Application of continuous wavelet transform and convolutional neural networks in fault diagnosis of PMSM stator windings

Efficiency, reliability, and durability play a key role in modern drive systems in line with the Industry 4.0 paradigm and the sustainability trend. To ensure this, highly efficient motors and appropriate systems must be deployed to monitor their condition and diagnose faults during the operation. F...

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Bibliographic Details
Published in:Bulletin of the Polish Academy of Sciences. Technical sciences 2024-04, Vol.72 (5), p.150202-150202
Main Authors: Pietrzak, P., Wolkiewicz, M.
Format: Article
Language:eng ; pol
Subjects:
Artificial neural networks
Classification
Coils (windings)
Continuous wavelet transform
Effectiveness
Fault detection
Fault diagnosis
Industrial applications
Industry 4.0
Network reliability
Neural networks
Permanent magnets
Process parameters
Short circuits
Stators
Synchronous motors
System reliability
Wavelet analysis
Wavelet transforms
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Summary:Efficiency, reliability, and durability play a key role in modern drive systems in line with the Industry 4.0 paradigm and the sustainability trend. To ensure this, highly efficient motors and appropriate systems must be deployed to monitor their condition and diagnose faults during the operation. For these reasons, in recent years, more and more research has been focused on developing new methods for fault diagnosis of permanent magnet synchronous motors (PMSMs). This paper proposes a novel hybrid method for the automatic detection and classification of PMSM stator winding faults based on combining the continuous wavelet transform (CWT) analysis of the negative sequence component of the stator phase currents with a convolutional neural network (CNN). CWT scalogram images are used as the inputs of the CNN-based interturn short circuits fault classifier model. Experimental tests were carried out to verify the effectiveness of the proposed approach under various motor operating conditions and at an incipient stage of fault propagation. In addition, the effects of the input image format, CNN structure, and training process parameters on model accuracy and classification effectiveness were investigated. The results of the experimental tests confirmed the high effectiveness of fault detection (99.4%) and classification (97.5%), as well as other important advantages of the developed method.
ISSN:2300-1917
0239-7528
2300-1917
DOI:10.24425/bpasts.2024.150202