|
|
|
| Bearing fault diagnosis method based on deep metric learning |
| LI Xiaojuan1,2, XU Zengbing1,2, XIONG Wen3, WANG Zhigang1,2, TAN Junjie1,2 |
1.Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan 430081, China;
2.Wuhan University of Science and Technology college of machinery and automation, Wuhan 430081, China;
3.Key Laboratory of Waterjet Propulsion Technology, No.708 Research Institute, China State Shipbuilding Corporation, Shanghai 200011, China |
|
|
|
Abstract As the intra-class scatter and inter-class similarity are big in bearing fault data, which constrains the diagnostic accuracy, a new method of deep metric learning for fault diagnosis is proposed. The deep neural network (DNN) is used to adaptively extract the fault features, and the marginal Fisher analysis method based on Euclidean distance is used to optimize the features, then, the BPNN classifier is added to the top-level feature output layer of the constructed deep metric network (DMN) to fine tune the parameters and realize fault classification and recognition. Finally, diagnostic analysis on bearing fault experimental data of different fault types and different fault severity verified that the method can effectively diagnose bearing faults with high precision and the effect is better than traditional deep belief network (DBN) fault diagnosis method as well as the common time-domain statistical features combined with support vector machine (SVM) classification fault diagnosis method.
|
|
Received: 08 April 2019
Published: 28 July 2020
|
|
|
|
[1] Wang D, Tse P W, Tsui K L. An enhanced Kurtogram method for fault diagnosis of rolling element bearings[J]. Mechanical Systems and Signal Processing, 2013, 35(1-2):176-199.
[2] 雷亚国,贾峰,周昕,林京.基于深度学习理论的机械装备大数据健康监测方法[J].机械工程学报, 2015,51(21):49-56.
LEI Yaguo, JIA Feng, ZHOU Xi, LIN Jing. A Deep Learning-based Method for Machinery Health Monitoring with Big Data[J]. Journal of Mechanical Engineering, 2015, 51(21): 49-56
[3] 苏鑫,吴迎亚,裴华健,蓝兴英,高金森.大数据技术在过程工业中的应用研究进展[J].化工进 展,2016,35(6):1652-1659.
SU Xin, WU Yingya, PEI Huajian, LAN Xingying, GAO Jinsen. Recent Development of The Application of Big Data Technology in Process Industries. Chemical Industry and Engineering Process, 2016, 35(6): 1652-1659.
[4] HOPKINS B, EVELSON B, LEAVER S, et al. Expand Your Digital Horizon with Big Data[R]. Forrester, 2011.
[5] Tamilselvan, P, Wang P. Failure Diagnosis Using Deep Belief Learning Based Health State Classification[J]. Reliability Engineering & System Safety, 2013, 115:124-135.
[6] 李巍华,单外平,曾雪琼.基于深度信念网络的轴承故障分类识别[J].振动工程学报,2016,29(2):340—347.
LI Weihua, SHAN Waiping, ZENG Xueqiong. Bearing Fault Identification Based on Deep Belief Network[J]. Journal of Vibration Engineering, 2016, 29(2): 340—347.
[7] J Olivier Janssens, Viktor Slavkovikj, Brain Vervisch.et a1. Convolutional Neural Network Based Fault Detection for Rotating Machinery[J]. J of Sound and Vibration,2016(337)331-345.
[8] 智洪欣,于洪涛,李邵梅,高超，王艳川.一种基于深度度量学习的视频分类方法[J].电子与信息学报,2018(11).
ZHI Hongxin, YU Hongtao, LI Shaomei, GUO Chao, WANG Yanchuan. A Deep Metric Learning Based Video Classification Method[J]. Journal of Electronics and Information Technology, 2018(11).
[9] Wu B, Chen Z, Wang J, et al. Exponential Discriminative Metric Embedding in Deep Learning[J]. Neurocomputing, 2018, 290.
[10] Hu J, Lu J, Tan Y P. Discriminative Deep Metric Learning for Face Verification in the Wild[C]. Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on. IEEE, 2014.
[11] Hu J, Lu J, Tan Y P. Deep Transfer Metric Learning[C]. IEEE Conference on Computer Vision & Pattern Recognition.2015.
[12] Song H O, Xiang Y, Jegelka S, et al. Deep Metric Learning via Lifted Structured Feature Embedding[J]. 2015.
[13] I. W. Tsang, J. T. Kwok, C. Bay, and H. Kong. Distance Metric Learning with Kernels. In ICANN, pages 126–129,2003.
[14] D.-Y. Yeung and H. Chang. A Kernel Approach for Semi-supervised Metric Learning.TNN,18(1):141–149,2007.
[15] Lu J, Hu J, Jie Z. Deep Metric Learning for Visual Understanding: An Overview of Recent Advances[J]. IEEE Signal Processing Magazine, 2017, 34(6):76-84.
[16] Yan S, Xu D, Zhang B, et al. Graph Embedding: A General Framework for Dimensionality Reduction[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence,2007,29(1):40.
[17] H. Yu and J. Yang. A Direct LDA Algorithm for High Dimensional Data-with Application to Face Recognition Pattern Recognition, vol.34, pp.2067-2070,2001.
[18] Hinton GE, Osindero S, The Y-W. A Fast Learning Algorithm for Deep Belief Nets[J]. Neural Computation, 2006, 18(7):1527-1554.
[19] The Case Western Reserve University Bearing Data Center. Bearing Data Center Fault Test Data [EB/OL] (1998-10-04). http://csegroups.case.edu/bearingdatacenter/pages/download-data-file.
[20] Sokolova M, Japkowicz N, Szpakowicz S. Beyond Accuracy, F-Score and ROC: A Family of Discriminant Measures for Performance Evaluation[J]. 2006.
[21] Van der Maaten L, Hinton G. Visualizing data using t-SNE. J Mach Learn Res. 2008; 9:2580-2605.
|
|
|
|
