Machine Learning for Predictive Maintenance in Industrial Systems

Abstract

Predictive maintenance (PdM) has emerged as a transformative approach in industrial systems, aimed at minimizing equipment downtime, optimizing maintenance schedules, and improving operational efficiency. Traditional maintenance techniques often lead to either excessive maintenance or unexpected equipment failures. Machine learning (ML) offers a promising avenue to address these limitations by leveraging historical and real-time data to anticipate equipment failures before they occur. This research paper explores the integration of machine learning algorithms for predictive maintenance in industrial environments, discussing the various types of ML models used, data acquisition challenges, model evaluation techniques, and experimental validation. Through extensive experimentation using real-world datasets, we demonstrate that ML-based predictive models significantly outperform traditional methods in predicting faults and optimizing maintenance schedules. The results underscore the potential of ML to drive cost savings, enhance equipment reliability, and ensure the safety of industrial operations.