Training Scheme for Convolutional Neural Network Based Multiple Fault Classifier of Permanent Magnet Synchronous Motors Under Variable Speed and Load Conditions
Authors:
- 1 September 2024
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-7
Abstract
This paper introduces a novel methodology for training Convolutional Neural Network models to detect multiple faults in permanent magnet synchronous motors (PMSMs). The proposed method aims to mitigate memory overloading and overfitting for a neural network. Within the framework, measured current and flux signals from a PMSM under variable speed condition in time domain are converted into spectrogram images using short-time Fourier transformation. The suggested algorithm can classify inter-turn short-circuit faults, local de-magnetization faults, and mixed-fault in PMSMs. The proposed scheme is validated using data from an in-house experimental setup.Keywords
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