Machine Learning-Based Stator Current Data-Driven PMSM Stator Winding Fault Diagnosis

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 24; p. 9668
• Main Authors: Pietrzak, Przemyslaw, Wolkiewicz, Marcin
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 10.12.2022; MDPI
• Subjects: Algorithms; artificial intelligence; Automation; condition monitoring; Data mining; Embedded systems; Fault diagnosis; Fourier transforms; interturn short circuits; Machine learning; Magnets, Permanent; Neural networks; permanent magnet synchronous motor; Popularity; Signal processing; Support vector machines; Wavelet transforms; fault diagnosis; permanent magnet synchronous motor; condition monitoring; machine learning; short-time Fourier transform; artificial intelligence; interturn short circuits
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22249668

Permanent magnet synchronous motors (PMSMs) have become one of the most important components of modern drive systems. Therefore, fault diagnosis and condition monitoring of these machines have been the subject of many studies in recent years. This article presents an intelligent stator current-data driven PMSM stator winding fault detection and classification method. Short-time Fourier transform is applied in the process of fault feature extraction from the stator phase current symmetrical components signal. Automation of the fault detection and classification process is carried out with the use of three selected machine learning algorithms: support vector machine, naïve Bayes classifier and multilayer perceptron. The concept and online verification of the original intelligent fault diagnosis system with the potential of a real industrial deployment are demonstrated. Experimental results are presented to evaluate the effectiveness of the proposed methodology.