[1]GUSTAVO N P L, ALEX M A, PEDRO A C R,et al.Entropy measures for early detection of bearing faults[J].Physica A: Statistical Mechanics and its Applications,2019,514:458-472.
[2]佘道明,贾民平,张菀.一种新型深度自编码网络的滚动轴承健康评估方法[J].东南大学学报(自然科学版),2018,48(05):801-806.
SHE Daoming, JIA Minping ,ZHANG Wan.Deep autoencoder network method for health assessment of rolling bearings[J].Journal of Southeast University (Natural Science Edition),2018,48(5):801-806.
[3]康守强,王玉静,崔历历,等.基于CFOA-MKHSVM的滚动轴承健康状态评估方法[J].仪器仪表学报,2016,37(9):2029-2035.
KANG Shouqiang,WANG Yujing,CUI Lili, et al.Health state assessment of a rolling bearing based on CFOA-MKHSVM method[J].Chinese Journal of Scientific Instrument,2016,37(9):2029-2035.
[4]WANG S S,CHEN J,WANG H, et al.Degradation evaluation of slewing bearing using HMM and improved GRU[J].Measure-ment, 2019,146:385-395.
[5]JIANG W, ZHOU J Z,LIU H, et al.A multi-step progressive fault diagnosis method for rolling element bearing based on energy entropy theory and hybrid ensemble auto-encoder[J].ISA Transactions, 2019,87: 235-250.
[6]宁永杰. 基于机器学习的滚动轴承状态评估与剩余寿命预测[D].徐州:中国矿业大学,2019.
[7]刘国增,赵建民,张鑫,等.基于小波包AR能量熵和平滑样条的轴承退化状态评估[J].轴承,2019(8):58-63.
LIU Guozeng, ZHAO Jianmin, ZHANG Xin,et al.Assessment on degradation state for bearings based on wavelet packet ar energy entropy and smoothing spline[J].Bearing, 2019(8):58-63.
[8]张小强,朱文辉,康铁宇,等.基于人工免疫算法的离散隐马尔科夫故障诊断模型优化[J].装备环境工程,2019,16(1):63-67.
ZHANG Xiaoqiang, ZHU Wenhui, KANG Tieyu, et al.Optimization of discrete hidden markov fault diagnosis model based on artificial immune algorithm[J].Equipment Environmental Engineering,2019,16(1): 63-67.
[9]刘美芳,余建波,尹纪庭.基于贝叶斯推论和自组织映射的轴承性能退化评估方法[J].计算机集成制造系统,2012,18(10): 2237-2244.
LIU Meifang, YU Jianbo, YIN Jiting.Bearing performance degradation assessment based on bayesian inference and self-organizing map[J].Computer Integrated Manufacturing Systems, 2012,18(10):2237-2244.
[10]PAVLE B, MATEJ G, DEJAN P, et al.Bearing fault prognostics using Renyi entropy based features and Gaussian process models[J].Mechanical Systems and Signal Processing, 2015,52:327-337.
[11]DOERSCH C.Tutorial on variational autoencoders[J].Stat, 2016, 1050: 13-37.
[12]HOU X X, SUN K,SHEN L L,et al.Improving variational autoencoder with deep feature consistent and generative adversarial training[J].Neurocomputing, 2019,341:83-194.
[13]KING D P, WELLING M.Auto-encoding variational bayes[C].Internationl Conference on Learning Representations,2014:1-14.
[14]HSU C C,HWANG H T,WU Y C,et al.Voice conversion from non-parallel corpora using variational auto-encoder[C]∥Global Con-ference on Signal and Information Processing.IEEE ,2017:1-6.
[15]马波,赵祎,齐良才.变分自编码器在机械故障预警中的应用[J].计算机工程与应用,2019,55(12):245-249.
MA Bo, ZHAO Yi,QI Liangcai.Application of variational auto-encoder in mechanical fault early warning[J].Computer Engineering and Applications,2019,55(12): 245-249.
[16]LAROCHELLE H, MURRAY I.The neural autoregressive distribution estimator[J].Journal of Machine Learning Research, 2011, 15:29-37.
[17]SALMAN H, YADOLLAHPOUR P, FLETCHER T, et al.Deep diffeomor-phic normalizing flows[J].ArXiv preprint, 2018:1-13.
[18]NEAL R M.Annealed importance sampling[J].Statistics and computing, 2001, 11(2): 125-139.
[19]WU Y, BURDA Y, SALAKHUTDINOV R, et al.On the quantitative analysis of decoder-based generative models[J].ArXiv preprint, 2016:1-17.
[20]REZENDE D J, MOREZENDE D, MOHAMED S.Variational inference with normalizing flows[C].International conference on machine learning.PMLR, 2015: 1530-1538.
[21]张锐戈. 滚动轴承振动信号非平稳、非高斯分析及故障诊断研究[D].西安:西安电子科技大学,2014.
[22]夏均忠,郑建波,白云川,吕麒鹏,杨刚刚.基于NAP和RMI的滚动轴承性能退化状态识别与评估[J].振动与冲击,2019,38(23):33-37.
XIA Junzhong, ZHENG Jianbo, BAI Yunchuan, et al.Perfor-mance degradation status identification and assessment for rolling bearing based on NAP and RMI[J].Journal of Vibration and Shock,2019,38(23): 33-37.
[23]LEE J, QIU H, YU G, et al.IMS, University of Cincinnati.Bearing data set, NASA ames prognostics data repository [R/OL].Moffett Field,CA,USA:NASA Ames Research Center,2007.[2018-12-12].http://ti.arc.nasa.gov/project/ prognostic-data-repository.
[24]李鹏.基于高斯混合模型的变分自动编码器[D].哈尔滨:哈尔滨工业大学,2017.
[25]ZHANG B, ZHANG S, LI W.Bearing performance degradation assessment using long short-term memory recurrent network[J].Computers in Industry, 2019, 106:14-29.