Abstract
The application of AI-driven predictive analytics in industrial maintenance is transforming how manufacturers manage equipment reliability and minimize downtime. By leveraging real-time data from IoT sensors, machine learning algorithms, and historical maintenance records, predictive analytics can forecast equipment failures before they occur, enabling timely and cost-effective interventions. This paper explores the role of AI-driven predictive analytics in industrial maintenance, discussing its applications, benefits, challenges, and real-world case studies. We examine the key technologies, including machine learning, data analytics, and condition monitoring, that power predictive maintenance systems, and explore the impact of these technologies on operational efficiency, cost savings, and equipment life cycle management.
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