Machine learning approaches for equipment failure prediction and predictive maintenance: a comprehensive review

Abstract

This comprehensive review explores the application of machine learning techniques in predicting equipment failures and facilitating predictive maintenance strategies. Drawing from recent literature and case studies, the paper examines various machine learning algorithms and methodologies employed in this domain. Key findings highlight the effectiveness of machine learning models in pre emptively identifying potential equipment failures, thereby enhancing maintenance practices and minimizing downtime. Implications for industries reliant on machinery and suggestions for future research directions are discussed