Predictive maintenance has emerged as a pivotal strategy within modern industrial settings, aimed at improving machinery reliability and operational efficiency by predicting potential failures before they occur. Machine Learning (ML) plays an instrumental role in this paradigm shift by enabling data-driven maintenance decisions that were previously infeasible with traditional methods. This review article presents a comprehensive examination of the applications, benefits, and challenges associated with the integration of machine learning techniques in predictive maintenance of industrial machinery. This investigation explores various ML algorithms utilized in anomaly detection, failure prediction, and lifespan estimation. Furthermore, the review highlights the importance of high-quality data acquisition and preprocessing, model selection, and the deployment of predictive systems. By systematically assessing recent advancements, this article elucidates current trends and provides insights into future research directions, thereby serving as a valuable resource for researchers and practitioners in the field of industrial maintenance.
Arimondo Scrivano (Thu,) studied this question.