Enhancing Mechanical Systems Reliability through Machine Learning

Abstract

In today's Industry 4.0 era, ensuring the reliability and safety of mechanical systems is more critical than ever. Traditional maintenance methods—reactive repairs and scheduled preventive checks—often fall short in predicting and preventing unexpected failures, leading to costly downtime and safety risks. This paper delves into how advancements in machine learning (ML) are revolutionizing maintenance strategies by enabling predictive insights derived from real-time sensor data. It highlights various ML techniques, from anomaly detection to deep learning models, and their successful applications across sectors such as manufacturing, automotive, aerospace, and energy. Additionally, the report discusses key challenges encountered during implementation—including data quality issues, cybersecurity threats, interoperability, and model interpretability—and explores future trends aimed at creating more transparent, secure, and scalable ML systems. Embracing these intelligent approaches promises to optimize asset management, reduce operational costs, and foster safer, more efficient industrial environments.