<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Applying the condition monitoring technology to industrial processes can help detect faults in time, minimise 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 development of such technology for drive systems. Permanent magnet synchronous motors (PMSMs) have recently been used in many industries. Therefore, the issues of condition monitoring of PMSM drives are important. This study proposes and compares diagnostic schemes based on the stator phase currents (SPCSCs) signal for condition monitoring and fault diagnosis of PMSM stator winding faults. The continuous wavelet transform (CWT) 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 faults. The concept for an original and intelligent PMSM stator winding condition monitoring system is proposed.</p>
</abstract>

Applying the condition monitoring technology to industrial processes can help detect faults in time, minimise 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 development of such technology for drive systems. Permanent magnet synchronous motors (PMSMs) have recently been used in many industries. Therefore, the issues of condition monitoring of PMSM drives are important. This study proposes and compares diagnostic schemes based on the stator phase currents (SPCSCs) signal for condition monitoring and fault diagnosis of PMSM stator winding faults. The continuous wavelet transform (CWT) 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 faults. The concept for an original and intelligent PMSM stator winding condition monitoring system is proposed.

Condition Monitoring and Fault Diagnosis of Permanent Magnet Synchronous Motor Stator Winding Using the Continuous Wavelet Transform and Machine Learning

Wrocław University of Science and Technology

Power Electronics and Drives

This work is licensed under the Creative Commons Attribution 4.0 International License.

Applying the condition monitoring technology to industrial processes can help detect faults in time, minimise their impact and reduce the cost of unplanned...

Condition Monitoring and Fault Diagnosis of Permanent Magnet Synchronous Motor Stator Winding Using the Continuous Wavelet Transform and Machine Learning

Online veröffentlicht: 24. Feb. 2024

Seitenbereich: 106 - 121

Eingereicht: 07. Dez. 2023

Akzeptiert: 24. Jan. 2024

DOI: https://doi.org/10.2478/pead-2024-0007

Schlüsselwörter
fault diagnosis, interturn short circuits, continuous wavelet transform, machine learning, permanent magnet synchronous motor

© 2024 Przemysław Pietrzak et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Condition Monitoring and Fault Diagnosis of Permanent Magnet Synchronous Motor Stator Winding Using the Continuous Wavelet Transform and Machine Learning

Online veröffentlicht: 24. Feb. 2024

Seitenbereich: 106 - 121

Eingereicht: 07. Dez. 2023

Akzeptiert: 24. Jan. 2024

DOI: https://doi.org/10.2478/pead-2024-0007

Schlüsselwörterfault diagnosis, interturn short circuits, continuous wavelet transform, machine learning, permanent magnet synchronous motor

© 2024 Przemysław Pietrzak et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
fault diagnosis, interturn short circuits, continuous wavelet transform, machine learning, permanent magnet synchronous motor