Fault diagnosis method for bearing based on fusing CNN and ViT

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Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (3) : 158-163.

NING Fangli, WANG Ke, HAO Mingyang

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Abstract

To the characteristics of small amount of data and non-stationary fault signal of bearing fault diagnosis, a bearing fault diagnosis method combining short-time Fourier transform, convolutional neural network and vision transformer is proposed. Firstly, the origin acoustic signal is converted by short-time Fourier transformer into a time-frequency image containing timing information and frequency information. Secondly, the time-frequency image is used as the input of the convolution neural network, which is used to implicitly extract the deep features of the image, and its output is the input of the vision transformer. And the vision transformer is used to extract time series information. Finally, the output layer is used to realize the pattern recognition of multiple faults of the bearing using Softmax function. The experimental results show that this method has a high accuracy rate for bearing fault diagnosis. In order to better explain and optimize the proposed bearing fault diagnosis method, the classification features are visualized by the t-distribution domain embedding algorithm.

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Short-time Fourier transformer / Convolution neural network / Vision transformer / t-Distributed stochastic neighbor embedding algorithm

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NING Fangli, WANG Ke, HAO Mingyang. Fault diagnosis method for bearing based on fusing CNN and ViT[J]. Journal of Vibration and Shock, 2024, 43(3): 158-163

References

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