Spectrum Transition Entropy: An Approach for Bearing Fault Diagnosis of Traction Motor

Accurate fault diagnosis of traction motor bearing is a necessary and indispensable part of the prognostics and health management of high-speed trains. Vibration signal analysis is usually utilized to diagnose bearing faults. However, challenges under actual train operations including strong multi-domain variability, high spectral complexity, high spectral uncertainty, and weak fault characteristics of vibration signals hinder accurate fault diagnosis. Thus, a novel fault diagnosis method based on spectrum transition entropy (SpTE) is proposed. Firstly, a symbolization method is constructed to represent the time-frequency distribution (TFD) of signals, completely considering spectral amplitude and phase information. Besides, to address the strong multi-domain variability, the transition probability matrix is introduced to describe the state transition process of the spectrum over time in the symbolic TFD (STFD). Moreover, to accurately quantify high spectral complexity and spectral uncertainty, the entropy value is calculated using the joint probability distribution of the symbolic state and its one-step transition state. Furthermore, a parameter optimization strategy is designed to enable the adaptive determination of SpTE parameters for enhancing sensitivity to weak faults. Finally, these extracted optimized features are identified using a classifier to achieve fault classification. The effectiveness of the proposed method is verified by three cases. The highest fault recognition rates are 99.67%, 99.30%, and 100%, respectively, which are better than baseline methods. The experimental results show that the proposed method has higher accuracy and provide a new approach for diagnosing traction motor rolling bearings.