Cira, FerhatArkan, MuslumGumus, Bilal2024-08-042024-08-042015978-1-4799-7743-7https://hdl.handle.net/11616/9719710th IEEE International Symposium on Diagnostics for Electrical Machines, Power Electronics, and Drives (SDEMPED) -- SEP 01-04, 2015 -- Guarda, PORTUGALIn this paper, detection of the stator winding inter-turn short circuit fault (SWISCF) in surface-mounted permanent magnet synchronous motors (SPMSMs) and classification of the fault severity via pattern recognition system (PRS) are presented. In order to automatically detect stator winding short circuit fault and to estimate severity of this fault, artificial neural network (ANN) based PRS has been used. It has been observed that the amplitude of the 3rd harmonics of the current is the most distinctive characteristic for detecting the short circuit fault ratio of the SPMSM. To increase the fault clasification accuracy of PRS both fundamental (1st) and 3rd harmonics are used. In order to validate proposed method experimental results are presented.eninfo:eu-repo/semantics/closedAccessPermanent Magnet Synchronous MotorsPattern Recognition SystemFault DetectionCondition MonitoringDigital Signal ProcessingFault Severity ClassificationConference Object3163212-s2.0-84959284010N/AWOS:000381495800047N/A