The fault diagnosis method of rolling bearing under variable working conditions based on deep transfer learning
The vibration signals of rolling bearing obtained under variable working conditions do not obey the same independent distribution so that the traditional method of bearing fault diagnosis has low accuracy, a fault diagnosis method about rolling bearing based on sparse denoising autoencoder (SDAE) fo...
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| Vydáno v: | Journal of the Brazilian Society of Mechanical Sciences and Engineering Ročník 42; číslo 11 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Berlin/Heidelberg
Springer Berlin Heidelberg
01.11.2020
Springer Nature B.V |
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| ISSN: | 1678-5878, 1806-3691 |
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| Abstract | The vibration signals of rolling bearing obtained under variable working conditions do not obey the same independent distribution so that the traditional method of bearing fault diagnosis has low accuracy, a fault diagnosis method about rolling bearing based on sparse denoising autoencoder (SDAE) for deep feature extraction combining transfer learning is proposed. First, the bearing vibration signal in the time domain is transformed for frequency domain signal via Fourier transform, which is input into the SDAE for adaptive deep feature extraction. Then, the joint geometrical and statistical alignment is introduced to deal with the deep feature samples for reducing the domain discrepancy both statistically and geometrically. Finally, the k-nearest neighbor classification algorithm is used for completing the fault diagnosis of rolling bearing under variable working conditions. The experimental results show that the method presented in the paper improves the accuracy rate of fault diagnosis about rolling bearing under variable working conditions, verifies its feasibility and effectiveness. |
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| AbstractList | The vibration signals of rolling bearing obtained under variable working conditions do not obey the same independent distribution so that the traditional method of bearing fault diagnosis has low accuracy, a fault diagnosis method about rolling bearing based on sparse denoising autoencoder (SDAE) for deep feature extraction combining transfer learning is proposed. First, the bearing vibration signal in the time domain is transformed for frequency domain signal via Fourier transform, which is input into the SDAE for adaptive deep feature extraction. Then, the joint geometrical and statistical alignment is introduced to deal with the deep feature samples for reducing the domain discrepancy both statistically and geometrically. Finally, the k-nearest neighbor classification algorithm is used for completing the fault diagnosis of rolling bearing under variable working conditions. The experimental results show that the method presented in the paper improves the accuracy rate of fault diagnosis about rolling bearing under variable working conditions, verifies its feasibility and effectiveness. |
| ArticleNumber | 585 |
| Author | Tang, Baoping Dong, Shaojiang He, Kun |
| Author_xml | – sequence: 1 givenname: Shaojiang surname: Dong fullname: Dong, Shaojiang organization: School of Mechatronics and Vehicle Engineering, Chongqing Jiaotong University – sequence: 2 givenname: Kun orcidid: 0000-0001-9916-6075 surname: He fullname: He, Kun email: 15923802747@163.com organization: School of Mechatronics and Vehicle Engineering, Chongqing Jiaotong University – sequence: 3 givenname: Baoping surname: Tang fullname: Tang, Baoping organization: State Key Laboratory of Mechanical Transmission, Chongqing University |
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| Cites_doi | 10.3390/e18080292 10.1016/j.measurement.2019.06.029 10.1016/j.measurement.2013.03.023 10.1109/TIE.2016.2524399 10.1016/j.measurement.2016.07.054 10.1006/mssp.2001.1462 10.1109/TNN.2010.2091281 10.1109/TIE.2019.2953010 10.1109/TIE.2012.2219838 10.1016/j.ymssp.2011.09.003 10.1016/j.ymssp.2017.03.034 10.1016/j.ins.2014.09.004 10.1038/nature14539 10.1109/TSMC.2017.2754287 10.1016/j.ymssp.2013.11.011 10.1038/381607a0 10.1016/j.ymssp.2015.08.030 10.1155/2014/765621 10.1109/TKDE.2009.191 10.3901/CJME.2015.1026.127 10.1016/j.mechmachtheory.2014.01.011 10.1109/CVPR.2017.547 10.1109/ICCV.2013.368 10.1109/DEMPED.2007.4393063 10.1109/ICCV.2013.274 10.1145/1390156.1390294 |
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| Keywords | Fault diagnosis Variable working conditions Transfer learning Rolling bearing Sparse denoising autoencoder |
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| References | Wang, Liang, Li (CR3) 2014; 45 Pan, Tsang, Kwork (CR26) 2011; 22 Olshausen, Field (CR30) 1996; 381 Samanta, Al-Balushi (CR8) 2003; 17 Yang, Wang, Cheng (CR19) 2013; 46 Shell, Coupland (CR23) 2015; 293 Yuan, Huang, Wang (CR13) 2018; 14 Guo, Chen, Shen (CR17) 2016; 93 Liu, Han (CR5) 2014; 18 CR34 Shao, Jiang, Zhao (CR16) 2017; 95 Fei (CR21) 2017; 5 Chen, Shen, Yan (CR25) 2017; 38 CR31 Sun, Wang, Liu (CR15) 2019; 146 Gretton, Borgwardt, Rasch (CR32) 2012; 13 Miguel, Giansalvo, Antonio (CR11) 2013; 60 Cocconcelli, Bassi, Secchi (CR6) 2012; 27 Xu, Zhao, Ma (CR4) 2013; 3 Wu, Yu, Liu (CR20) 2016; 18 Cerradam, Zurita, Cabrera (CR7) 2016; 70 CR29 CR28 CR9 CR27 Yann, Yoshua, Geoffrey (CR12) 2015; 521 Pan, Yang (CR22) 2010; 20 Shen, Chen, Yan (CR24) 2017; 30 Chen, Qie, Zhang (CR1) 2016; 29 Liu, Wang, Chen (CR18) 2014; 2014 Yang, Lei, Jia (CR33) 2020; 67 Wen, Gao, Li (CR14) 2019; 49 Yang, Liu, Chen (CR2) 2017; 13 Duan, Wang (CR10) 2016; 63 BA Olshausen (2661_CR30) 1996; 381 J Shell (2661_CR23) 2015; 293 B Samanta (2661_CR8) 2003; 17 M Cocconcelli (2661_CR6) 2012; 27 2661_CR28 2661_CR27 HH Liu (2661_CR5) 2014; 18 SJ Pan (2661_CR22) 2010; 20 XJ Guo (2661_CR17) 2016; 93 C Chen (2661_CR25) 2017; 38 2661_CR29 TY Wu (2661_CR20) 2016; 18 MD Sun (2661_CR15) 2019; 146 TY Wang (2661_CR3) 2014; 45 HM Liu (2661_CR18) 2014; 2014 XF Yuan (2661_CR13) 2018; 14 F Shen (2661_CR24) 2017; 30 2661_CR9 RC Duan (2661_CR10) 2016; 63 M Cerradam (2661_CR7) 2016; 70 HD Shao (2661_CR16) 2017; 95 SJ Pan (2661_CR26) 2011; 22 2661_CR31 Y Yang (2661_CR19) 2013; 46 SW Fei (2661_CR21) 2017; 5 L Wen (2661_CR14) 2019; 49 BY Yang (2661_CR2) 2017; 13 A Gretton (2661_CR32) 2012; 13 LC Yann (2661_CR12) 2015; 521 GH Chen (2661_CR1) 2016; 29 B Yang (2661_CR33) 2020; 67 2661_CR34 J Xu (2661_CR4) 2013; 3 DP Miguel (2661_CR11) 2013; 60 |
| References_xml | – volume: 13 start-page: 1321 issue: 3 year: 2017 end-page: 1331 ident: CR2 article-title: Fault diagnosis for a wind turbine generator bearing via sparse representation and shift-invariant K-SVD publication-title: IEEE Trans Ind Electron – volume: 3 start-page: 87 year: 2013 end-page: 118 ident: CR4 article-title: Fault diagnosis of complex industrial process using KICA and sparse SVM publication-title: Math Probl Eng – volume: 18 start-page: 292 issue: 8 year: 2016 ident: CR20 article-title: On multi-scale entropy analysis of order-tracking measurement for bearing fault diagnosis under variable speed publication-title: Entropy doi: 10.3390/e18080292 – volume: 146 start-page: 305 year: 2019 end-page: 314 ident: CR15 article-title: A sparse stacked denoising autoencoder with optimized transfer learning applied to the fault diagnosis of rolling bearings publication-title: Measurement doi: 10.1016/j.measurement.2019.06.029 – volume: 46 start-page: 2306 issue: 8 year: 2013 end-page: 2312 ident: CR19 article-title: A fault diagnosis approach for bearings based on VPMCD under variable speed conditio publication-title: Measurement doi: 10.1016/j.measurement.2013.03.023 – volume: 63 start-page: 3815 issue: 6 year: 2016 end-page: 3823 ident: CR10 article-title: Fault diagnosis of on-load tap-changer in converter transformer based on time-frequency vibration analysis publication-title: IEEE Trans Ind Electron doi: 10.1109/TIE.2016.2524399 – volume: 93 start-page: 490 year: 2016 end-page: 502 ident: CR17 article-title: Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis publication-title: Measurement doi: 10.1016/j.measurement.2016.07.054 – volume: 17 start-page: 317 issue: 2 year: 2003 end-page: 328 ident: CR8 article-title: Artificial neural network based fault diagnostics of rolling element bearings using time-domain features publication-title: Mech Syst Signal Process doi: 10.1006/mssp.2001.1462 – volume: 22 start-page: 199 issue: 2 year: 2011 end-page: 210 ident: CR26 article-title: Domain adaptation via transfer component analysis publication-title: IEEE Trans Neural Netw doi: 10.1109/TNN.2010.2091281 – volume: 67 start-page: 9747 issue: 11 year: 2020 end-page: 9757 ident: CR33 article-title: A polynomial kernel induced distance metric to improve deep transfer learning for fault diagnosis of machines publication-title: IEEE Trans Ind Electron doi: 10.1109/TIE.2019.2953010 – ident: CR29 – volume: 60 start-page: 3398 issue: 8 year: 2013 end-page: 3407 ident: CR11 article-title: Bearing fault detection by a novel condition-monitoring scheme based on statistical-time features and neural networks publication-title: IEEE Trans Ind Electron doi: 10.1109/TIE.2012.2219838 – ident: CR27 – volume: 13 start-page: 723 year: 2012 end-page: 773 ident: CR32 article-title: A kernal two-sample test publication-title: J Mach Learn Res – volume: 27 start-page: 667 issue: 1 year: 2012 end-page: 682 ident: CR6 article-title: An algorithm to diagnosis to diagnose ball bearing faults in servomotors running arbitrary motion profiles publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2011.09.003 – volume: 95 start-page: 187 year: 2017 end-page: 204 ident: CR16 article-title: A novel deep autoencoder feature learning method for rotating machinery fault diagnosis publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2017.03.034 – volume: 293 start-page: 59 year: 2015 end-page: 79 ident: CR23 article-title: Fuzzy transfer learning: methodology and application publication-title: Inf Sci doi: 10.1016/j.ins.2014.09.004 – volume: 30 start-page: 118 issue: 1 year: 2017 end-page: 126 ident: CR24 article-title: Application of singular value decomposition and transfer learning in motor fault diagnosis publication-title: J Vib Eng – volume: 521 start-page: 436 issue: 7553 year: 2015 end-page: 444 ident: CR12 article-title: Deep learning publication-title: Nature doi: 10.1038/nature14539 – volume: 49 start-page: 136 issue: 1 year: 2019 end-page: 144 ident: CR14 article-title: A new deep transfer learning based on sparse auto-encoder for fault diagnosis publication-title: IEEE Trans Syst Man Cybern Syst doi: 10.1109/TSMC.2017.2754287 – volume: 38 start-page: 33 issue: 1 year: 2017 end-page: 40 ident: CR25 article-title: Bearing Fault diagnosis based on improved LSSVM and transfer learning method publication-title: J Instrum – ident: CR31 – volume: 14 start-page: 3235 issue: 7 year: 2018 end-page: 3243 ident: CR13 article-title: Deep learning based feature representation and its application for soft sensor modeling with variable-wise weighted SAE publication-title: IEEE Trans Ind Electron – volume: 45 start-page: 139 issue: 1 year: 2014 end-page: 153 ident: CR3 article-title: Rolling element bearing fault diagnosis via fault characteristic order(FCO) analysis publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2013.11.011 – ident: CR9 – volume: 5 start-page: 269 issue: 3 year: 2017 end-page: 276 ident: CR21 article-title: Fault diagnosis of bearing under varying load conditions by utilizing composite features self-adaptive reduction-based RVM classifier publication-title: J Vib Eng Technol – ident: CR34 – volume: 381 start-page: 607 issue: 6583 year: 1996 end-page: 609 ident: CR30 article-title: Emergence of simple-cell receptive field properties by learning a sparse code for natural images publication-title: Nature doi: 10.1038/381607a0 – volume: 70 start-page: 87 issue: 1 year: 2016 end-page: 103 ident: CR7 article-title: Fault diagnosis in spur gears based on genetic algorithm and random forest publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2015.08.030 – volume: 2014 start-page: 765621 year: 2014 ident: CR18 article-title: Rolling bearing fault diagnosis under variable conditions using Hilbert–Huang transform and singular value decomposition publication-title: Math Probl Eng doi: 10.1155/2014/765621 – ident: CR28 – volume: 20 start-page: 1345 issue: 10 year: 2010 end-page: 1359 ident: CR22 article-title: A survey on transfer learning publication-title: IEEE Trans Knowl Data Eng doi: 10.1109/TKDE.2009.191 – volume: 29 start-page: 204 issue: 1 year: 2016 end-page: 211 ident: CR1 article-title: Improved CICA algorithm used for single channel compound fault diagnosis of Rolling Bearings publication-title: Chin J Mech Eng doi: 10.3901/CJME.2015.1026.127 – volume: 18 start-page: 67 issue: 75 year: 2014 end-page: 78 ident: CR5 article-title: A fault diagnosis method based on local mean decomposition and multi-scale entropy for roller bearings publication-title: Mech Mach Theory doi: 10.1016/j.mechmachtheory.2014.01.011 – volume: 13 start-page: 723 year: 2012 ident: 2661_CR32 publication-title: J Mach Learn Res – volume: 60 start-page: 3398 issue: 8 year: 2013 ident: 2661_CR11 publication-title: IEEE Trans Ind Electron doi: 10.1109/TIE.2012.2219838 – ident: 2661_CR29 doi: 10.1109/CVPR.2017.547 – volume: 63 start-page: 3815 issue: 6 year: 2016 ident: 2661_CR10 publication-title: IEEE Trans Ind Electron doi: 10.1109/TIE.2016.2524399 – volume: 18 start-page: 67 issue: 75 year: 2014 ident: 2661_CR5 publication-title: Mech Mach Theory doi: 10.1016/j.mechmachtheory.2014.01.011 – volume: 95 start-page: 187 year: 2017 ident: 2661_CR16 publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2017.03.034 – volume: 18 start-page: 292 issue: 8 year: 2016 ident: 2661_CR20 publication-title: Entropy doi: 10.3390/e18080292 – ident: 2661_CR28 doi: 10.1109/ICCV.2013.368 – ident: 2661_CR9 doi: 10.1109/DEMPED.2007.4393063 – volume: 67 start-page: 9747 issue: 11 year: 2020 ident: 2661_CR33 publication-title: IEEE Trans Ind Electron doi: 10.1109/TIE.2019.2953010 – volume: 3 start-page: 87 year: 2013 ident: 2661_CR4 publication-title: Math Probl Eng – volume: 14 start-page: 3235 issue: 7 year: 2018 ident: 2661_CR13 publication-title: IEEE Trans Ind Electron – volume: 20 start-page: 1345 issue: 10 year: 2010 ident: 2661_CR22 publication-title: IEEE Trans Knowl Data Eng doi: 10.1109/TKDE.2009.191 – ident: 2661_CR34 – volume: 70 start-page: 87 issue: 1 year: 2016 ident: 2661_CR7 publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2015.08.030 – volume: 521 start-page: 436 issue: 7553 year: 2015 ident: 2661_CR12 publication-title: Nature doi: 10.1038/nature14539 – volume: 27 start-page: 667 issue: 1 year: 2012 ident: 2661_CR6 publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2011.09.003 – ident: 2661_CR27 doi: 10.1109/ICCV.2013.274 – volume: 45 start-page: 139 issue: 1 year: 2014 ident: 2661_CR3 publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2013.11.011 – volume: 17 start-page: 317 issue: 2 year: 2003 ident: 2661_CR8 publication-title: Mech Syst Signal Process doi: 10.1006/mssp.2001.1462 – volume: 93 start-page: 490 year: 2016 ident: 2661_CR17 publication-title: Measurement doi: 10.1016/j.measurement.2016.07.054 – volume: 2014 start-page: 765621 year: 2014 ident: 2661_CR18 publication-title: Math Probl Eng doi: 10.1155/2014/765621 – volume: 29 start-page: 204 issue: 1 year: 2016 ident: 2661_CR1 publication-title: Chin J Mech Eng doi: 10.3901/CJME.2015.1026.127 – volume: 46 start-page: 2306 issue: 8 year: 2013 ident: 2661_CR19 publication-title: Measurement doi: 10.1016/j.measurement.2013.03.023 – volume: 293 start-page: 59 year: 2015 ident: 2661_CR23 publication-title: Inf Sci doi: 10.1016/j.ins.2014.09.004 – ident: 2661_CR31 doi: 10.1145/1390156.1390294 – volume: 30 start-page: 118 issue: 1 year: 2017 ident: 2661_CR24 publication-title: J Vib Eng – volume: 5 start-page: 269 issue: 3 year: 2017 ident: 2661_CR21 publication-title: J Vib Eng Technol – volume: 49 start-page: 136 issue: 1 year: 2019 ident: 2661_CR14 publication-title: IEEE Trans Syst Man Cybern Syst doi: 10.1109/TSMC.2017.2754287 – volume: 38 start-page: 33 issue: 1 year: 2017 ident: 2661_CR25 publication-title: J Instrum – volume: 146 start-page: 305 year: 2019 ident: 2661_CR15 publication-title: Measurement doi: 10.1016/j.measurement.2019.06.029 – volume: 13 start-page: 1321 issue: 3 year: 2017 ident: 2661_CR2 publication-title: IEEE Trans Ind Electron – volume: 22 start-page: 199 issue: 2 year: 2011 ident: 2661_CR26 publication-title: IEEE Trans Neural Netw doi: 10.1109/TNN.2010.2091281 – volume: 381 start-page: 607 issue: 6583 year: 1996 ident: 2661_CR30 publication-title: Nature doi: 10.1038/381607a0 |
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| SubjectTerms | Algorithms Engineering Fault diagnosis Feature extraction Fourier transforms Learning Mechanical Engineering Noise reduction Roller bearings Statistical methods Technical Paper Vibration Working conditions |
| Title | The fault diagnosis method of rolling bearing under variable working conditions based on deep transfer learning |
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