Classifying the Percentage of Broken Magnets in Permanent Magnet Synchronous Motors Using Combined Short-Time Fourier Transform and a Pre-Trained Convolutional Neural Network

Amin Ghafouri Matanagh (), Salih Baris Ozturk (), Taner Goktas and Omar Hegazy
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Amin Ghafouri Matanagh: Department of Electrical Engineering, Istanbul Technical University, Istanbul 34469, Türkiye
Salih Baris Ozturk: Department of Electrical Engineering, Istanbul Technical University, Istanbul 34469, Türkiye
Taner Goktas: Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir 35160, Türkiye
Omar Hegazy: MOBI Research Group, ETEC Department, Vrije Universiteit Brussel (VUB), 1050 Brussels, Belgium

Energies, 2024, vol. 17, issue 2, 1-17

Abstract: In critical applications of electrical machines, ensuring validity and safety is paramount to prevent system failures with potentially hazardous consequences. The integration of machine learning (ML) technologies plays a crucial role in monitoring system performance and averting failures. Among various motor types, permanent magnet synchronous motors (PMSMs) are widely favored for their versatile speed range, enhanced power density, and ease of control, finding applications in both industrial settings and electric vehicles. This study focuses on the detection and classification of the percentage of broken magnets in PMSMs using a pre-trained AlexNet convolutional neural network (CNN) model. The dataset was generated by combining finite element methods (FEMs) and short-time Fourier transform (STFT) applied to stator phase currents, which exhibited significant variations due to diverse broken magnet structures. Leveraging transfer learning, the pre-trained AlexNet model underwent adjustments, including the elimination and rearrangement of the final three layers and the introduction of new layers tailored for electrical machine applications. The resulting pre-trained CNN model achieved a remarkable performance, boasting a 99.94% training accuracy and 0.0004% training loss in the simulation dataset, utilizing a PMSM with 4% magnet damage for experimental validation. The model’s effectiveness was further affirmed by an impressive 99.95% area under the receiver operating characteristic (ROC) curve in the experimental dataset. These results underscore the efficacy and robustness of the proposed pre-trained CNN method in detecting and classifying the percentage of broken magnets, even with a limited dataset.

Keywords: convolutional neural network (CNN); fault diagnostics; permanent magnet synchronous motors (PMSMs); broken magnet; transform learning; signal processing (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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