重参数化VGG网络在滚动轴承故障诊断中的应用研究

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振动与冲击 ›› 2023, Vol. 42 ›› Issue (11) : 313-323.

论文

丁汕汕1，陈仁文1，黄翊君1，刘飞1，刘昊1，肖安2

作者信息 +

Application study of reparameterized VGG network in rolling bearing fault diagnosis

DING Shanshan1， CHEN Renwen1， HUANG Yijun1， LIU Fei1， LIU Hao1， XIAO An2

Author information +

文章历史 +

摘要

基于神经网络的滚动轴承故障诊断方法训练时，存在诊断准确率低和易受到变工况噪声干扰的问题，提出一种基于重参数化VGG（RepVGG）滚动轴承故障诊断方法。首先，为满足神经网络对数据量的要求，采用数据增强技术来扩充原始数据，然后，使用短时傅里叶变换（STFT）对原始的振动信号处理成单通道时频图，并使用伪彩色处理技术转换成三通道时频图，进一步将数据输入到RepVGG网络的不同结构中进行滚动轴承的故障诊断。最后，在凯斯西储大学滚动轴承数据集（CWRU）上开展实验验证，实验结果表明，RepVGG在变工况及噪声干扰下的平均诊断准确率分别为98.02%、95%以上，高于基于VGG、ResNet的故障诊断模型，有较高的故障诊断准确率且泛化性更好。

Abstract

Training time of neural network based rolling bearing fault diagnosis method, there are problems of low diagnostic accuracy and susceptibility to interference from variable operating noise, a rolling bearing fault diagnosis method based on reparameterized VGG (RepVGG) is proposed to solve these problems. Firstly, to meet the data volume requirement of the neural network, the data enhancement technique is used to expand the original data. Then, the original vibration signal is processed into a single-channel time-frequency map using the short-time Fourier transform (STFT) and converted into a three-channel time-frequency map using the pseudo-color processing technique, and the data are further input into different structures of the RepVGG network for the fault diagnosis of rolling bearings. Finally, experimental validation is carried out on the rolling bearing dataset (CWRU) of Case Western Reserve University, and the experimental results show that the average diagnostic accuracy of RepVGG under variable operating conditions and noise interference is 98.02% and over 95%, respectively, which is higher than that of VGG and ResNet fault diagnosis models, with higher fault diagnosis accuracy and stronger generalization.

导出引用

丁汕汕1，陈仁文1，黄翊君1，刘飞1，刘昊1，肖安2. 重参数化VGG网络在滚动轴承故障诊断中的应用研究[J]. 振动与冲击, 2023, 42(11): 313-323

DING Shanshan1， CHEN Renwen1， HUANG Yijun1， LIU Fei1， LIU Hao1， XIAO An2. Application study of reparameterized VGG network in rolling bearing fault diagnosis[J]. Journal of Vibration and Shock, 2023, 42(11): 313-323

参考文献

[1] Li Y, Ding K, He G, et al. Non-stationary vibration feature extraction method based on sparse decomposition and order tracking for gearbox fault diagnosis[J]. Measurement, 2018, 124: 453–469.
[2] Zhao R, Yan R, Chen Z, et al. Deep learning and its appli-cations to machine health monitoring[J]. Mechanical Systems and Signal Processing, 2019, 115: 213–237.
[3] Liu R, Yang B, Zio E, et al. Artificial intelligence for fault diagnosis of rotating machinery: A review[J]. Mechanical Systems and Signal Processing, 2018, 108: 33–47.
[4] Haidong S, Hongkai J, Xingqiu L, et al. Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine[J]. Knowledge-Based Systems, 2018, 140: 1–14.
[5] Cai B, Liu H, Xie M. A real-time fault diagnosis method-ology of complex systems using object-oriented Bayesian networks[J]. Mechanical Systems and Signal Processing, 2016, 80: 31–44.
[6] Cai B, Shao X, Liu Y, et al. Remaining Useful Life Esti-mation of Structure Systems Under the Influence of Multiple Causes: Subsea Pipelines as a Case Study[J]. IEEE Transac-tions on Industrial Electronics, 2020, 67(7): 5737–5747.
[7] Zhang X, Zhou J. Multi-fault diagnosis for rolling element bearings based on ensemble empirical mode decomposition and optimized support vector machines[J]. Mechanical Systems and Signal Processing, 2013, 41(1–2): 127–140.
[8] Huang J, Hu X, Yang F. Support vector machine with genetic algorithm for machinery fault diagnosis of high voltage circuit breaker[J]. Measurement, 2011, 44(6): 1018–1027.
[9] Schmidhuber J. Deep learning in neural networks: An overview[J]. Neural Networks, 2015, 61: 85–117.
[10] Dong S, Xu X, Chen R. Application of fuzzy C-means method and classification model of optimized K-nearest neighbor for fault diagnosis of bearing[J]. Journal of the Bra-zilian Society of Mechanical Sciences and Engineering, 2016, 38(8): 2255–2263.
[11] Dong S, Luo T, Zhong L, et al. Fault diagnosis of bearing based on the kernel principal component analysis and opti-mized k-nearest neighbour model[J]. Journal of Low Fre-quency Noise, Vibration and Active Control, 2017, 36(4): 354–365.
[12] 雷子豪, 温广瑞, 周桥, 等. 基于MMDFE-DA的滚动轴承故障诊断方法[J]. 振动.测试与诊断, 2022, 42(01): 182-189+203.
LEI Zihao.WEN Guangrui, ZHOU Qiao, et al. MMDFE-DA based rolling bearing fault diagnosis method[J]. Vibration. Test and Diagnosis, 2022, 42(01): 182-189+203.
[13] 潘鹏飞. 基于试飞数据的航空发动机状态监测与故障诊断[J]. 推进技术, 2021, 42(12): 2826–2837.
PAN Pengfei. Aero-engine condition monitoring and fault diagnosis based on test flight data[J]. Propulsion Technology, 2021, 42(12): 2826-2837.
[14] Jia F, Lei Y, Lin J, et al. Deep neural networks: A prom-ising tool for fault characteristic mining and intelligent diag-nosis of rotating machinery with massive data[J]. Mechanical Systems and Signal Processing, 2016, 72–73: 303–315.
[15] Lu C, Wang Z-Y, Qin W-L, et al. Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification[J]. Signal Processing, 2017, 130: 377–388.
[16] Xie J, Du G, Shen C, et al. An End-to-End Model Based on Improved Adaptive Deep Belief Network and Its Applica-tion to Bearing Fault Diagnosis[J]. IEEE Access, 2018, 6: 63584–63596.
[17] Jiang H, Shao H, Chen X, et al. A feature fusion deep belief network method for intelligent fault diagnosis of rotating machinery[J]. Journal of Intelligent & Fuzzy Systems, 2018, 34(6): 3513–3521.
[18] Chen Z, Gryllias K, Li W. Mechanical fault diagnosis using Convolutional Neural Networks and Extreme Learning Machine[J]. Mechanical Systems and Signal Processing, 2019, 133: 106272.
[19] Chen Z, Mauricio A, Li W, et al. A deep learning method for bearing fault diagnosis based on Cyclic Spectral Coherence and Convolutional Neural Networks[J]. Mechanical Systems and Signal Processing, 2020, 140: 106683.
[20] Jia F, Lei Y, Lu N, et al. Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization[J]. Mechanical Systems and Signal Processing, 2018, 110: 349–367.
[21] Jia F, Lei Y, Guo L, et al. A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines[J]. Neurocomputing, 2018, 272: 619–628.
[22] Zhang J, Sun Y, Guo L, et al. A new bearing fault diag-nosis method based on modified convolutional neural net-works[J]. Chinese Journal of Aeronautics, 2020, 33(2): 439–447.
[23] Zhao Z, Zhang Q, Yu X, et al. Applications of Unsuper-vised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1–28.
[24] 刘飞, 陈仁文, 邢凯玲, 等. 基于迁移学习与深度残差网络的滚动轴承快速故障诊断算法[J]. 振动与冲击, 2022, 41(03): 154–164.
LIU Fei, CHEN Renwen, XING Kailin, et al. Fast Fault Di-agnosis Algorithm for Rolling Bearings Based on Transfer Learning and Deep Residual Network[J]. Journal of Vibration and Shock,2022,41(03):154-164.
[25] Ding X, Zhang X, Ma N, et al. RepVGG: Making VGG-style ConvNets Great Again[J]. ArXiv:2101.03697 [Cs], 2021[2022-04-19].
[26] Kingma D P, Ba J. Adam: A Method for Stochastic Op-timization[Z]. arXiv, 2017(2017-01-29)[2022-08-15].
Adam.
[27] 谷玉海, 朱腾腾, 饶文军, 等. 基于Adam-DBN网络的行星齿轮箱故障诊断方法研究[J]. 组合机床与自动化加工技术, 2020(4): 118–122.
GU Yuhai, ZHU Tengteng, RAO Wenjun, et al. Research on the fault diagnosis method of planetary gearbox based on Adam-DBN network[J]. Modular Machine Tool & Automatic Manufacturing Technique,2020(4): 118–122.
[28] He Y, Tang H, Ren Y, et al. A deep multi-signal fusion adversarial model based transfer learning and residual network for axial piston pump fault diagnosis[J]. Measurement, 2022, 192: 110889.
[29] Su H, Xiang L, Hu A, et al. A novel method based on meta-learning for bearing fault diagnosis with small sample learning under different working conditions[J]. Mechanical Systems and Signal Processing, 2022, 169: 108765.
[30] Huo C, Jiang Q, Shen Y, et al. New transfer learning fault diagnosis method of rolling bearing based on ADC-CNN and LATL under variable conditions[J]. Measurement, 2022, 188: 110587.
[31] Selvaraju R R, Cogswell M, Das A, 等. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization[J]. International Journal of Computer Vision, 2020, 128(2): 336–359.
[32] Simonyan K, Zisserman A. VERY DEEP CONVOLU-TIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION[J]. 2015: 14. .
[33] He K, Zhang X, Ren S, et al. Deep Residual Learning for Image Recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, USA: IEEE, 2016: 770–778[2022-03-08].
[34] Liu C, Wang W, Wang M, et al. An efficient instance selection algorithm to reconstruct training set for support vector machine[J]. Knowledge-Based Systems, 2017, 116: 58–73.
[35] Zhang W, Li C, Peng G, et al. A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load[J]. Mechanical Systems and Signal Processing, 2018, 100: 439–453.