Health Indicator for Low-Speed Axial Bearings Using Variational Autoencoders

This paper proposes a method for calculating a health indicator (HI) for low-speed axial rolling element bearing (REB) health assessment by utilizing the latent representation obtained by variational inference using Variational Autoencoders (VAEs), trained on each speed reference in the dataset. Fur...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	IEEE access Ročník 8; s. 35842 - 35852
Hlavní autoři:	Hemmer, Martin, Klausen, Andreas, Khang, Huynh Van, Robbersmyr, Kjell G., Waag, Tor I.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Autoregressive models Autoregressive processes Bearing fault detection condition monitoring conditional variational autoencoder Data points Datasets Decoding Dimensionality reduction Drilling machines Drilling machines (tools) False alarms Fault detection generative models Industries Low speed Offshore drilling rigs Roller bearings Rolling bearings Training unsupervised learning variational autoencoder
ISSN:	2169-3536, 2169-3536
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	This paper proposes a method for calculating a health indicator (HI) for low-speed axial rolling element bearing (REB) health assessment by utilizing the latent representation obtained by variational inference using Variational Autoencoders (VAEs), trained on each speed reference in the dataset. Further, versatility is added by conditioning on the speed, extending the VAE to a conditional VAE (CVAE), thereby incorporating all speeds in a single model. Within the framework, the coefficients of autoregressive (AR) models are used as features. The dimensionality reduction inherent in the proposed method lowers the need of expert knowledge to design good condition indicators. Moreover, the suggested methodology allows for setting the probability of false alarms when encoding new data points to the latent variable space using the trained model. The effectiveness of the proposed method is validated based on two different datasets: from a workshop test of an offshore drilling machine and from an in-house test rig for axial bearings. In both datasets, the HI is exceeding the warning and alarm levels with a probability of false alarm (PFA) of 10 -6 , and the method is most effective at lower shaft speeds.
AbstractList	This paper proposes a method for calculating a health indicator (HI) for low-speed axial rolling element bearing (REB) health assessment by utilizing the latent representation obtained by variational inference using Variational Autoencoders (VAEs), trained on each speed reference in the dataset. Further, versatility is added by conditioning on the speed, extending the VAE to a conditional VAE (CVAE), thereby incorporating all speeds in a single model. Within the framework, the coefficients of autoregressive (AR) models are used as features. The dimensionality reduction inherent in the proposed method lowers the need of expert knowledge to design good condition indicators. Moreover, the suggested methodology allows for setting the probability of false alarms when encoding new data points to the latent variable space using the trained model. The effectiveness of the proposed method is validated based on two different datasets: from a workshop test of an offshore drilling machine and from an in-house test rig for axial bearings. In both datasets, the HI is exceeding the warning and alarm levels with a probability of false alarm (PFA) of 10-6, and the method is most effective at lower shaft speeds.
Author	Robbersmyr, Kjell G. Waag, Tor I. Hemmer, Martin Khang, Huynh Van Klausen, Andreas
Author_xml	– sequence: 1 givenname: Martin orcidid: 0000-0001-8751-8731 surname: Hemmer fullname: Hemmer, Martin email: martin.hemmer@uia.no organization: Department of Engineering Sciences, University of Agder, Grimstad, Norway – sequence: 2 givenname: Andreas orcidid: 0000-0002-5411-3655 surname: Klausen fullname: Klausen, Andreas organization: Department of Engineering Sciences, University of Agder, Grimstad, Norway – sequence: 3 givenname: Huynh Van orcidid: 0000-0002-0480-6859 surname: Khang fullname: Khang, Huynh Van organization: Department of Engineering Sciences, University of Agder, Grimstad, Norway – sequence: 4 givenname: Kjell G. orcidid: 0000-0001-9578-7325 surname: Robbersmyr fullname: Robbersmyr, Kjell G. organization: Department of Engineering Sciences, University of Agder, Grimstad, Norway – sequence: 5 givenname: Tor I. orcidid: 0000-0002-0530-3866 surname: Waag fullname: Waag, Tor I. organization: NORCE Norwegian Research Centre AS, Grimstad, Norway
BookMark	eNqFUU1PGzEQtSoqQSm_gMtKnDfY3l1_HNOIlkiROAS4Wv4YU0fLOrUdUf59DYsQ6oWRrBnPzHtjz_uGjqY4AULnBC8IwfJyuVpdbbcLiileUMl72dMv6IQSJttu6NjRh_gYneW8w9VETQ38BG2uQY_ld7OeXLC6xNT4ejbxqd3uAVyz_Bv02PwAncL0kJu7XF1zX2-6hDjV0vJQIkw2Okj5O_rq9Zjh7M2fotufV7er63Zz82u9Wm5a22NRWmqAeOstH8TA-t5jw33PKHdSYMqptNJYYwYPnIEAYbUxbADjvDOuY6Y7ReuZ1kW9U_sUHnV6VlEH9ZqI6UHpVIIdQWEpCRAqsCa6LsZqITB3HoOxnmrvK9fFzLVP8c8BclG7eEj1Y1nRfug5Yx2htaubu2yKOSfw71MJVi8iqFkE9SKCehOhouR_KBvK695K0mH8BHs-YwMAvE-TGBNc3_MPFPuXgg
CODEN	IAECCG
CitedBy_id	crossref_primary_10_1080_00295639_2024_2372515 crossref_primary_10_1016_j_ress_2024_110121 crossref_primary_10_1088_1361_6501_ad4732 crossref_primary_10_3390_app112311516 crossref_primary_10_1016_j_measurement_2022_112108 crossref_primary_10_1080_17445302_2023_2211241 crossref_primary_10_1016_j_ress_2021_107805 crossref_primary_10_1109_ACCESS_2024_3370949 crossref_primary_10_1007_s00170_020_06338_y crossref_primary_10_1109_JSEN_2024_3409904 crossref_primary_10_32604_cmes_2022_019521 crossref_primary_10_1109_TCYB_2022_3228524 crossref_primary_10_1109_JSEN_2024_3416958 crossref_primary_10_1109_JSEN_2020_3040696 crossref_primary_10_1109_ACCESS_2023_3255417 crossref_primary_10_3390_en16124544 crossref_primary_10_1007_s10845_021_01822_y crossref_primary_10_1109_JSEN_2023_3309013 crossref_primary_10_1109_TIM_2021_3072131 crossref_primary_10_1109_TIM_2022_3212547 crossref_primary_10_1016_j_isatra_2020_12_052 crossref_primary_10_1016_j_neucom_2025_129588 crossref_primary_10_1002_int_23008 crossref_primary_10_1016_j_measurement_2021_110460 crossref_primary_10_1016_j_pnucene_2024_105114 crossref_primary_10_1016_j_ins_2024_120635
Cites_doi	10.1016/S0301-679X(99)00077-8 10.1007/s12206-017-0306-y 10.1016/j.measurement.2019.02.046 10.1016/j.measurement.2019.01.033 10.1016/j.ymssp.2018.02.016 10.1109/TIM.2018.2806984 10.1016/j.apacoust.2015.10.028 10.1016/j.compind.2019.01.008 10.1109/ACCESS.2018.2888842 10.1016/j.ymssp.2015.10.020 10.1109/ISGTEurope.2016.7856316 10.1007/978-3-319-13560-1_20 10.1109/TIT.1968.1054102 10.1111/j.2517-6161.1995.tb02031.x 10.1016/j.eswa.2010.09.042 10.1109/TIM.2017.2669947 10.1109/TIE.2015.2509913 10.1109/ACCESS.2018.2877447 10.1109/ChiCC.2015.7260634 10.1109/TPEL.2014.2358494 10.1214/aoms/1177729694 10.1016/j.ymssp.2017.06.012 10.1016/S0963-8695(02)00006-3 10.3390/s18051389 10.1016/j.rser.2015.12.111 10.1016/j.ymssp.2010.07.017 10.1109/TIM.2014.2330494 10.1016/j.ymssp.2016.06.033 10.1016/j.ymssp.2017.03.034 10.1016/S0963-8695(02)00005-1 10.1016/j.measurement.2017.02.033 10.1177/1475921718788299 10.1109/AERO.2007.352867 10.1098/rspa.1931.0069 10.1109/ACCESS.2017.2728010 10.3390/designs2040056 10.1016/j.sigpro.2016.07.028 10.1109/PHM-Chongqing.2018.00156
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020
DBID	97E ESBDL RIA RIE AAYXX CITATION 7SC 7SP 7SR 8BQ 8FD JG9 JQ2 L7M L~C L~D DOA
DOI	10.1109/ACCESS.2020.2974942
DatabaseName	IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE Xplore Open Access Journals IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts METADEX Technology Research Database Materials Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional DOAJ Directory of Open Access Journals
DatabaseTitle	CrossRef Materials Research Database Engineered Materials Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Advanced Technologies Database with Aerospace METADEX Computer and Information Systems Abstracts Professional
DatabaseTitleList	Materials Research Database
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	2169-3536
EndPage	35852
ExternalDocumentID	oai_doaj_org_article_0991e1280a1a494ca8807df0ebcf2aff 10_1109_ACCESS_2020_2974942 9001012
Genre	orig-research
GrantInformation_xml	– fundername: Norges Forskningsråd grantid: 237896 funderid: 10.13039/501100005416
GroupedDBID	0R~ 4.4 5VS 6IK 97E AAJGR ABAZT ABVLG ACGFS ADBBV AGSQL ALMA_UNASSIGNED_HOLDINGS BCNDV BEFXN BFFAM BGNUA BKEBE BPEOZ EBS EJD ESBDL GROUPED_DOAJ IPLJI JAVBF KQ8 M43 M~E O9- OCL OK1 RIA RIE RNS AAYXX CITATION 7SC 7SP 7SR 8BQ 8FD JG9 JQ2 L7M L~C L~D
ID	FETCH-LOGICAL-c408t-2be1fcfc7585644f0b7f4627d9802729c9bcbb5fe76e8e8cabb65ebdfdbd36b3
IEDL.DBID	DOA
ISICitedReferencesCount	31
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000567617600006&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2169-3536
IngestDate	Fri Oct 03 12:46:19 EDT 2025 Sun Jun 29 16:13:41 EDT 2025 Tue Nov 18 22:06:01 EST 2025 Sat Nov 29 02:41:58 EST 2025 Wed Aug 27 02:35:31 EDT 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Language	English
License	https://creativecommons.org/licenses/by/4.0/legalcode
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c408t-2be1fcfc7585644f0b7f4627d9802729c9bcbb5fe76e8e8cabb65ebdfdbd36b3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0001-8751-8731 0000-0001-9578-7325 0000-0002-0480-6859 0000-0002-0530-3866 0000-0002-5411-3655
OpenAccessLink	https://doaj.org/article/0991e1280a1a494ca8807df0ebcf2aff
PQID	2454766312
PQPubID	4845423
PageCount	11
ParticipantIDs	crossref_primary_10_1109_ACCESS_2020_2974942 ieee_primary_9001012 crossref_citationtrail_10_1109_ACCESS_2020_2974942 doaj_primary_oai_doaj_org_article_0991e1280a1a494ca8807df0ebcf2aff proquest_journals_2454766312
PublicationCentury	2000
PublicationDate	20200000 2020-00-00 20200101 2020-01-01
PublicationDateYYYYMMDD	2020-01-01
PublicationDate_xml	– year: 2020 text: 20200000
PublicationDecade	2020
PublicationPlace	Piscataway
PublicationPlace_xml	– name: Piscataway
PublicationTitle	IEEE access
PublicationTitleAbbrev	Access
PublicationYear	2020
Publisher	IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml	– name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References	hemmer (ref30) 2017 ref12 benjamini (ref33) 2018; 57 ref15 stamboliska (ref4) 2014 ref14 ref53 ref55 ref11 ref10 ref17 ref16 ref19 bechhoefer (ref13) 2016; 7 ref18 ref51 loshchilov (ref48) 2017 ref45 box (ref54) 2015 ref43 ranzato (ref39) 2008 bechhoefer (ref5) 2016; 7 goodfellow (ref41) 2014 bechhoefer (ref32) 2011 ref8 ref7 ref9 ref3 ref6 arjovsky (ref44) 2017 yule (ref52) 1927; 226 ref35 ref34 ref37 ref36 ref31 ref2 ref1 ref38 kingma (ref47) 2015 kingma (ref40) 2013 clevert (ref50) 2015 abadi (ref46) 2015 srivastava (ref49) 2014; 15 ref24 ref23 ref26 ref25 ref20 ref22 ref21 ref28 makhzani (ref42) 2015 ref27 ref29
References_xml	– ident: ref1 doi: 10.1016/S0301-679X(99)00077-8 – ident: ref17 doi: 10.1007/s12206-017-0306-y – ident: ref14 doi: 10.1016/j.measurement.2019.02.046 – ident: ref6 doi: 10.1016/j.measurement.2019.01.033 – ident: ref18 doi: 10.1016/j.ymssp.2018.02.016 – ident: ref20 doi: 10.1109/TIM.2018.2806984 – ident: ref9 doi: 10.1016/j.apacoust.2015.10.028 – ident: ref27 doi: 10.1016/j.compind.2019.01.008 – ident: ref26 doi: 10.1109/ACCESS.2018.2888842 – year: 2017 ident: ref48 article-title: Decoupled weight decay regularization publication-title: arXiv 1711 05101 – ident: ref10 doi: 10.1016/j.ymssp.2015.10.020 – ident: ref55 doi: 10.1109/ISGTEurope.2016.7856316 – year: 2017 ident: ref44 article-title: Towards principled methods for training generative adversarial networks publication-title: arXiv 1701 04862 – year: 2015 ident: ref42 article-title: Adversarial autoencoders publication-title: arXiv 1511 05644 – start-page: 1185 year: 2008 ident: ref39 article-title: Sparse feature learning for deep belief networks publication-title: Proc Adv Neural Inf Process Syst – ident: ref24 doi: 10.1007/978-3-319-13560-1_20 – ident: ref34 doi: 10.1109/TIT.1968.1054102 – volume: 57 start-page: 289 year: 2018 ident: ref33 article-title: Controlling the false discovery rate: A practical and powerful approach to multiple testing publication-title: J Roy Statist Soc B Statist Methodol doi: 10.1111/j.2517-6161.1995.tb02031.x – ident: ref19 doi: 10.1016/j.eswa.2010.09.042 – ident: ref29 doi: 10.1109/TIM.2017.2669947 – ident: ref23 doi: 10.1109/TIE.2015.2509913 – ident: ref28 doi: 10.1109/ACCESS.2018.2877447 – ident: ref35 doi: 10.1109/ChiCC.2015.7260634 – ident: ref21 doi: 10.1109/TPEL.2014.2358494 – ident: ref43 doi: 10.1214/aoms/1177729694 – volume: 226 start-page: 267 year: 1927 ident: ref52 article-title: On a method of investigating periodicities disturbed series, with special reference to Wolfer's sunspot numbers publication-title: Philos Trans R Soc Lond Ser A Containing Papers Math Phys Character – ident: ref3 doi: 10.1016/j.ymssp.2017.06.012 – start-page: 2672 year: 2014 ident: ref41 article-title: Generative adversarial nets publication-title: Proc Adv Neural Inf Process Syst – start-page: 278 year: 2017 ident: ref30 article-title: A comparison of acoustic emission and vibration measurements for condition monitoring of an offshore drilling machine publication-title: Proc Annu Conf Prognostics Health Manage Soc – year: 2013 ident: ref40 article-title: Auto-encoding variational Bayes publication-title: arXiv 1312 6114 – year: 2015 ident: ref54 publication-title: Time Series Analysis Forecasting and Control – ident: ref8 doi: 10.1016/S0963-8695(02)00006-3 – volume: 7 start-page: 1 year: 2016 ident: ref13 article-title: Techniques for large, slow bearing fault detection publication-title: Int Conf Prognostics Health Manage – ident: ref16 doi: 10.3390/s18051389 – year: 2014 ident: ref4 publication-title: Proactive Condition Monitoring of Low-Speed Machines – ident: ref51 doi: 10.1016/j.rser.2015.12.111 – ident: ref2 doi: 10.1016/j.ymssp.2010.07.017 – ident: ref22 doi: 10.1109/TIM.2014.2330494 – ident: ref12 doi: 10.1016/j.ymssp.2016.06.033 – volume: 15 start-page: 1929 year: 2014 ident: ref49 article-title: Dropout: A simple way to prevent neural networks from overfitting publication-title: J Mach Learn Res – year: 2015 ident: ref46 article-title: TensorFlow: Large-scale machine learning on heterogeneous distributed systems publication-title: arXiv 1603 04467 – ident: ref36 doi: 10.1016/j.ymssp.2017.03.034 – ident: ref7 doi: 10.1016/S0963-8695(02)00005-1 – ident: ref15 doi: 10.1016/j.measurement.2017.02.033 – start-page: 275 year: 2011 ident: ref32 article-title: Gear health threshold setting based on a probability of false alarm publication-title: Proc Annu Conf Prognostics Health Manage Soc – ident: ref45 doi: 10.1177/1475921718788299 – ident: ref31 doi: 10.1109/AERO.2007.352867 – year: 2015 ident: ref50 article-title: Fast and accurate deep network learning by exponential linear units (ELUs) publication-title: arXiv 1511 07289 – year: 2015 ident: ref47 article-title: Adam: A method for stochastic optimization publication-title: arXiv 1412 6980 – ident: ref53 doi: 10.1098/rspa.1931.0069 – ident: ref37 doi: 10.1109/ACCESS.2017.2728010 – ident: ref25 doi: 10.3390/designs2040056 – ident: ref38 doi: 10.1016/j.sigpro.2016.07.028 – volume: 7 start-page: 1 year: 2016 ident: ref5 article-title: Fault detection on large slow bearings publication-title: Proc PHME – ident: ref11 doi: 10.1109/PHM-Chongqing.2018.00156
SSID	ssj0000816957
Score	2.361755
Snippet	This paper proposes a method for calculating a health indicator (HI) for low-speed axial rolling element bearing (REB) health assessment by utilizing the...
SourceID	doaj proquest crossref ieee
SourceType	Open Website Aggregation Database Enrichment Source Index Database Publisher
StartPage	35842
SubjectTerms	Autoregressive models Autoregressive processes Bearing fault detection condition monitoring conditional variational autoencoder Data points Datasets Decoding Dimensionality reduction Drilling machines Drilling machines (tools) False alarms Fault detection generative models Industries Low speed Offshore drilling rigs Roller bearings Rolling bearings Training unsupervised learning variational autoencoder
SummonAdditionalLinks	– databaseName: IEEE Electronic Library (IEL) dbid: RIE link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LS8QwEB508aAH3-L6IgePVtvYNsmxu7goLCK4iLeQpAkIsivaVX--kzQWRRG8lDY0bdovj28myTcAx7rITI00OVHK6CRXlibCYnMXyG2dNix1Qa7pbsyur_n9vbhZgJNuL4y1Niw-s6f-NMzl1zMz966yMxEU0bDDXWSsbPdqdf4UH0ACXxCFhbJUnFXDIX4DmoA0PaVIm0VOvw0-QaM_BlX50ROH4WW09r-CrcNqpJGkanHfgAU73YSVL-KCWzBudxiRq6mfikHTmiA_JePZW3L7hEMWqd6x6pEB1nTvLCdh7QC5w6voHiTVvJl5mUu_1HkbJqOLyfAyibETEpOnvEmotpkzznhzACmPSzVzeUlZLTgaolQYoY3WhbOstNxyo7QuC6trV-v6vNTnO9CbzqZ2Fwh3wqcwnhcup4pxgySG47HWDq3Dog_0859KE3XFfXiLRxnsi1TIFgjpgZARiD6cdJmeWlmNv28feLC6W70mdkhAFGRsYhK5bmZxuE1VpjCTUdg1sdqlVhtHlXN92PLIdQ-JoPXh4BN6Gdvvi6Re5wzJWEb3fs-1D8u-gK0z5gB6zfPcHsKSeW0eXp6PQtX8AFiL4is priority: 102 providerName: IEEE
Title	Health Indicator for Low-Speed Axial Bearings Using Variational Autoencoders
URI	https://ieeexplore.ieee.org/document/9001012 https://www.proquest.com/docview/2454766312 https://doaj.org/article/0991e1280a1a494ca8807df0ebcf2aff
Volume	8
WOSCitedRecordID	wos000567617600006&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: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2169-3536 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000816957 issn: 2169-3536 databaseCode: DOA dateStart: 20130101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2169-3536 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000816957 issn: 2169-3536 databaseCode: M~E dateStart: 20130101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1BS8MwFA4iHvQg6hSnc-Tg0bo0tk1y7IZDYQ7BId5CkiYgSCe6qSd_uy9pNiaCXrwEGtKkeXl5-V6afA-hU52npgKYnChldJIpSxNhYboLwLZOG0ZcoGu6H7HxmD88iNuVUF_-TFhDD9wIrgcIJrVgRIlKVSYyo0DhWOWI1cZR5Zy3voSJFWcq2GCeFtBcpBlKieiVgwH0CBxCSs4pgGiR0W9LUWDsjyFWftjlsNgMd9B2RIm4bL5uF63Zeg9trXAHttCouUCEr2v_pwU8ZwzwE4-m78ndM6xIuPwAzcJ9UGS_F47D0QB8D09x9w-X89nUs1j6k8z7aDK8nAyukhgaITEZ4bOEaps644xH-4BoHNHMZQVlleDgZ1JhhDZa586ywnLLjdK6yK2uXKWri0JfHKD1elrbQ4S5Ez6H8Sx3GVWMG8AoHNJKO3D-8jaiCyFJE2nDffSKJxncByJkI1npJSujZNvobPnSc8Oa8Xvxvpf-sqinvA4ZoAgyKoL8SxHaqOXHblmJCPR5UHdnMZYyTs9XST2NGWCtlB79R9PHaNN3p9mZ6aD12cvcnqAN8zZ7fH3pBs2E9ObzshvuF34BoLLoTA
linkProvider	Directory of Open Access Journals
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1bS8MwFD6ICuqDd3Fe8-Cj1TS2a_I4h0OxDsEhvoUkTUCQTXRTf74naVYURfCltKFp0365fOck-Q7Akc5TUyFNTpQyOsmUZYmw2NwFclunTUFdkGu6L4t-nz88iNsZOG72wlhrw-Ize-JPw1x-NTIT7yo7FUERDTvcuTzLGK13azUeFR9CAl8RpYVSKk473S5-BRqBjJ4wJM4iY9-Gn6DSH8Oq_OiLwwDTW_lf0VZhORJJ0qmRX4MZO1yHpS_yghtQ1nuMyNXQT8agcU2QoZJy9J7cPeOgRTofWPnIOdZ17y4nYfUAucer6CAkncl45IUu_WLnTRj0LgbdyyRGT0hMRvk4YdqmzjjjDQIkPY7qwmVtVlSCoynKhBHaaJ07W7Qtt9wordu51ZWrdHXW1mdbMDscDe02EO6ETyl4lruMqYIbpDEcj5V2aB_mLWDTfypNVBb3AS6eZLAwqJA1ENIDISMQLThuMj3Xwhp_337uwWpu9arYIQFRkLGRSWS7qcUBl6pUYSajsHMqKketNo4p51qw4ZFrHhJBa8HeFHoZW_CrZF7pDOlYynZ-z3UIC5eDm1KWV_3rXVj0ha1dM3swO36Z2H2YN2_jx9eXg1BNPwFAouVy
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=Health+Indicator+for+Low-Speed+Axial+Bearings+Using+Variational+Autoencoders&rft.jtitle=IEEE+access&rft.au=Hemmer%2C+Martin&rft.au=Klausen%2C+Andreas&rft.au=Khang%2C+Huynh+Van&rft.au=Robbersmyr%2C+Kjell+G.&rft.date=2020&rft.issn=2169-3536&rft.eissn=2169-3536&rft.volume=8&rft.spage=35842&rft.epage=35852&rft_id=info:doi/10.1109%2FACCESS.2020.2974942&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_ACCESS_2020_2974942
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2169-3536&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2169-3536&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2169-3536&client=summon