Detection of Inter-Turn Short Circuit in Stator Windings of Electric Machines Using Magnetic Symmetry Index and Machine Learning Methods

Electric machines have been in the spotlight as the transportation industry, along with many other high-impact industries, undergo the electrification process. While researchers debate whether one geometry or configuration has a higher power density, higher efficiency, or easier manufacturability, it is agreeable that nearly all the competing electric machines are symmetric. Symmetry can be used to develop a health index, whereas asymmetric signatures point to anomalies that stem from manufacturing imperfections, problems related to rotor faults, or inter-turn short circuits in the stator winding. This study takes a Switched Reluctance Motor (SRM) and an Induction Motor (IM) and analyzes their polar magnetic symmetry to introduce a stator health index. Moreover, this symmetry index in conjunction with a discriminative classifier is used to determine and classify the fault severity. Finally, this study presents a viable method to determine the health (in terms of inter-turn stator short circuit faults) of the motor and shows that support vector machine (SVM) and XGBoost can successfully classify the severity of an inter-turn short circuit fault for IM and SRM, respectively.