Bearing Fault Diagnosis in Electric Motors: A Structured Review of Recent Methods and Engineering Trends

Rolling bearings are among the most failure-prone critical components in electric motors, and their operational conditions have a direct impact on the safety and reliability of motor systems. Owing to weak incipient fault characteristics, complex operating conditions, and diverse signal manifestations, bearing fault diagnosis has become a key research focus in the field of motor condition monitoring. This paper presents a structured review of representative recent methods for electric motor bearing fault diagnosis, with particular emphasis on vibration signals and motor current signals. From the perspectives of physical interpretability, signal representation, and data-driven learning, bearing fault diagnosis approaches based on fault mechanism modeling, feature extraction, and artificial intelligence (AI) are structurally organized and compared. In addition, data augmentation techniques and meta-learning methods for small-sample scenarios, as well as transfer learning (TL) methods for variable operating conditions and virtual-to-real transfer scenarios, are summarized. Through a comparative analysis of different technical routes, the key challenges and emerging trends in bearing fault diagnosis under complex operating conditions and practical engineering scenarios are identified, providing an engineering-oriented reference for method selection and future research.