The Impact of Machine Learning on Predictive Maintenance in Industrial Operations

This review paper examines the impact of machine learning (ML) on predictive maintenance in industrial operations, highlighting significant advancements in maintenance strategies. By employing algorithms like regression analysis, neural networks, and decision trees, ML enables the prediction of equipment failures with high accuracy, reducing downtime and operational costs. Integrating ML with predictive analytics and real-time data analysis provides deep insights into equipment behavior and failure patterns, facilitating proactive maintenance actions. Challenges such as data quality, high initial investment, and the need for specialized skills are discussed. Future trends, including advancements in ML algorithms, IoT and big data integration, and sustainability implications, are explored. The paper concludes with practical implications for industry practitioners and recommendations for further research to enhance predictive maintenance strategies.