AI-Based Predictive Maintenance Market is expected to reach US$ 60.2 million by 2030

The AI-based predictive maintenance market accounted for USD 9.2 million in 2023-e driven by the expansion of the healthcare industry and advanced medical care. The overall demand for the AI-based predictive maintenance market is projected to expand at a CAGR of 34.4% from 2024 to 2030, totaling around USD 60.2 million by 2030. The significance of predictive maintenance in enhancing productivity within factories cannot be overstated. The widespread adoption of predictive maintenance solutions is rapidly gaining traction across both large enterprises and small to medium-sized enterprises (SMEs). This surge in adoption can be attributed to a myriad of advantages, such as diminished downtime, prolonged equipment lifespan, heightened plant safety, optimized maintenance schedules, diminished maintenance costs, and an enhanced yield rate.

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Predictive maintenance relies on various technologies including the Internet of Things (IoT), predictive analytics, and artificial intelligence (AI). Connected sensors gather data from assets such as machinery and equipment. AI and machine learning are used to analyze the data in real time to build a picture of the current condition of the equipment. Thereafter, triggering an alert if any potential defect is identified and delivering it to the maintenance team. As well as providing defect warnings, advances in machine learning algorithms enable predictive maintenance solutions to make predictions about the future condition of equipment. These can be used to drive greater efficiency in maintenance-related workflows and processes such as just-in-time work order scheduling and labor and parts supply chains.

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Benefits from a predictive maintenance strategy center around anticipating equipment faults and failures, reducing maintenance and operating costs by optimizing time and resources, and improving the performance and reliability of equipment. Deloitte reported in 2022 that predictive maintenance can result in a 5-15% reduction in facility downtime and a 5-20% increase in labor productivity.1 Predictive maintenance also has a beneficial impact on operational sustainability by minimizing energy usage and waste.

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AI is very effective at performing predictive analytics because it can collect, organize, and analyze data quickly. AI predictive analytics models will be able to generate a solution based on millions of data points in a matter of minutes. Of course, the capabilities of AI extend far beyond predictive analytics. With artificial intelligence and machine learning, organizations can apply predictive maintenance to their operation, processing huge amounts of sensor data to detect equipment failure before it happens.

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