An intelligent diagnosis scheme based on generative adversarial learning deep neural networks and its application to planetary gearbox fault pattern recognition
•A new diagnostic model named SDAE-GAN is proposed.•The model combines Generative Adversarial Networks and Stacked Denoising Autoencoders.•The performance of the model is investigated in the planetary gearbox experiment platform.•Results suggest that SDAE-GAN is better than SDAE and other common dia...
Saved in:
| Published in: | Neurocomputing (Amsterdam) Vol. 310; pp. 213 - 222 |
|---|---|
| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier B.V
08.10.2018
|
| Subjects: | |
| ISSN: | 0925-2312, 1872-8286 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | •A new diagnostic model named SDAE-GAN is proposed.•The model combines Generative Adversarial Networks and Stacked Denoising Autoencoders.•The performance of the model is investigated in the planetary gearbox experiment platform.•Results suggest that SDAE-GAN is better than SDAE and other common diagnostic models in classification precision.
Planetary gearbox has complex structures and works under various non-stationary operating conditions. The vibration signals of planetary gearbox are complicated and usually polluted by noise and interference. It is difficult to extract effective features of early faults. In addition, there are only a small number of fault samples for planetary gearbox fault diagnosis. All of these increase the difficulty of planetary gearbox fault diagnosis. Aiming at these problems, a novel fault diagnostic method is proposed which combines Generative Adversarial Networks (GAN) and Stacked Denoising Autoencoders (SDAE). The generator of GAN can generate new samples which has similar distribution with original samples from planetary gearbox vibration signals. Then, generated samples are transformed to the discriminator together with original samples which expand the sample size. SDAE is used as the discriminator of GAN which can automatically extract effective fault features from input samples and discriminate their authenticity and fault categories. Through novel adversarial machine learning mechanism, the generator and discriminator are concurrently optimized to enhance the quality of generation samples and the ability of fault mode classification. The experimental results show that the developed SDAE-GAN method for planetary gearbox has good anti-noise ability and achieve better fault diagnosis performance in the case of small samples. |
|---|---|
| AbstractList | •A new diagnostic model named SDAE-GAN is proposed.•The model combines Generative Adversarial Networks and Stacked Denoising Autoencoders.•The performance of the model is investigated in the planetary gearbox experiment platform.•Results suggest that SDAE-GAN is better than SDAE and other common diagnostic models in classification precision.
Planetary gearbox has complex structures and works under various non-stationary operating conditions. The vibration signals of planetary gearbox are complicated and usually polluted by noise and interference. It is difficult to extract effective features of early faults. In addition, there are only a small number of fault samples for planetary gearbox fault diagnosis. All of these increase the difficulty of planetary gearbox fault diagnosis. Aiming at these problems, a novel fault diagnostic method is proposed which combines Generative Adversarial Networks (GAN) and Stacked Denoising Autoencoders (SDAE). The generator of GAN can generate new samples which has similar distribution with original samples from planetary gearbox vibration signals. Then, generated samples are transformed to the discriminator together with original samples which expand the sample size. SDAE is used as the discriminator of GAN which can automatically extract effective fault features from input samples and discriminate their authenticity and fault categories. Through novel adversarial machine learning mechanism, the generator and discriminator are concurrently optimized to enhance the quality of generation samples and the ability of fault mode classification. The experimental results show that the developed SDAE-GAN method for planetary gearbox has good anti-noise ability and achieve better fault diagnosis performance in the case of small samples. |
| Author | Wang, Zirui Wang, Youren Wang, Jun |
| Author_xml | – sequence: 1 givenname: Zirui surname: Wang fullname: Wang, Zirui email: zwang162@sheffield.ac.uk – sequence: 2 givenname: Jun surname: Wang fullname: Wang, Jun – sequence: 3 givenname: Youren surname: Wang fullname: Wang, Youren |
| BookMark | eNotkE1OwzAQhS1UJNrCDVj4Agm2EzvJBqmq-JMqsYG15cTj4pLake0WuA1HxVVZPWnmzZunb4FmzjtA6JaSkhIq7nalg8Pg9yUjtC0JLwmrL9Cctg0rWtaKGZqTjvGCVZRdoUWMO0JoQ1k3R78rh61LMI52Cy5hbdXW-WgjjsMH7AH3KoLG3uG8hqCSPQJW-gghqmDViEdQwVm3xRpgwrlHyEMH6cuHz4iV09imrNM02iFf56Dk8TSqbFHhJ6eq0PtvbNRhTHhSKUFwOMDgt86e7Nfo0qgxws2_LtH748Pb-rnYvD69rFebApioUtGYqmN1z4hpuSK86irOOsOYYb0Rg-mHuuaip6CBt6CFgZ6LptdCNU1NtdDVEt2fcyE_OVoIMg4W3ADa5jZJam8lJfLEW-7kmbc88ZaEy8y7-gPNC35G |
| ContentType | Journal Article |
| Copyright | 2018 Elsevier Ltd |
| Copyright_xml | – notice: 2018 Elsevier Ltd |
| DOI | 10.1016/j.neucom.2018.05.024 |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1872-8286 |
| EndPage | 222 |
| ExternalDocumentID | S0925231218305617 |
| GroupedDBID | --- --K --M .DC .~1 0R~ 123 1B1 1~. 1~5 4.4 457 4G. 53G 5VS 7-5 71M 8P~ 9JM 9JN AABNK AACTN AADPK AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAXLA AAXUO AAYFN ABBOA ABCQJ ABFNM ABJNI ABMAC ABYKQ ACDAQ ACGFS ACRLP ACZNC ADBBV ADEZE AEBSH AEKER AENEX AFKWA AFTJW AFXIZ AGHFR AGUBO AGWIK AGYEJ AHHHB AHZHX AIALX AIEXJ AIKHN AITUG AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD AXJTR BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ IHE J1W KOM LG9 M41 MO0 MOBAO N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 RIG ROL RPZ SDF SDG SDP SES SPC SPCBC SSN SSV SSZ T5K ZMT ~G- |
| ID | FETCH-LOGICAL-e263t-7f3924b20f85a05393529f22f2bf6cfbc4456b1ede58ed6feb567bd6a7741d6d3 |
| ISICitedReferencesCount | 321 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000437299800019&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0925-2312 |
| IngestDate | Fri Feb 23 02:30:23 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Fault diagnosis Generative adversarial networks Deep stacked denoising autoencoders Adversarial machine learning Planetary gearbox Small samples |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-e263t-7f3924b20f85a05393529f22f2bf6cfbc4456b1ede58ed6feb567bd6a7741d6d3 |
| PageCount | 10 |
| ParticipantIDs | elsevier_sciencedirect_doi_10_1016_j_neucom_2018_05_024 |
| PublicationCentury | 2000 |
| PublicationDate | 2018-10-08 |
| PublicationDateYYYYMMDD | 2018-10-08 |
| PublicationDate_xml | – month: 10 year: 2018 text: 2018-10-08 day: 08 |
| PublicationDecade | 2010 |
| PublicationTitle | Neurocomputing (Amsterdam) |
| PublicationYear | 2018 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Ratliff, Burden, Sastry (bib0033) 2013 Sun, Wang (bib0003) 2017; 38 Zhang, Su, Pu (bib0014) 2014; 50 Sawalhi, Randall (bib0002) 2014; 42 Tamilselvan, Wang (bib0019) 2013; 115 Vincent, Larochelle, Lajoie (bib0030) 2010; 11 Khazaee, Ahmadi, Omid (bib0037) 2014; 228 Bengio (bib0028) 2009; 2 Li, Yang, Li (bib0022) 2017; 91 Erhan, Bengio, Courville (bib0029) 2010; 11 Zhang, Khawaja, Patrick (bib0007) 2008; 13 Golafshan, Sanliturk (bib0013) 2016; 70–71 Qing, Guiming, Qingfei (bib0012) 2011 2015. Zhao, Jia (bib0006) 2017; 94 Weidong, Shuseng (bib0011) 2015; 36 Laha (bib0008) 2017; 100 Swami, Sharma, Jain (bib0034) 2015; 70 Jia, Lei, Guo (bib0017) 2018; 272 Gan, Wang, Zhu (bib0020) 2016; 72–73 Feng, Liang, Chu (bib0004) 2013; 38 Sun, Shao, Yan (bib0021) 2016; 52 Yang, Pan, Li (bib0010) 2015; 34 K. Diederik and B. Jimmy. ADAM: a method for stochastic optimization. arXiv preprint arXiv Lei, Lin, Zuo, He (bib0001) 2014; 48 Chen, Zi, He (bib0005) 2013; 38 Liu, Qu, Zuo (bib0038) 2013; 67 Coates, Y. Ng, Lee (bib0035) 2011; 15 Xu, Zhao, Ma, Hou (bib0009) 2016; 31 Lei, Jia, Zhou (bib0016) 2015; 51 Wang, Gou, Duan (bib0032) 2017; 43 Guo, Chen, Shen (bib0015) 2016; 93 2014. Shao, Jiang, Zhao (bib0024) 2017; 95 Goodfellow, Pougetabadie, Mirza (bib0025) 2014; 3 A. Radford, L. Metz, and S. Chintala. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv Hinton, Salakhutdinov (bib0023) 2006; 313 2016. Hao, Yi, Qu (bib0018) 2018; 275 A. Odena, C. Olah, and J. Shlens. Conditional image synthesis with auxiliary classifier GANs. arXiv preprint arXiv Lei, Jia, Lin (bib0036) 2016; 63 |
| References_xml | – volume: 94 start-page: 129 year: 2017 end-page: 147 ident: bib0006 article-title: A novel strategy for signal denoising using reweighted SVD and its applications to weak fault feature enhancement of rotating machinery publication-title: Mech. Syst. Signal Process. – volume: 51 start-page: 49 year: 2015 end-page: 56 ident: bib0016 article-title: A deep learning-based method for machinery health monitoring with big data publication-title: J. Mech. Eng. – volume: 313 start-page: 504 year: 2006 end-page: 507 ident: bib0023 article-title: Reducing the dimensionality of data with neural networks publication-title: Science – start-page: 1 year: 2011 end-page: 4 ident: bib0012 article-title: The research of pattern recognition of gear pump based on EMD and KPCA-SVM publication-title: Proceedings of the International Conference on System Science, Engineering Design and Manufacturing Informatization(ICSEM) – volume: 115 start-page: 124 year: 2013 end-page: 135 ident: bib0019 article-title: Failure diagnosis using deep belief learning based health state classification publication-title: Reliab. Eng. Syst. Saf. – volume: 11 start-page: 3371 year: 2010 end-page: 3408 ident: bib0030 article-title: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion publication-title: J. Mach. Learn. Res. – volume: 15 start-page: 215 year: 2011 end-page: 223 ident: bib0035 article-title: An analysis of single-layer networks in unsupervised feature learning publication-title: J. Mach. Learn. Res. – volume: 31 start-page: 219 year: 2016 end-page: 226 ident: bib0009 article-title: Denoising method based on dual-tree complex wavelet transform and MCA and its application in gear fault diagnosis publication-title: J. Aerosp. Power – volume: 38 start-page: 165 year: 2013 end-page: 205 ident: bib0004 article-title: Recent advances in time frequency analysis methods for machinery fault diagnosis: a review with application examples publication-title: Mech. Syst. Signal Process. – reference: , 2014. – volume: 95 start-page: 187 year: 2017 end-page: 204 ident: bib0024 article-title: A novel deep autoencoder feature learning method for rotating machinery fault diagnosis publication-title: Mech. Syst. Signal Process. – volume: 43 start-page: 321 year: 2017 end-page: 332 ident: bib0032 article-title: Generative adversarial networks: the state of the art and beyond publication-title: Acta Autom. Sin. – reference: K. Diederik and B. Jimmy. ADAM: a method for stochastic optimization. arXiv preprint arXiv: – volume: 13 start-page: 558 year: 2008 end-page: 565 ident: bib0007 article-title: Blind deconvolution denoising for helicopter vibration signals publication-title: IEEE/ASME Trans. Mechatron. – volume: 272 start-page: 619 year: 2018 end-page: 628 ident: bib0017 article-title: A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines publication-title: Neurocomputing – volume: 228 start-page: 21 year: 2014 end-page: 32 ident: bib0037 article-title: Classifier fusion of vibration and acoustic signals for fault diagnosis and classification of planetary gears based on Dempster–Shafer evidence theory publication-title: J. Process Mech. Eng. – volume: 38 start-page: 020892.1 year: 2017 end-page: 14 ident: bib0003 article-title: Advance in the study on fault diagnosis of helicopter planetary gears publication-title: Acta Aeronautica Et Astronautica Sinica – volume: 2 start-page: 1 year: 2009 end-page: 127 ident: bib0028 article-title: Learning deep architectures for AI publication-title: Found. Trends® Mach. Learn. – volume: 3 start-page: 2672 year: 2014 end-page: 2680 ident: bib0025 article-title: Generative adversarial networks publication-title: Adv. Neural Inf. Process. Syst. – reference: , 2015. – volume: 38 start-page: 549 year: 2013 end-page: 568 ident: bib0005 article-title: Adaptive redundant multiwavelet denoising with improved neighboring coefficients for gearbox fault detection publication-title: Mech. Syst. Signal Process. – start-page: 917 year: 2013 end-page: 924 ident: bib0033 article-title: Characterization and computation of local Nash equilibria in continuous games publication-title: Proceedings of the Fifty First Annual Allerton Conference on Communication, Control, and Computing – reference: A. Odena, C. Olah, and J. Shlens. Conditional image synthesis with auxiliary classifier GANs. arXiv preprint arXiv: – reference: A. Radford, L. Metz, and S. Chintala. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv: – volume: 63 start-page: 1 year: 2016 ident: bib0036 article-title: An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data publication-title: IEEE Trans. Ind. Electron. – volume: 70 start-page: 1 year: 2015 end-page: 12 ident: bib0034 article-title: Speech enhancement by noise driven adaptation of perceptual scales and thresholds of continuous wavelet transform coefficients publication-title: Speech Commun. – volume: 11 start-page: 625 year: 2010 end-page: 660 ident: bib0029 article-title: Why does unsupervised pre-training help deep learning? publication-title: J. Mach. Learn. Res. – volume: 70–71 start-page: 36 year: 2016 end-page: 50 ident: bib0013 article-title: SVD and Hankel matrix based de-noising approach for ball bearing fault detection and its assessment using artificial faults publication-title: Mech. Syst. Signal Process. – volume: 42 start-page: 368 year: 2014 end-page: 376 ident: bib0002 article-title: Gear parameter identification in a wind turbine gearbox using vibration signals publication-title: Mech. Syst. Signal Process. – volume: 72–73 start-page: 92 year: 2016 end-page: 104 ident: bib0020 article-title: Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings publication-title: Mech. Syst. Signal Process. – volume: 91 start-page: 295 year: 2017 end-page: 312 ident: bib0022 article-title: A fault diagnosis scheme for planetary gearboxes using modified multi-scale symbolic dynamic entropy and mRMR feature selection publication-title: Mech. Syst. Signal Process. – volume: 50 start-page: 70 year: 2014 end-page: 77 ident: bib0014 article-title: Gear fault diagnosis method using ensemble empirical mode decomposition energy distribution and gray similar incidence publication-title: J. Mech. Eng. – volume: 52 start-page: 65 year: 2016 end-page: 71 ident: bib0021 article-title: Induction motor fault diagnosis based on deep neural network of sparse auto-encoder publication-title: J. Mech. Eng. – volume: 100 start-page: 157 year: 2017 end-page: 163 ident: bib0008 article-title: Enhancement of fault diagnosis of rolling element bearing using maximum kurtosis fast nonlocal means denoising publication-title: Measurement – volume: 36 start-page: 1861 year: 2015 end-page: 1870 ident: bib0011 article-title: Overall-improved fault diagnosis approach based on support vector machine publication-title: Chin. J. Sci. Instrum. – volume: 275 start-page: 2111 year: 2018 end-page: 2125 ident: bib0018 article-title: A novel adaptive fault detection methodology for complex system using deep belief networks and multiple models: a case study on cryogenic propellant loading system publication-title: Neurocomputing – volume: 67 start-page: 1217 year: 2013 end-page: 1230 ident: bib0038 article-title: Fault level diagnosis for planetary gearboxes using hybrid kernel feature selection and kernel fisher discriminant analysis publication-title: Int. J. Adv. Manuf. Technol. – volume: 48 start-page: 292 year: 2014 end-page: 305 ident: bib0001 article-title: Condition monitoring and fault diagnosis of planetary gearboxes: a review publication-title: Measurement – reference: , 2016. – volume: 93 start-page: 490 year: 2016 end-page: 502 ident: bib0015 article-title: Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis publication-title: Measurement – volume: 34 start-page: 49 year: 2015 end-page: 54 ident: bib0010 article-title: A novel incremental semi-supervised VPMCD gear fault on-line diagnosis method publication-title: J. Vib. Shock |
| SSID | ssj0017129 |
| Score | 2.6464396 |
| Snippet | •A new diagnostic model named SDAE-GAN is proposed.•The model combines Generative Adversarial Networks and Stacked Denoising Autoencoders.•The performance of... |
| SourceID | elsevier |
| SourceType | Publisher |
| StartPage | 213 |
| SubjectTerms | Adversarial machine learning Deep stacked denoising autoencoders Fault diagnosis Generative adversarial networks Planetary gearbox Small samples |
| Title | An intelligent diagnosis scheme based on generative adversarial learning deep neural networks and its application to planetary gearbox fault pattern recognition |
| URI | https://dx.doi.org/10.1016/j.neucom.2018.05.024 |
| Volume | 310 |
| WOSCitedRecordID | wos000437299800019&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1872-8286 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017129 issn: 0925-2312 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtZ1Li9swEMdFuttDL32XdvtgDr0Fl0R-H03Z0u5hKXRLQy_Gei0JWSck9pKP08-xn64zluSY5NIWeglB4ER4foxGo5m_GHufYSiUyYkKkkRUQWQyGYgqNgHG8iqPZJhE0l42kV5eZrNZ_nU0uvO9MLfLtK6z3S5f_1dT4xgam1pn_8Lc_Y_iAH5Ho-Mnmh0__8jwRd1pQFidzYaSq1RLN9-OcR-rb_SY1i1FZwTXneJ0VzpU0bXM26q7wWPpkyVK6_WY9C5xsLbV4tv-rGFw8E3h65pqZhuqwLvGx8VqNzZVu2xItpUyjuO-TslRsPCiUS0uoN3FEi5lUdyQcoMiTPsUxQ-X0_4537Tzw8GLtj4cQg_mO9xcPmPqxGX3SbajRhubreRxgKGoddza-uos5V0X_NCZh65I1rvjcLCyc9sBfbRo2PzF4gO-Uaogokl1aq62uftAjvsbTYVmgr6Qdl_pPXbK0zhHj3pafDmfXfRnWOmUW6VHN3XfuNlVFx7_1yAaGkQ4V4_ZQ7c1gcIi9YSNdP2UPfLXfoBbBZ6xX0UNA8KgJwwsYdARBqsa9oTBgDDwhAERBpYw8IQBEgZIGAwIg2YFPWHgCIOOMHCEwYCw5-z7p_Orj58Dd9FHoHkSNkFqMEqPBJ-YLK5wVchxV5Abzg0XJpFGyAjDfDHVSseZVonRIk5SoZIK9y5TlajwBTupV7V-yUBlSpPGnIwMSfDnmdA8jKUUMsm5NNUrlvq3XLoY08aOJdJQ-pLHRWntU5J9yklcon3O_vnJ1-zBHvQ37KTZtPotuy9vm_l2885h8xsjOq4I |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=An+intelligent+diagnosis+scheme+based+on+generative+adversarial+learning+deep+neural+networks+and+its+application+to+planetary+gearbox+fault+pattern+recognition&rft.jtitle=Neurocomputing+%28Amsterdam%29&rft.au=Wang%2C+Zirui&rft.au=Wang%2C+Jun&rft.au=Wang%2C+Youren&rft.date=2018-10-08&rft.pub=Elsevier+B.V&rft.issn=0925-2312&rft.eissn=1872-8286&rft.volume=310&rft.spage=213&rft.epage=222&rft_id=info:doi/10.1016%2Fj.neucom.2018.05.024&rft.externalDocID=S0925231218305617 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0925-2312&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0925-2312&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0925-2312&client=summon |