基于GAF-CapsNet的电机轴承故障诊断方法

doi:10.19595/j.cnki.1000-6753.tces.220312

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Abstract In mechanical motor faults, up to 30 % of the damage is caused by bearing faults, which makes bearing fault diagnosis and maintenance more critical. Traditional intelligent fault diagnosis methods are challenging to achieve good results when dealing with big data because of their poor generalization ability of feature extraction. In recent years, due to the substantial increase in training resources and the rapid development of computing power, deep learning has gradually become a new player in intelligent fault diagnosis.
This paper proposes a new GAF-CapsNet model to solve the problem that the relative position relationship cannot be fully extracted when one-dimensional mechanical vibration signals are input into the convolutional neural network. The Gramian Angular Field (GAF) encoding method converts the original data into images with conspicuous features. The Gramian Angular Summation Fields (GASF) and Gramian Angular Difference Fields (GADF) are used, respectively. Two groups of feature maps are input into the convolution layer of the small convolution kernel for information reading and feature extraction and then into the capsule network for deeper feature extraction and fault identification. Finally, ten capsules of the digital capsule layer correspond to different fault types.
The Gram-angle field encodes the collected vibration signals, which can be quickly perspective to identify the temporal correlations in different time intervals and generate corresponding feature maps. The Capsule network is sensitive to the relative position of small-size images and has advantages in feature extraction. At the same time, considering the excellent feature extraction ability of the VGG network, a deep small-size convolutional layer is added based on the combination of the capsule network and the VGG network. The vibration images encoded by the Gram Angle field were input to the improved CapsNet network for training, and the GAF-CapsNet model was formed to diagnose bearing faults. Among the two methods of GAF coding, GADF coding performs better than GASF coding in a capsule network. Overall, the GAF encoding method retains relatively complete fault characteristics of the original vibration signal. Due to the influence of different sampling sizes on the accuracy, the experiment proves that the 128 sampling size is the best input size for improving the capsule network.
The performance of the GAF-CapsNet model is tested on the rolling bearing data in the bearing fault database of Case Western Reserve University (CWRU). The results show that the GASF coding method has a poor effect compared with the GADF coding method. Gadfly-cabinet with a sound effect has 99.27 % accuracy, and GASF-CAPSNet with a poor effect has 98.83 %. Compared with other coding methods and convolutional neural networks, the performance of this model is generally higher than that of other models. The maximum difference was 1.64 %.
Finally, the proposed model can accurately predict the fault location and severity in the confusion matrix experiment. Compared with one-dimensional convolution and other networks, the proposed model performs strongly in the anti-noise experiment. The model's accuracy can reach more than 65 % when the SNR is -4dB.

Key words： Bearing fault diagnosis Gramian angular field capsule network

Received: 07 March 2022

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	Zhang Hui
	Ge Baojun
	Han Bin
	Zhao Lina

Cite this article:

Zhang Hui,Ge Baojun,Han Bin等. Fault Diagnosis Method of Motor Bearing Based on GAF-CapsNet[J]. Transactions of China Electrotechnical Society, 2023, 38(10): 2675-2685.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.220312 OR https://dgjsxb.ces-transaction.com/EN/Y2023/V38/I10/2675

[1] 张神林. 基于卷积神经网络的滚动轴承及行星齿轮箱故障诊断方法[D]. 马鞍山: 安徽工业大学, 2018.
[2] 于寅虎. 控制智能化拉开工业4.0生产方式的序幕[J]. 电子技术应用, 2015, 41(2): 8, 10.
Yu Yinhu. Intelligent control kicked off the pro- duction mode of Industry 4.0[J]. Application of Electronic Technique, 2015, 41(2): 8, 10.
[3] Wang Dong, Tse P W. Prognostics of slurry pumps based on a moving-average wear degradation index and a general sequential Monte Carlo method[J]. Mechanical Systems and Signal Processing, 2015, 56/57: 213-229.
[4] 李泽, 王辉, 钱勇, 等. 基于加速鲁棒特征的含噪局部放电模式识别[J]. 电工技术学报, 2022, 37(3): 775-785.
Li Ze, Wang Hui, Qian Yong, et al.Pattern recog- nition of partial discharge in the presence of noise based on speeded up robust features[J]. Transactions of China Electrotechnical Society, 2022, 37(3): 775-785.
[5] 任世锦, 潘剑寒, 李新玉, 等. 基于ELMD与改进SMSVM的机械故障诊断方法[J]. 南京航空航天大学学报, 2019, 51(5): 693-703.
Ren Shijin, Pan Jianhan, Li Xinyu, et al.Novel machinery fault diagnosis approach via ELMD and improved SMSVM[J]. Journal of Nanjing University of Aeronautics & Astronautics, 2019, 51(5): 693-703.
[6] 李世晓, 杜锦华, 龙云. 基于一维卷积神经网络的机电作动器故障诊断[J]. 电工技术学报, 2022, 37(增刊1): 62-73.
Li Shixiao, Du Jinhua, Long Yun.Fault diagnosis of electromechanical actuators based on one-dimensional convolutional neural network[J]. Transactions of China Electrotechnical Society, 2022, 37(S1): 62-73.
[7] Ghate V N, Dudul S V.Cascade neural-network-based fault classifier for three-phase induction motor[J]. IEEE Transactions on Industrial Electronics, 2011, 58(5): 1555-1563.
[8] Chen Zhiqiang, Deng Shengcai, Chen Xudong, et al.Deep neural networks-based rolling bearing fault diagnosis[J]. Microelectronics Reliability, 2017, 75: 327-333.
[9] 俞啸, 范春旸, 董飞, 等. 基于EMD与深度信念网络的滚动轴承故障特征分析与诊断方法[J]. 机械传动, 2018, 42(6): 157-163.
Yu Xiao, Fan Chunyang, Dong Fei, et al.Fault feature analysis and diagnosis method of rolling bearing based on empirical mode decomposition and deep belief network[J]. Journal of Mechanical Trans- mission, 2018, 42(6): 157-163.
[10] 王艳新, 闫静, 王建华, 等. 基于域对抗迁移卷积神经网络的小样本GIS绝缘缺陷智能诊断方法[J]. 电工技术学报, 2022, 37(9): 2150-2160.
Wang Yanxin, Yan Jing, Wang Jianhua, et al.Intelligent diagnosis for GIS with small samples using a novel adversarial transfer learning in convolutional neural network[J]. Transactions of China Electro- technical Society, 2022, 37(9): 2150-2160.
[11] 王卓, 王玉静, 王庆岩, 等. 基于协同深度学习的二阶段绝缘子故障检测方法[J]. 电工技术学报, 2021, 36(17): 3594-3604.
Wang Zhuo, Wang Yujing, Wang Qingyan, et al.Two stage insulator fault detection method based on collaborative deep learning[J]. Transactions of China Electrotechnical Society, 2021, 36(17): 3594-3604.
[12] 葛磊蛟, 廖文龙, 王煜森, 等. 数据不足条件下基于改进自动编码器的变压器故障数据增强方法[J]. 电工技术学报, 2021, 36(增刊1): 84-94.
Ge Leijiao, Liao Wenlong, Wang Yusen, et al.Data augmentation method for transformer fault based on improved auto-encoder under the condition of insufficient data[J]. Transactions of China Electro- technical Society, 2021, 36(S1): 84-94.
[13] 张西宁, 向宙, 唐春华. 一种深度卷积自编码网络及其在滚动轴承故障诊断中的应用[J]. 西安交通大学学报, 2018, 52(7): 1-8, 59.
Zhang Xining, Xiang Zhou, Tang Chunhua.A deep convolutional auto-encoding neural network and its application in bearing fault diagnosis[J]. Journal of Xi’an Jiaotong University, 2018, 52(7): 1-8, 59.
[14] 宫文峰, 陈辉, 张美玲, 等. 基于深度学习的电机轴承微小故障智能诊断方法[J]. 仪器仪表学报, 2020, 41(1): 195-205.
Gong Wenfeng, Chen Hui, Zhang Meiling, et al.Intelligent diagnosis method for incipient fault of motor bearing based on deep learning[J]. Chinese Journal of Scientific Instrument, 2020, 41(1): 195-205.
[15] 刘炳集, 熊邦书, 欧巧凤, 等. 基于时频图和CNN的滚动轴承故障诊断[J]. 南昌航空大学学报(自然科学版), 2018, 32(2): 86-91.
Liu Bingji, Xiong Bangshu, Ou Qiaofeng, et al.Fault diagnosis of rolling bearing based on time-frequency representations and CNN[J]. Journal of Nanchang Hangkong University (Natural Sciences), 2018, 32(2): 86-91.
[16] 袁建虎, 韩涛, 唐建, 等. 基于小波时频图和CNN的滚动轴承智能故障诊断方法[J]. 机械设计与研究, 2017, 33(2): 93-97.
Yuan Jianhu, Han Tao, Tang Jian, et al.An approach to intelligent fault diagnosis of rolling bearing using wavelet time-frequency representations and CNN[J]. Machine Design & Research, 2017, 33(2): 93-97.
[17] 闫佳瑛, 朱希安. 基于VMD与CNN的滚动轴承故障诊断方法[J]. 北京信息科技大学学报(自然科学版), 2020, 35(6): 84-89.
Yan Jiaying, Zhu Xi’an.Fault diagnosis method of rolling bearing based on VMD and CNN[J]. Journal of Beijing Information Science & Technology University, 2020, 35(6): 84-89.
[18] 仝钰, 庞新宇, 魏子涵. 基于GADF-CNN的滚动轴承故障诊断方法[J]. 振动与冲击, 2021, 40(5): 247-253, 260.
Tong Yu, Pang Xinyu, Wei Zihan.Fault diagnosis method of rolling bearing based on GADF-CNN[J]. Journal of Vibration and Shock, 2021, 40(5): 247-253, 260.
[19] 陈德伦, 梁晓瑜, 曾九孙. 基于SAX和CNN的滚动轴承故障诊断方法研究[J]. 计算机仿真, 2020, 37(12): 299-306.
Chen Delun, Liang Xiaoyu, Zeng Jiusun.Rolling element bearing fault diagnosis using a symbolic representation and convolutional neural networks[J]. Computer Simulation, 2020, 37(12): 299-306.
[20] Sabour S, Frosst N, Hinton G E.Dynamic routing between capsules[C]//Proceedings of the 31st Inter- national Conference on Neural Information Pro- cessing, 2017: 3859-3869.
[21] 张振良, 刘君强, 张曦, 等. 基于粒子群优化以及深度胶囊网络的轴承故障诊断[J]. 计算机与数字工程, 2021, 49(2): 333-339, 352.
Zhang Zhenliang, Liu Junqiang, Zhang Xi, et al.Bearing fault diagnosis based on PSO and CapsNet[J]. Computer & Digital Engineering, 2021, 49(2): 333-339, 352.
[22] 侯东晓, 穆金涛, 方成, 等. 基于GADF与引入迁移学习的ResNet34对变速轴承的故障诊断[J]. 东北大学学报(自然科学版), 2022, 43(3): 383-389.
Hou Dongxiao, Mu Jintao, Fang Cheng, et al.Fault diagnosis of variable speed bearings based on GADF and ResNet34 introduced transfer learning[J]. Journal of Northeastern University (Natural Science), 2022, 43(3): 383-389.
[23] Smith W A, Randall R B. Rolling element bearing diagnostics using the case western reserve university data: a benchmark study[J]. Mechanical Systems and Signal Processing, 2015, 64/65: 100-131.
[24] Simonyan K, Zisserman A J A E-P. Very deep con- volutional networks for large-scale image recog- nition[J/OL].2014, https://ui.adsabs.harvard.edu/abs/ 2014arXiv1409.1556S.
[25] He Kaiming, Zhang Xiangyu, Ren Shaoqing, et al. Deep residual learning for image recognition[EB/OL].2015, https://ui.adsabs.harvard.edu/abs/2015arXiv151203385H.
[26] Wen Long, Li Xinyu, Gao Liang, et al.A new convolutional neural network-based data-driven fault diagnosis method[J]. IEEE Transactions on Industrial Electronics, 2018, 65(12): 9953-10107
[27] 李恒, 张氢, 秦仙蓉, 等. 基于短时傅里叶变换和卷积神经网络的轴承故障诊断方法[J]. 振动与冲击, 2018, 37(19): 124-131.
Li Heng, Zhang Qing, Qin Xianrong, et al.Fault diagnosis method for rolling bearings based on short- time Fourier transform and convolution neural net- work[J]. Journal of Vibration and Shock, 2018, 37(19): 124-131.
[28] Udmale S S, Patil S S, Phalle V M, et al.A bearing vibration data analysis based on spectral kurtosis and ConvNet[J]. Soft Computing, 2019, 23(19): 9341-9359.
[29] 陈里里, 付志超, 凌静, 等. 基于WPD-CNN二维时频图像的滚动轴承故障诊断[J]. 组合机床与自动化加工技术, 2021(3): 57-60, 65.
Chen Lili, Fu Zhichao, Ling Jing, et al.Rolling bearing fault diagnosis based on WPD-CNN two- dimensional time-frequency image[J]. Modular Machine Tool & Automatic Manufacturing Technique, 2021(3): 57-60, 65.
[30] 庞俊, 刘鑫, 段敏霞, 等. 基于改进卷积神经网络轴承故障诊断[J]. 组合机床与自动化加工技术, 2021(3): 66-69.
Pang Jun, Liu Xin, Duan Minxia, et al.Fault diagnosis based on improved convolutional neural network[J]. Modular Machine Tool & Automatic Manufacturing Technique, 2021(3): 66-69.