Cira, FerhatArkan, MuslumGumus, Bilal2024-08-042024-08-0420161975-01022093-7423https://doi.org/10.5370/JEET.201.6.11.2.416https://hdl.handle.net/11616/102775In this study, automatic detection of stator winding inter-turn short circuit fault (SWISCFs) in surface-mounted permanent magnet synchronous motors (SPMSMs) and automatic classification of fault severity via a pattern recognition system (PRS) are presented. In the case of a stator short circuit fault, performance losses become an important issue for SPMSMs. To detect stator winding short circuit faults automatically and to estimate the severity of the fault, an artificial neural network (ANN)-based PRS was used. It was found that the amplitude of the third harmonic of the current was the most distinctive characteristic for detecting the short circuit fault ratio of the SPMSM. To validate the proposed method, both simulation results and experimental results are presented.eninfo:eu-repo/semantics/closedAccessCondition monitoringDigital signal processingFault detectionFault severity classificationPattern recognition systemPermanent magnet synchronous motorsArticle11241642410.5370/JEET.201.6.11.2.416WOS:000370909800017Q4