Predictive Maintenance for Industrial Equipments Using ML & DL

This study explores the impact of artificial intelligence on predictive maintenance (PdM) in smart manufacturing. It provides insights for students, organizations, and academic institutions by examining data-driven PdM approaches. The paper introduces a new PdM framework designed for automatic washing machines, addressing unique research challenges. The study categorizes industrial PdM applications based on machine learning and deep learning methods, with a focus on performance indicators. Recent advancements favor deep learning, revealing innovative algorithms with the potential to transform PdM. The importance of defect identification for cost savings is emphasized, along with the use of unsupervised learning in situations with limited historical data. The solutions discussed aim to reduce downtime, increase equipment availability, and promote sustainability by extending the lifespan of critical machine components. The research highlights the potential of data-driven modeling in monitoring tool wear and bearing failures, showcasing AI's trans formative impact on manufacturing maintenance practices.