Machine Learning Approaches for Fault Detection and Diagnosis in Mechanical Systems

Abstract

Fault detection and diagnosis (FDD) are paramount in maintaining the operational integrity and efficiency of mechanical systems across various industries. Traditional FDD methods, heavily reliant on manual inspections, scheduled maintenance, and simple threshold-based algorithms, are increasingly unable to meet the demands of modern, complex systems. These methods often lead to significant downtime, high maintenance costs, and in some cases, catastrophic failures due to their reactive nature and inability to predict and prevent faults before they occur. Furthermore, traditional approaches struggle with the analysis of large-scale data from sensors, leading to delayed or inaccurate fault detection. The advent of machine learning (ML) offers a revolutionary solution to these challenges, bringing forth the ability to analyze vast amounts of data in real-time, learn from historical trends, and predict future failures with high accuracy. ML algorithms can process and interpret data from a multitude of sensors embedded in mechanical systems, enabling predictive maintenance and significantly improving system reliability and efficiency. Unlike traditional methods, ML-based FDD approaches are dynamic, learning continuously from new data, and adapting to changes in system behavior without explicit reprogramming. This adaptability is crucial for the longevity and sustainability of mechanical systems in an era of rapid technological advancements. This paper delves into the integration of machine learning in fault detection and diagnosis, presenting a comprehensive study that not only highlights the shortcomings of traditional FDD methods but also showcases the superiority of ML-based approaches through theoretical exploration, methodology, and case studies. We examine various ML algorithms tailored to different types of faults and mechanical systems, providing insights into their implementation and effectiveness. Our research contributes to the existing body of knowledge by offering a detailed comparison of traditional and ML-based FDD methods, identifying best practices for implementing ML in mechanical systems, and outlining future directions for research. By bridging the gap between conventional methods and the potential of machine learning, this paper aims to pave the way for more reliable, efficient, and intelligent maintenance strategies in the mechanical industry.