Review Paper on - Learning for Predictive Maintenance in Industry

Predictive maintenance (PdM) has emerged as a transformative approach in modern industries to minimize unplanned downtime, optimize asset utilization, and reduce maintenance costs. With the increasing availability of industrial sensor data and advancements in computing power, machine learning (ML) has become a key enabler for effective PdM strategies. This paper presents a comprehensive review of recent literature on the application of ML techniques in predictive maintenance across various industrial domains. The study explores supervised, unsupervised, and reinforcement learning approaches used for anomaly detection, fault diagnosis, and remaining useful life (RUL) prediction. Key challenges such as data quality and availability, real-time processing, scalability, and model interpretability are critically discussed. Furthermore, the review highlights current trends, industrial case studies, and future research directions, emphasizing the role of ML in advancing Industry 4.0 initiatives. This work aims to provide researchers and practitioners with a consolidated understanding of state-of-the-art ML methodologies for predictive maintenance and their potential to enhance reliability and efficiency in industrial systems.