@Article{cmes.2026.079927,
AUTHOR = {Yue Su, Shukuan Zhang, Jinghao Jiao, Jiankang Zhong, Qianxi Zhao},
TITLE = {An Improved Support Vector Machine Method for Fault Diagnosis of Inter-Turn Short Circuit in PMSM with Enhanced Fault Representation},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {147},
YEAR = {2026},
NUMBER = {1},
PAGES = {0--0},
URL = {http://www.techscience.com/CMES/v147n1/67151},
ISSN = {1526-1506},
ABSTRACT = {This paper introduces a novel dual-layer optimization fault diagnosis framework for inter-turn short-circuit (ITSC) faults in permanent magnet synchronous motors (PMSMs). The synergistic of a SABO-optimized VMD for enhanced feature extraction and an MFO-optimized SVM for intelligent classification is proposed. Firstly, mathematical and simulation models of ITSC faults in PMSMs are established to obtain fault phase currents and motor electromagnetic torques as characteristic fault signals. Then, the SABO algorithm is used to optimize the VMD parameters, followed by VMD decomposition of the characteristic fault signals to obtain Intrinsic Mode Functions (IMFs), and the time-domain parameters of the optimal IMF are calculated to obtain feature vectors. Finally, the fault type is predicted using an SVM optimized by the Moth-Flame Optimizer (MFO). Simulation results show that the accuracy of fault diagnosis can reach 93.6%, indicating that the proposed method can achieve accurate diagnosis of ITSC faults and effectively improve the accuracy of fault diagnosis.},
DOI = {10.32604/cmes.2026.079927}
}