[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.