AI-Powered Predictive Maintenance in Manufacturing: A Data-Driven Approach
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Abstract
Predictive maintenance powered by AI has transformed the manufacturing industry by reducing downtime and optimizing operational efficiency. This paper investigates the integration of machine learning algorithms with IoT sensors to predict equipment failures. It explores techniques such as time-series analysis, anomaly detection, and deep learning for real-time monitoring and fault prediction. The study highlights successful implementations across various industries, emphasizing cost savings and improved productivity. Challenges related to data quality, scalability, and integration with legacy systems are discussed, along with strategies for overcoming them.
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