小数据条件下基于测地流核函数的域自适应故障诊断方法研究

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摘要针对机械设备的状态监测和故障诊断面临的先验样本数据少、样本空间不完备的“小数据”困境，提出了基于测地流核函数的域自适应故障诊断方法。以有限的先验样本数据作为源域，以实际监测数据作为目标域，分别提取设备状态特征并将特征分布子空间嵌入格拉斯曼流形，基于测地流核函数对源域和目标域在特征分布结构上的相似性进行度量，从而实现域自适应基础上的故障诊断。基于轴承振动数据的实验验证表明，基于测地流核函数的域自适应故障诊断能够有效抑制工况变化、采样母体差异的影响，提高故障诊断正确率。

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	刘海宁1
	宋方臻1
	窦仁杰1
	黄亦翔2
	刘成良2

关键词 ：域自适应, 故障诊断, 小数据, 测地流核函数

Abstract：The shortage of prior faulty data or the incompleteness of sample space, which leads to a "small data" trap, is a common situation encountered when implementing intelligent condition monitoring and fault diagnosis of machines.To overcome this problem, a domain adaptive fault diagnostic scheme was proposed based on the geodesic flow kernel method.With prior data samples as the source domain and monitored data as the target domain, status features of the machine were extracted and selected separately to construct the subspaces of machine conditions.By embedding the subspaces in a Grassmann manifold, the structural similarity of subspaces was evaluated based on the geodesic flow kernel to achieve the domain adaptive fault diagnosis.The verification based on the bearing vibration data demonstrates that the domain adaptive fault diagnosis based on the geodesic flow kernel can effectively reduce the impact of the variety in working conditions and the physical differences underlying sampling populations, so that the accuracy of fault diagnosis can be improved.

Key words： domain adaptation fault diagnosis small data geodesic flow kernel

收稿日期: 2017-04-24 出版日期: 2018-09-15

引用本文:

刘海宁1，宋方臻1，窦仁杰1，黄亦翔2，刘成良2. 小数据条件下基于测地流核函数的域自适应故障诊断方法研究[J]. 振动与冲击, 2018, 37(18): 36-42.
LIU Haining1,SONG Fangzhen1,DOU Renjie1,HUANG Yixiang2,LIU Chengliang2 . Domain adaptive fault diagnosis based on the geodesic flow kernel under small data condition. JOURNAL OF VIBRATION AND SHOCK, 2018, 37(18): 36-42.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2018/V37/I18/36

[1] LEE J, BAGHERI B, KAO H. A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems[J]. Manufacturing Letters, Society of Manufacturing Engineers (SME), 2015, 3: 18–23.
[2] 王国彪, 何正嘉, 陈雪峰等. 机械故障诊断基础研究何去何从[J]. 机械工程学报, 2013, 49(1): 63–72.
WANG Guo-biao, HE Zheng-jia,CHEN Xue-feng, et al．Basic research on machinery fault diagnosis- what is the prescription [J]. Journal of Mechanical Engineering, 2013(1): 63－72．
[3] WORDEN K, STASZEWSKI W J, HENSMAN J J. Natural computing for mechanical systems research: A tutorial overview[J]. Mechanical Systems and Signal Processing, Elsevier, 2011, 25(1): 4–111.
[4] 钟秉林, 黄仁. 机械故障诊断学[M]. 高文龙. 第三版. 北京: 机械工业出版社, 2006.
ZHONG Bing-lin, HUANG Ren. Introduction to machine fault diagnosis[M]. Beijing: China Machine Press, 2006.
[5] BISHOP C. Pattern Recognition and Machine Learning[M]. Springer, 2006.
[6] YIN G, ZHANG Y-T, LI Z-N, et al. Online fault diagnosis method based on Incremental Support Vector Data Description and Extreme Learning Machine with incremental output structure[J]. Neurocomputing, Elsevier, 2014, 128: 224–231.
[7] 刘刚, 屈梁生. 应用Bootstrap方法构造机械故障特征库[J]. 振动工程学报, 2002, 15(2): 106–110.
LIU Gang, QU Liang-shen. An application of bootstrap resampling method to knowledge base design in mechinery fault diagnosis[J]. Journal of Vibration Engineering, 2002, 15(1):106-110.
[8] 瞿雷, 戴光昊, 王琇峰等. 基于特征评估与核主分量分析的齿轮故障分类方法[J]. 机械传动, 2014, 38(11): 105–110.
QU Lei, DAI Guang-hao, WANG Xiu-feng, et al. Method of gear fault classification based on feature evaluation and kernel principal component analysis .Journal of Mechanical Transmission, 2014, 38 (11):105-110
[9] SUN R, TSUNG F, QU L. Combining bootstrap and genetic programming for feature discovery in diesel engine diagnosis[J]. International Journal of Industrial Engineering : Theory Applications and Practice, 2004, 11(3): 273–281.
[10] CERRADA M, ZURITA G, CABRERA D, et al. Fault diagnosis in spur gears based on genetic algorithm and random forest[J]. Mechanical Systems and Signal Processing, Elsevier, 2016, 70–71: 87–103.
[11] BOQING G, YUAN S, FEI S, et al. Geodesic flow kernel for unsupervised domain adaptation[C]//2012 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2012: 2066–2073.
[12] 刘建伟, 孙正康, 罗雄麟. 域自适应学习研究进展[J]. 自动化学报, 2014, 40(8): 1576–1600.
LIU Jian-Wei, SUN Zheng-Kang, et al.Review and research development on domain adaptation learning[J]. Acta Automatica Sinica, 2014, 40(8):1576-1600.
[13] PATEL V M, GOPALAN R, LI R, et al. Visual Domain Adaptation: A survey of recent advances[J]. IEEE Signal Processing Magazine, 2015, 32(3): 53–69.
[14] SHIMODAIRA H. Improving predictive inference under covariate shift by weighting the log-likelihood function[J]. Journal of Statistical Planning and Inference, 2000, 90(2): 227–244.
[15] GONG B. Kernel Methods for Unsupervised Domain Adaptation[D]. Citeseer, 2015.
[16] HAMM J. Subspace-Based Learning With Grassmann Kernels[D]. University of Pennsylvania, 2008.
[17] GOPALAN R, RUONAN LI, CHELLAPPA R. Domain adaptation for object recognition: An unsupervised approach [C]//2011 International Conference on Computer Vision. IEEE, 2011: 999–1006.
[18] WEISS K, KHOSHGOFTAAR T M, WANG D. A survey of transfer learning[M]. Journal of Big Data, Springer International Publishing, 2016, 3(1).
[19] LOPARO K A. Bearing vibration data set[EB/OL]. Case Western Reserve University, 2003. : http://csegroups.case. edu/bearingdatacenter/home(2003).
[20] LEI Y. A new approach to intelligent fault diagnosis of rotating machinery[J]. 2008, 35: 1593–1600.
[21] CHANG C-C, LIN C-J. LIBSVM: A library for support vector machines[J]. ACM Transactions on Intelligent Systems and Technology, 2011, 2(3): 27:1--27:27.