Research on the Application of Machine Learning in Fault Diagnosis Technology

Abstract

Modern industrial equipment is increasingly characterized by large scale, high complexity, and continuous operation. Traditional fault diagnosis methods exhibit significant limitations when dealing with massive volumes of data and various nonlinear relationships. Machine learning, which is capable of autonomously discovering patterns and identifying structures from data, provides a novel pathway for fault diagnosis. This paper systematically reviews the fundamental theories, commonly used algorithms, and practical application scenarios of machine learning in fault diagnosis, with particular attention to the applications of supervised learning and unsupervised learning in the fields of machinery, electric power, and transportation. It then discusses the current challenges, including poor data quality, limited model interpretability, and insufficient fault samples. Finally, future development directions are explored from the perspectives of explainable artificial intelligence, generative models, edge computing, and physics-informed data fusion. Overall, machine learning is driving fault diagnosis from a “repair-after-failure” paradigm toward “predictive maintenance.” However, for robust deployment in industrial environments, further efforts are still required in algorithm transparency, data efficiency, and cross-domain adaptability.