The global predictive maintenance market size was valued at USD 7.85 billion in 2022 and is expected to grow at a compound annual growth rate (CAGR) of 29.5% from 2023 to 2030. Integrating AI and ML into predictive maintenance prevents unplanned downtime and asset failures. AI-based preventive maintenance solutions include IoT hardware components that connect physical assets and an advanced analytics platform that helps predict failures and avoid unplanned downtime. IoT sensors, which are embedded in the equipment, collect various data, including environmental and manufacturing operations data, to determine component failure before breakdown. AI models can also predict patterns for failure modes of certain components. AI's major benefits in predictive maintenance include preventing production losses owing to faulty equipment, eliminating manual inspection, and enhancing workplace safety by automatically collecting data from machines in hard-to-reach places.

Digital twin technology offers a replica of the actual proof in digital format by collecting real-world data of the physical system or objects. It provides simulated output, for example, determining how various inputs would affect business equipment systems. Some major applications include visualization of products in real-time, troubleshooting remote equipment, connecting disparate systems and promoting traceability, and managing complexities and system-level linkages. For the use of digital twin in predictive maintenance, generally, certain criteria need to be considered, such as predictive problem, meaning there should be a target or an outcome to predict; recorded data must be appropriate and sufficient for supporting use cases; operational history, which includes both good and bad outcomes of problems, is required, and the businesses should have domain expertise.

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Predictive Maintenance Market Segments Highlights:

• Solution segment accounted for the largest share of 80.6% of the overall revenue in 2022. Predictive maintenance by solution entails adopting a software or technology solution that uses predictive analytics and data-driven information to improve tasks related to maintenance.
• Integrated segment accounted for the largest share of 75.1% of the overall revenue in 2022 owing to growing preference for predictive maintenance solutions that can be easily integrated with end users’ existing Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), and other software to increase responsiveness with real-time equipment monitoring, automate service procedures with AI, and leverage Big Data to obtain deeper insights, among others.
• Integration and deployment segment is estimated to occupy 42.6% of the market share in 2022. Owing to continued digitalization, growing technological awareness, and increasing adoption of predictive maintenance solutions, among other factors.
• On-premise segment is projected to occupy the largest market share of 75.8% in 2022 owing to benefits, such as better control and a high level of customization with on-premise installation. Several leading incumbents have been offering on-premise solutions. For instance, SAP SE offers on-premise editions of its predictive maintenance solutions.
• Large enterprises segment is expected to occupy 72.0% in 2022. A large enterprise that manufactures, sells, and distributes products to thousands of customers across a large supply chain requires powerful software that tracks, maintains, and provides real-time insights about assets.
• Vibration monitoring segment of the predictive maintenance market is expected to occupy 26.6% in 2022, owing to technological advancement of sensors, which enables accurate and real-time data from several types of equipment.
• Manufacturing segment of the predictive maintenance market is expected to occupy 27.9% in 2022. Using advanced analytics solutions such as predictive maintenance, digital technologies can help manufacturers reduce costs and increase production and efficiency.
• North American region dominated the market in 2022 and accounted for a market share of 34.81% in 2022. The growth can be attributed to the increasing adoption of advanced technologies such as Machine Learning (ML), acoustic monitoring, Artificial Intelligence (AI), and the Internet of Things (IoT), proliferation of customer channels, and growing concerns over asset maintenance and operational costs.

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Predictive Maintenance Market Segmentation

Grand View Research has segmented the global predictive maintenance market on the basis of component, solution, service, deployment, enterprise size, monitoring technique, end use and region:

Predictive Maintenance Component Outlook (Revenue, USD Billion; 2018 - 2030)

• Solution
• Service

Predictive Maintenance Solution Outlook (Revenue, USD Billion; 2018 - 2030)

• Integrated
• Standalone

Predictive Maintenance Service Outlook (Revenue, USD Billion; 2018 - 2030)

• Integration and Deployment
• Support & Maintenance
• Training & Consulting

Predictive Maintenance Deployment Model Outlook (Revenue, USD Billion; 2018 - 2030)

• Cloud
• On-premise

Predictive Maintenance Enterprise Size Outlook (Revenue, USD Billion; 2018 - 2030)

• Small & Medium Enterprises
• Large Enterprises

Predictive Maintenance Monitoring Technique Outlook (Revenue, USD Billion; 2018 - 2030)

• Torque Monitoring
• Vibration Monitoring
• Oil Analysis
• Thermography
• Corrosion Monitoring
• Others

Predictive Maintenance End Use Outlook (Revenue, USD Billion; 2018 - 2030)

• Aerospace & Defense
• Automotive & Transportation
• Energy & Utilities
• Healthcare
• IT & Telecommunications
• Manufacturing
• Oil & Gas
• Others

Predictive Maintenance Regional Outlook (Revenue, USD Billion; 2018 - 2030)

• North America
• Europe
• Asia Pacific
• Latin America
• Middle East & Africa

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