Condition monitoring and fault diagnosis of permanent magnet synchronous motor stator winding using continuous wavelet transform and machine learning
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Wroclaw University of Technology
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Marcin Wolkiewicz   

Wroclaw University of Technology
Power Electronics and Drives 2024;9 (44)
Applying condition monitoring technology to industrial processes can help to detect faults in time, minimize their impact and reduce the cost of unplanned downtime. Since the introduction of the Industry 4.0 paradigm, many companies have been investing in the develop-ment of such technology for drive systems. Permanent magnet synchronous motors (PMSMs) have recently been used in many indus-tries. Therefore, the issues of condition monitoring of PMSM drives are important. This study proposes and compares diagnostic schemes based on the stator phase currents signal for condition monitoring and fault diagnosis of PMSM stator winding faults. The con-tinuous wavelet transform is used for the extraction of the symptoms of interturn short circuits in PMSM stator winding. Machine learning algorithms are applied to automate the detection and classification of the fault. The concept for an original and intelligent PMSM stator winding condition monitoring system is proposed.
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