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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摘要滚动轴承是机械设备的关键零部件之一，其故障诊断对设备安全稳定运行至关重要。针对滚动轴承振动信号的非平稳特点，提出EMDPWVD时频图像联合改进Vision Transformer(ViT)网络模型的智能故障诊断新方法。首先针对实际信号研究短时傅里叶变换(Short-time Fourier Transform, STFT)、连续小波变换(Continuous Wavelet Transform, CWT)和经验模态分解联合伪魏格纳分布(Empirical Mode Decomposition & Pseudo-Wigner-ville Distribution, EMDPWVD)三种时频分析方法，考虑STFT和CWT无法同时获得高的时间分辨率和频率分辨率，优选EMDPWVD作为智能故障诊断网络的时频图像构造方法。其次，以经典ViT作为故障诊断基础模型，将时频图像按照预定尺寸分块并线性映射为输入序列，通过自注意力机制整合图像全局信息，借助堆叠Transformer编码器完成网络传输，进而实现故障诊断。为进一步提高故障诊断准确率，将池化层作为ViT的预处理网络，获得改进的Pooling ViT(PiT)模型，实现时频图像的空间特征延展，提升模型对输入图像敏感度。结果表明，所提方法对滚动轴承不同故障类型均有高的诊断准确率，PiT较ViT的准确率提高4.40%，证明对ViT加入池化层能够实现滚动轴承故障诊断效果提升。

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	樊红卫1
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	马宁阁1
	马嘉腾1
	陈步冉1
	曹现刚1
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	张旭辉1
	2

关键词 ：滚动轴承, 故障诊断, 时频图像, Vision Transformer, 池化层

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

收稿日期: 2023-05-17 出版日期: 2024-06-15

引用本文:

樊红卫1,2，马宁阁1，马嘉腾1，陈步冉1，曹现刚1,2，张旭辉1,2. 基于EMDPWVD时频图像和改进ViT网络的滚动轴承智能故障诊断[J]. 振动与冲击, 2024, 43(11): 246-254.
FAN Hongwei1,2, MA Ningge1, MA Jiateng1, CHEN Buran1, CAO Xiangang1,2, ZHANG Xuhui1,2. Intelligent fault diagnosis of rolling bearing based on EMDPWVD time-frequency images and improved ViT network. JOURNAL OF VIBRATION AND SHOCK, 2024, 43(11): 246-254.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2024/V43/I11/246

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