参考文献/References:
1 ] 钟 秉 林 ,黄 仁 . 机 械 故 障 诊 断 学 [ M ] . 北 京 :机 械 工 业 出版 社 , 2002 .
[ 2 ] YAN R Q ,GAO R X , CHEN X F .W avelets for fault diagnosis of rotary machines : A review with applications [ J ] .Signal Processing , 2014 , 96 : 1- 15 .
[ 3 ] M C N A M A R A B , WIESE NFELD K .Theory of stochastic resonance [ J ] .Physical Review A, 39 ( 9 ): 4854-4869 .
[ 4 ] LIU R N , Y A N G B Y , ZIO E , et al.Artificialintelligence for fault diagnosis of rotating machinery : A review [ J ] . M echanical Systems and Signal Processing ,2018 , 108 : 33- 47 .
[ 5 ] F E N G J , L EI Y G , JIN G L .Deep neural networks : a pro mising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data [ J ] . M echanical Systems and Signal Processing ,2018 , 72 / 73 : 303- 315 .
[ 6 ] W A N G B , L EI Y , LI N , et al.Deep separable convolutional network for remaining useful life prediction of machinery [ J ] .M echanical Systems and Signal Processing, 2019 , 134 : 106330 .
[ 7 ] C H E N Z Y , G R Y L LIA S K , LI W .M echanical fault diagnosis using convolutional neural networks and extreme learning machine [ J ] . M echanical Systems and Signal Processing , 2019 , 133 : 106272 .
[ 8 ] T H IR U K O V A L L U R U R , DIXIT S , SE V A K U L A R K , et al.Generating feature sets for fault diagnosis using denoising stacked auto encoder [ C ]// 2016 IE E E International Conference on Prognostics and H ealth M anagem ent ( IC P H M ) . New Y orks : IE E E ,2016 : 1- 7 .
[ 9 ] L U C , W A N G Z Y , QINW L, et al.Fault diagnosis of rotary machinery co m ponents using a stacked denoising autoencoder based health state identification[ J ] .Signal Processing , 2016 , 130 : 377-388 .
[ 10 ] S H A OH D, JIA N GH, Z H A OH, et al.A novel deep autoencoder feature learning method for rotating machinery fault diagnosis [ J ] . M echanical Systems and Signal Processing , 2017 , 95 : 187-204 .
[ 11 ] L EI Y G , JIA F , LIN J , et al.A n intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data [ J ] .IE E E Transactions on Industrial Electronics , 2016 , 63 ( 5 ): 3137- 3147 .
[ 12 ] Z H A N G Z Z , LI S M , W A N G J R , et al.General normalized sparse filtering : A novel unsupervised learning method for rotating machinery fault diagnosis [ J ] .M echanical Systems and Signal Processing , 2019 ,124 : 596- 612 .
[ 13 ] N GIA M J , K O H P W , C H E N Z , et al.Sparse filtering[ C ]// International Conference on Neural Information Processing Systems , 2011 : 1125- 1133 .
[ 14 ] Case western reserve university bearing data center[ DS / O L ] . [ 2019
10 13 ] .http :// csegroups .case .edu / bearingdatacenter / pages / dow nload data file .
[ 15 ] S H A O S , M C A L E E R S , Y A N R , et al.Highly accurate machine fault diagnosis using deep transfer learning [ J ] .IE E E Transactions on Industrial Informatics , 2019 , 15 ( 4 ): 2446- 2455 .
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