Manufacturing Technology 2025, 25(4):559-568 | DOI: 10.21062/mft.2025.048

Fault Diagnosis of Electric Motor Rotor Systems Based on Feature Extraction and CNN-BiGRU-Attention

Mei Zhang ORCID..., Zilong Sun ORCID..., Wenchao Zheng ORCID...
School of Electrical and Information Engineering, Anhui University of Science & Technology, Huainan, 232001, China

To enhance the accuracy of fault diagnosis (FD) in motor rotor systems, this study introduces a novel method that leverages feature extraction (FE) combined with a CNN-BiGRU-Attention deep learning model. Initially, the time-domain features of the vibration signals are extracted using Variational Mode Decomposition (VMD), which also effectively denoises the data. Subsequently, the frequency-domain features of the vibration signals are extracted via Fast Fourier Transform (FFT). The aggregated features are then fed into the CNN-BiGRU-Attention model to perform fault classification. In this model, the Convolutional Neural Network (CNN) module extracts local spatial features, the Bidirectional Gated Recurrent Unit (BiGRU) module models the temporal dependencies, and the Attention mechanism enhances the focus on critical fault information, thereby improving the model's classification performance. Experimental results demonstrate that the proposed FD method achieves an accuracy of 99.58%. Compared to other commonly used models, the performance metrics of our model show significant advantages and superior performance.

Keywords: Electric motor rotor system, Fault diagnosis, Variational mode decomposition, Fast fourier transform, CNN-BiGRU-Attention model
Grants and funding:

This work was supported by National Natural Science Foundation of China under Grant 52374154

Received: April 30, 2025; Revised: September 10, 2025; Accepted: September 23, 2025; Prepublished online: October 22, 2025; Published: November 11, 2025  Show citation

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Zhang M, Sun Z, Zheng W. Fault Diagnosis of Electric Motor Rotor Systems Based on Feature Extraction and CNN-BiGRU-Attention. Manufacturing Technology. 2025;25(4):559-568. doi: 10.21062/mft.2025.048.
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