|Table of Contents|

Multi-scale convolution intra-class transfer learning for train bearing fault diagnosis(PDF)

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2020年05期
Page:
151-164
Research Field:
载运工具运用工程
Publishing date:
2020-10-20

Info

Title:
Multi-scale convolution intra-class transfer learning for train bearing fault diagnosis
Author(s):
SHEN Chang-qing1;  WANG Xu1;  WANG Dong2;  QUE Hong-bo3;  SHI Juan-juan1;  ZHU Zhong-kui1
1. School of Rail Transportation, Soochow University, Suzhou 215131, Jiangsu, China; 2. State Key Laboratory of Mechanical System and Vibration, Shanghai Jiaotong University, Shanghai 200240, China; 3. CRRC Qishuyan Locomotive and Rolling Stock Technology Research Institute Co., Ltd., Changzhou 213011, Jiangsu, China
Keywords:
train bearing;  fault diagnosis;  deep transfer learning;  conditional distribution;  intra-class adaptive;  feature extraction
PACS:
U270.1
DOI:
10.19818/j.cnki.1671-1637.2020.05.012
Abstract:
Considering the inconsistent distribution of bearing vibration data collected under different working conditions, the generalization ability of traditional deep learning model decreases. A multi-scale convolution intra-class adaptive deep transfer learning model was proposed. The spectrum of vibration data was analyzed using the modified ResNet-50. The middle-level features were obtained. A multi-scale feature extractor was developed, the middle-level features were processed, and the high-level features were generated. The high-level features were used as the inputs of classifier. The pseudo-labels were computed, and then the conditional distribution distances of vibration data collected under variable working conditions reduced for the intra-class adaptation. To verify the generality and superiority of model, the proposed method was employed to analyze a train wheelset bearing dataset and the Case Western Reserve University dataset under variable working conditions. Analysis result indicates that the high-level features of samples with the same label in different domains are properly aligned. More satisfactory fault diagnosis accuracies are obtained by the proposed model. In six fault diagnosis cases of train bearing under variable working conditions, the average diagnosis accuracy of the proposed model is 90.75%, approximately 10% higher than those of traditional deep learning models, while the recall rate is 0.927. In twelve fault diagnosis cases of Case Western Reserve University dataset under variable working conditions, the average accuracy obtained by the proposed model is 99.97%, approximately 10% higher than those of traditional models. The conditional distribution discrepancy between different domains reduces by using the pseudo-labels. The inconsistency problem of data distribution of source domain and target domain is properly addressed. The high-level features of samples from different scales can be aligned by the multi-scale feature learner.The generalization and robustness of the model largely increase. In conclusion, the proposed model has a high potential for the train bearing fault diagnosis under variable working conditions. 7 tabs, 13 figs, 37 refs.

References:

[1] 谢 国,王竹欣,黑新宏,等.面向热轴故障的高速列车轴温阈值预测模型[J].交通运输工程学报,2018,18(3):129-137. XIE Guo,WANG Zhu-xin, HEI Xin-hong, et al. Axle temperature threshold prediction model of high-speed train for hot axle fault[J]. Journal of Traffic and Transportation Engineering, 2018, 18(3): 129-137.(in Chinese)
[2] YAN Ru-qiang, SHEN Fei, SUN Chuang, et al. Knowledge transfer for rotary machine fault diagnosis[J]. IEEE Sensors Journal, 2020, 20(15): 8374-8393.
[3] WU Zheng-hong, JIANG Hong-kai, ZHAO Ke, et al. An adaptive deep transfer learning method for bearing fault diagnosis[J]. Measurement, 2019, DOI: 10.1016/j.measurement.2019.107227.
[4] SHAO Si-yu, MCALEER S, YAN Ru-qiang, et al. Highly accurate machine fault diagnosis using deep transfer learning[J]. IEEE Transactions on Industrial Informatics, 2018, 15: 2446-2455.
[5] FENG Zhi-peng, CHEN Xiao-wang. Adaptive iterative generalized demodulation for nonstationary complex signal analysis: principle and application in rotating machinery fault diagnosis[J]. Mechanical Systems and Signal Processing, 2018, 110: 1-27.
[6] JIANG Xing-xing, WANG Jun, SHI Juan-juan, et al. A coarse-to-fine decomposing strategy of VMD for extraction of weak repetitive transients in fault diagnosis of rotating machines[J]. Mechanical Systems and Signal Processing, 2019, 116: 668-692.
[7] ISLAM M M M, KIM J M. Reliable multiple combined fault diagnosis of bearings using heterogeneous feature models and multiclass support vector machines[J]. Reliability Engineering and System Safety, 2019, 184: 55-66.
[8] 刘 强,詹志强,王 硕,等.数据驱动的高速列车轴承多模态运行监控与故障诊断[J].中国科学:信息科学,2020,50(4):527-539. LIU Qiang, ZHAN Zhi-qiang, WANG Shuo, et al. Data-driven multimodal operation monitoring and fault diagnosis of high-speed train bearings[J]. Scientia Sinica Informationis, 2020, 50(4): 527-539.(in Chinese)
[9] KUNCAN M, KAPLAN K, MINAZ M R, et al. A novel feature extraction method for bearing fault classification with one dimensional ternary patterns[J]. ISA Transactions, 2019, 100: 346-357.
[10] 周生通,朱经纬,周新建,等.组合载荷作用下动车牵引电机转子系统弯扭耦合振动特性[J].交通运输工程学报,2020,20(1):159-170. ZHOU Sheng-tong, ZHU Jing-wei, ZHOU Xin-jian, et al. Bending-torsional coupling vibration characteristics of EMU traction motor rotor system under combined loads[J]. Journal of Traffic and Transportation Engineering, 2020, 20(1): 159-170.(in Chinese)
[11] 郭俊超,甄 冬,孟召宗,等.基于 WAEEMD 和 MSB 的滚动轴承故障特征提取[J].中国机械工程,https:∥kns.cnki.net/kcms/detail/42.1294.TH.20200810.1640.024.html. GUO Jun-chao, ZHEN Dong, MENG Zhao-zong, et al. Feature extraction of rolling element bearing based on weighted average ensemble empirical mode decomposition and modulation bispectrum analysis[J]. China Mechanical Engineering, https:∥kns.cnki.net/kcms/detail/42.1294.TH.20200810.1640.024.html.(in Chinese)
[12] 雷亚国,杨 彬,杜兆钧,等.大数据下机械装备故障的深度迁移诊断方法[J].机械工程学报,2019,55(7):1-8. LEI Ya-guo, YANG Bin, DU Zhao-jun, et al. Deep transfer diagnosis method for machinery in big data era[J]. Journal of Mechanical Engineering, 2019, 55(7): 1-8.(in Chinese)
[13] 严文超,王伟奇,黄 蓉.基于RSSD和小波变换的滚动轴承故障诊断[J].武汉工程大学学报,2019,41(4):399-404. YAN Wen-chao, WANG Wei-qi, HUANG Rong. Rolling bearing fault diagnosis method based on resonance-based sparse signal decomposition and wavelet transform[J]. Journal of Wuhan Institute of Technology, 2019, 41(4): 399-404.(in Chinese)
[14] MAQSOOD A, OSLEBO D, CORZINE K, et al. STFT cluster analysis for DC pulsed load monitoring and fault detection on naval shipboard power systems[J]. IEEE Transactions on Transportation Electrification, 2020, 6(2): 821-831.
[15] MAURYA S, SINGH V, VERMA N K. Condition monitoring of machines using fused features from EMD-based local energy with DNN[J]. IEEE Sensors Journal, 2020, 20(15): 8316-8327.
[16] 王海龙,夏筱筠,孙维堂.基于EMD与卷积神经网络的滚动轴承故障诊断[J].组合机床与自动化加工技术,2019(10):46-48,52. WANG Hai-long, XIA Xiao-jun, SUN Wei-tang. Rolling bearing fault diagnosis based on EMD and convolutional neural network[J]. Modular Machine Tool and Automatic Manufacturing Technique, 2019(10): 46-48, 52.(in Chinese)
[17] LI Ning, HUANG Wei-guo, GUO Wen-jun, et al. Multiple enhanced sparse decomposition for gearbox compound fault diagnosis[J]. IEEE Transactions on Instrumentation Measurement, 2020, 69(3): 770-781.
[18] QIN Yi. A new family of model-based impulsive wavelets and their sparse representation for rolling bearing fault diagnosis[J]. IEEE Transactions on Industrial Electronics, 2018, 65(3): 2716-2726.
[19] HE Qing-bo, WU En-hao, PAN Yuan-yuan. Multi-scale stochastic resonance spectrogram for fault diagnosis of rolling element bearings[J]. Journal of Sound and Vibration, 2018, 420: 174-184.
[20] JIA Feng, LEI Ya-guo, GUO Liang, 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.
[21] LI Ke, XIONG Meng, LI Fu-cai, et al. A novel fault diagnosis algorithm for rotating machinery based on a sparsity and neighborhood preserving deep extreme learning machine[J]. Neurocomputing, 2019, 350: 261-270.
[22] ZHU Jun, CHEN Nan, PENG Wei-wen. Estimation of bearing remaining useful life based on multiscale convolutional neural network[J]. IEEE Transactions on Industrial Electronics, 2019, 66(4): 3208-3216.
[23] PAN S J, YANG Qiang. A survey on transfer learning[J]. IEEE Transactions on Knowledge and Data Engineering, 2009, 22: 1345-1359.
[24] HAN Te, LIU Chao, YANG Wen-guang, et al. Deep transfer network with joint distribution adaptation: a new intelligent fault diagnosis framework for industry application[J]. ISA Transactions, 2020, 97: 269-281.
[25] XU Yong-hui, PAN S J, XIONG Hui, et al. A unified framework for metric transfer learning[J]. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(6): 1158-1171.
[26] WEISS K, KHOSHGOFTAAR T M, WANG Ding-ding. A survey of transfer learning[J]. Journal of Big Data, 2016, DOI: 10.1186/s40537-016-0043-6.
[27] CAO Pei, ZHANG Sheng-li, TANG Jiong. Preprocessing- free gear fault diagnosis using small datasets with deep convolutional neural network-based transfer learning[J]. IEEE Access, 2018, 6: 26241-26253.
[28] GUO Liang, LEI Ya-guo, XING Sai-bo, et al. Deep convolutional transfer learning network: a new method for intelligent fault diagnosis of machines with unlabeled data[J]. IEEE Transactions on Industrial Electronics, 2018, 66(9): 7316-7325.
[29] WEN Long, GAO Liang, LI Xin-yu. A new deep transfer learning based on sparse auto-encoder for fault diagnosis[J]. IEEE Transactions on Systems, Man, and Cybernetics Systems, 2019, 49(1): 136-144.
[30] YANG Bin, LEI Ya-guo, JIA Feng, et al. An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings[J]. Mechanical Systems and Signal Processing, 2019, 122: 692-706.
[31] GRETTON A, BORGWARDT K M, RASCH M J, et al. A kernel two-sample test[J]. Journal of Machine Learning Research, 2012, 13: 723-773.
[32] HE Kai-ming, ZHANG Xiang-yu, REN Shao-qing, et al. Deep residual learning for image recognition[C]∥IEEE. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2016: 770-778.
[33] PANS J, TSANG I W, KWOK J T, et al. Domain adaptation via transfer component analysis[J]. IEEE Transactions on Neural Networks, 2010, 22(2): 199-210.
[34] WANG Jin-dong, CHEN Yi-qiang, HU Li-sha, et al. Stratified transfer learning for cross-domain activity recognition[C]∥IEEE. 2018 IEEE International Conference on Pervasive Computing and Communications(PerCom). New York: IEEE, 2018: 1-10.
[35] JIAO Jin-yang, ZHAO Ming, LIN Jing, et al. Residual joint adaptation adversarial network for intelligent transfer fault diagnosis[J]. Mechanical Systems and Signal Processing, 2020, 145: 106962-1-14.
[36] ZHU Jun, CHEN Nan, SHEN Chang-qing. A new deep transfer learning method for bearing fault diagnosis under different working conditions[J]. IEEE Sensors Journal, 2020, 20(15): 8394-8402.
[37] VAN DERMAATEN L, HINTON G. Visualizing data using t-SNE[J]. Journal of Machine Learning Research, 2008, 9: 2579-2605.

Memo

Memo:
-
Last Update: 2020-10-20