Research on Intelligent Diagnosis Algorithm for Electrical Faults of Induction Motor Stator and Rotor Based on Coordinate Transformation and Support Vector Machine

As the key power source of train traction system, the safety and reliability of traction asynchronous motor operation is crucial. To improve the efficiency of vehicle maintenance, online monitoring of the working state of traction asynchronous motors has received increasing attention. This paper proposes a diagnosis algorithm based on the combination of coordinate transformation and support vector machine for traction asynchronous motors. Firstly, coordinate transformation can convert power frequency components into direct flow, facilitating the extraction of fault features. Then, from the perspective of data driven, this paper collects historical fault data under varying operating conditions and loads, and designs an intelligent fault diagnosis framework. Finally, a fault recognition classifier is trained based on support vector machines to identify stator inter turn short circuit and rotor bar breakage faults of traction asynchronous motors, and to guide accurate vehicle maintenance.