powered Predictive Maintenance Platforms Market, By Geography

  • 10.1 Introduction
  • 10.2 North America
    • 10.2.1 US
    • 10.2.2 Canada
    • 10.2.3 Mexico
  • 10.3 Europe
    • 10.3.1 Germany
    • 10.3.2 UK
    • 10.3.3 Italy
    • 10.3.4 France
    • 10.3.5 Spain
    • 10.3.6 Rest of Europe
  • 10.4 Asia Pacific
    • 10.4.1 Japan
    • 10.4.2 China
    • 10.4.3 India
    • 10.4.4 Australia
    • 10.4.5 New Zealand
    • 10.4.6 South Korea
    • 10.4.7 Rest of Asia Pacific
  • 10.5 South America
    • 10.5.1 Argentina
    • 10.5.2 Brazil
    • 10.5.3 Chile
    • 10.5.4 Rest of South America
  • 10.6 Middle East & Africa
    • 10.6.1 Saudi Arabia
    • 10.6.2 UAE
    • 10.6.3 Qatar
    • 10.6.4 South Africa
    • 10.6.5 Rest of Middle East & Africa

11 Key Developments

  • 11.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 11.2 Acquisitions & Mergers
  • 11.3 New Product Launch
  • 11.4 Expansions
  • 11.5 Other Key Strategies

12 Company Profiling

  • 12.1 IBM
  • 12.2 GE Digital
  • 12.3 Siemens
  • 12.4 C3.ai
  • 12.5 Hitachi Vantara
  • 12.6 ABB
  • 12.7 SAP
  • 12.8 Uptake
  • 12.9 PTC
  • 12.10 OpenText
  • 12.11 Dassault Systemes
  • 12.12 Rapid Innovation
  • 12.13 Schneider Electric
  • 12.14 Microsoft
  • 12.15 Honeywell

List of Tables

  • Table 1 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Component (2024-2032) ($MN)
  • Table 3 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Hardware (2024-2032) ($MN)
  • Table 4 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Software (2024-2032) ($MN)
  • Table 5 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Services (2024-2032) ($MN)
  • Table 6 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Deployment Mode (2024-2032) ($MN)
  • Table 7 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By On-Premises (2024-2032) ($MN)
  • Table 8 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Cloud-Based (2024-2032) ($MN)
  • Table 9 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Hybrid (2024-2032) ($MN)
  • Table 10 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Technology (2024-2032) ($MN)
  • Table 11 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Machine Learning (2024-2032) ($MN)
  • Table 12 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Natural Language Processing (2024-2032) ($MN)
  • Table 13 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Computer Vision (2024-2032) ($MN)
  • Table 14 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Edge AI (2024-2032) ($MN)
  • Table 15 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Application (2024-2032) ($MN)
  • Table 16 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Condition Monitoring (2024-2032) ($MN)
  • Table 17 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Failure Detection & Diagnosis (2024-2032) ($MN)
  • Table 18 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Remaining Useful Life (RUL) Estimation (2024-2032) ($MN)
  • Table 19 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Energy Efficiency Optimization (2024-2032) ($MN)
  • Table 20 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Maintenance Scheduling Automation (2024-2032) ($MN)
  • Table 21 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Downtime Risk Forecasting (2024-2032) ($MN)
  • Table 22 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By End User (2024-2032) ($MN)
  • Table 23 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Discrete Manufacturing (2024-2032) ($MN)
  • Table 24 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Process Manufacturing (2024-2032) ($MN)
  • Table 25 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Transportation & Logistics (2024-2032) ($MN)
  • Table 26 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Aerospace & Defense (2024-2032) ($MN)
  • Table 27 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Healthcare Facilities (2024-2032) ($MN)
  • Table 28 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Energy & Utilities (2024-2032) ($MN)
  • Table 29 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Oil & Gas (2024-2032) ($MN)
  • Table 30 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Telecommunications (2024-2032) ($MN)
  • Table 31 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Mining & Heavy Equipment (2024-2032) ($MN)
  • Table 32 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Smart Agriculture (2024-2032) ($MN)

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