基于EMDPWVD时频图像和改进ViT网络的滚动轴承智能故障诊断

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Abstract Rolling bearing is one of the key components of mechanical equipment, and its fault diagnosis is crucial for the safe and stable operation of equipment. For rolling bearings with non-stationary vibration signal, a new intelligent fault diagnosis method of EMDPWVD time-frequency images combined with improved Vision Transformer(ViT) network model is proposed. For the actual signals, three time-frequency analysis methods as Short-time Fourier Transform(STFT), Continuous Wavelet Transform(CWT), and Empirical Mode Decomposition & Pseudo-Wigner-ville Distribution(EMDPWVD) are firstly studied. Considering that STFT and CWT cannot simultaneously achieve the high time and frequency resolution, EMDPWVD is selected as the time-frequency image preparation method used for the intelligent fault diagnosis network. Secondly, a typical ViT is used as the basic fault diagnosis model, which divides time-frequency images into the blocks with predetermined size and then linearly maps them into input sequences. Meanwhile, the global information of the images is integrated by a self attention mechanism, and the network transmission is completed using a stacked Transformer encoder to finally achieve the fault diagnosis. To further improve the accuracy of fault diagnosis, the pooling layer is used as the preprocessing network of ViT to obtain an improved Pooling Vision Transformer(PiT) model, which extends the spatial features of time-frequency images and enhances the sensitivity of the model to the input images. The results show that the proposed method has the high diagnosis accuracy for different fault types of rolling bearings, and the accuracy of PiT is 4.40% higher than that of ViT, which proves that adding pooling layers to ViT can improve the effect of rolling bearing fault diagnosis.

Key words： Rolling bearing Fault diagnosis Time-frequency image Vision Transformer(ViT) Pooling layer

Received: 17 May 2023 Published: 15 June 2024

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	Articles by authors
	FAN Hongwei1
	2
	MA Ningge1
	MA Jiateng1
	CHEN Buran1
	CAO Xiangang1
	2
	ZHANG Xuhui1
	2

Cite this article:

FAN Hongwei1,2,MA Ningge1, et al. Intelligent fault diagnosis of rolling bearing based on EMDPWVD time-frequency images and improved ViT network[J]. JOURNAL OF VIBRATION AND SHOCK, 2024, 43(11): 246-254.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2024/V43/I11/246

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