Robust Monitoring and Fault Isolation of Nonlinear Industrial Processes Using Denoising Autoencoder and Elastic Net

Robust process monitoring and reliable fault isolation in industrial processes usually encounter different challenges, including process nonlinearity and noise interference. In this brief, a novel method denoising autoencoder and elastic net (DAE-EN) is proposed to solve the aforementioned issues by...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE transactions on control systems technology Vol. 28; no. 3; pp. 1083 - 1091
Main Authors:	Yu, Wanke, Zhao, Chunhui
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.05.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Coders Data mining Denoising autoencoder (DAE) Discriminant analysis elastic net (EN) fault isolation Feature extraction Kernel kernel density estimation (KDE) Monitoring Neurons Noise reduction Nonlinearity Principal component analysis process monitoring Robustness Statistical methods
ISSN:	1063-6536, 1558-0865
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	Robust process monitoring and reliable fault isolation in industrial processes usually encounter different challenges, including process nonlinearity and noise interference. In this brief, a novel method denoising autoencoder and elastic net (DAE-EN) is proposed to solve the aforementioned issues by effectively integrating DAE and EN. The DAE is first trained to robustly capture the nonlinear structure of the industrial data. Then, the encoder network is updated into a sparse model using EN, so that the key variables associated with each neuron can be selected. After that two statistics are developed based on the extracted systematic structure and the retained residual information. In addition, another statistic is also constructed by combining the aforementioned two statistics to provide an overall measurement for the process sample. In this way, a robust monitoring model can be constructed to monitor the abnormal status in industrial processes. After the fault is detected, the faulty neurons are identified by the sparse exponential discriminant analysis, so that the associated faulty variables along each faulty neuron can thus be isolated. Two real industrial processes are used to validate the performance of the proposed method. Experimental results show that the proposed method can effectively detect the abnormal samples in industrial processes and accurately isolate the faulty variables from the normal ones.
AbstractList	Robust process monitoring and reliable fault isolation in industrial processes usually encounter different challenges, including process nonlinearity and noise interference. In this brief, a novel method denoising autoencoder and elastic net (DAE–EN) is proposed to solve the aforementioned issues by effectively integrating DAE and EN. The DAE is first trained to robustly capture the nonlinear structure of the industrial data. Then, the encoder network is updated into a sparse model using EN, so that the key variables associated with each neuron can be selected. After that two statistics are developed based on the extracted systematic structure and the retained residual information. In addition, another statistic is also constructed by combining the aforementioned two statistics to provide an overall measurement for the process sample. In this way, a robust monitoring model can be constructed to monitor the abnormal status in industrial processes. After the fault is detected, the faulty neurons are identified by the sparse exponential discriminant analysis, so that the associated faulty variables along each faulty neuron can thus be isolated. Two real industrial processes are used to validate the performance of the proposed method. Experimental results show that the proposed method can effectively detect the abnormal samples in industrial processes and accurately isolate the faulty variables from the normal ones.
Author	Yu, Wanke Zhao, Chunhui
Author_xml	– sequence: 1 givenname: Wanke orcidid: 0000-0002-3927-5656 surname: Yu fullname: Yu, Wanke email: yuwanke@zju.edu.cn organization: State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, China – sequence: 2 givenname: Chunhui orcidid: 0000-0002-0254-5763 surname: Zhao fullname: Zhao, Chunhui email: chhzhao@zju.edu.cn organization: State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, China
BookMark	eNp9kTlPxDAQhS0EEucPQDSWqLP4iJO4RMu1EpdgqaOxM0FGwQbbKfj3ZHcRBQXVvOJ9bzRv9sm2Dx4JOeZsxjnTZ8v583ImGNcz0ehal9UW2eNKNQVrKrU9aVbJolKy2iX7Kb0xxksl6j2SnoIZU6Z3wbscovOvFHxHr2AcMl2kMEB2wdPQ0_vgB-cRIl34bkKig4E-xmAxJUz0Ja3YC_TBrdX5mAN6GzqM68TLAVJ2lt5jPiQ7PQwJj37mAXm5ulzOb4rbh-vF_Py2sELLXHRYVtBpBRat1bLukBluLEBt6r7nRnamkQAN8MYIpXtTQa2UsZMPSxAgD8jpJvcjhs8RU27fwhj9tLIVUnNRccbl5Ko3LhtDShH71rq8vjpHcEPLWbtquF013K4abn8ankj-h_yI7h3i17_MyYZxiPjrn35UMiHlN-yTjHo
CODEN	IETTE2
CitedBy_id	crossref_primary_10_1002_acs_3888 crossref_primary_10_1109_TNNLS_2023_3291371 crossref_primary_10_1109_TIM_2023_3324003 crossref_primary_10_1016_j_conengprac_2022_105156 crossref_primary_10_3390_technologies12120237 crossref_primary_10_1016_j_engappai_2023_105838 crossref_primary_10_3390_math10224351 crossref_primary_10_1016_j_compchemeng_2021_107241 crossref_primary_10_1016_j_jprocont_2021_08_014 crossref_primary_10_1016_j_bspc_2021_103343 crossref_primary_10_1016_j_ifacol_2021_08_281 crossref_primary_10_1002_apj_70022 crossref_primary_10_1109_TII_2019_2951011 crossref_primary_10_1109_TCYB_2021_3109618 crossref_primary_10_1016_j_aei_2024_102470 crossref_primary_10_1002_cjce_25478 crossref_primary_10_1016_j_jii_2022_100410 crossref_primary_10_1016_j_jprocont_2022_06_011 crossref_primary_10_1088_1361_6501_ac145f crossref_primary_10_1109_TASE_2022_3230687 crossref_primary_10_1109_TIE_2023_3239861 crossref_primary_10_1109_TIM_2025_3545728 crossref_primary_10_1088_1361_6501_ac4f02 crossref_primary_10_1016_j_neucom_2022_03_012 crossref_primary_10_1109_ACCESS_2020_3015783 crossref_primary_10_1016_j_jmsy_2025_07_002 crossref_primary_10_1016_j_jprocont_2020_06_013 crossref_primary_10_1016_j_asoc_2024_112051 crossref_primary_10_1109_TASE_2019_2915286 crossref_primary_10_1109_TIE_2021_3105980 crossref_primary_10_1016_j_jprocont_2022_08_004 crossref_primary_10_1016_j_jprocont_2022_11_010 crossref_primary_10_1109_ACCESS_2020_2998792 crossref_primary_10_1007_s10586_025_05375_0 crossref_primary_10_1016_j_jfranklin_2025_107899 crossref_primary_10_1109_TII_2022_3174715 crossref_primary_10_1016_j_mfglet_2023_08_045 crossref_primary_10_1177_01423312241295796 crossref_primary_10_1002_cjce_25004 crossref_primary_10_1088_1361_6501_adfcfd crossref_primary_10_3390_pr11051486 crossref_primary_10_1016_j_ins_2021_12_106 crossref_primary_10_1109_TII_2020_2990975 crossref_primary_10_1080_00207721_2022_2093420 crossref_primary_10_1016_j_conengprac_2023_105531 crossref_primary_10_1016_j_compchemeng_2021_107587 crossref_primary_10_1016_j_chemolab_2022_104711 crossref_primary_10_1016_j_jprocont_2023_01_007 crossref_primary_10_3390_pr12040682 crossref_primary_10_1016_j_jprocont_2021_02_002 crossref_primary_10_1016_j_jprocont_2022_02_004 crossref_primary_10_1016_j_arabjc_2022_104062 crossref_primary_10_1080_10589759_2025_2491731 crossref_primary_10_1002_cjce_24689 crossref_primary_10_1016_j_conengprac_2024_106140 crossref_primary_10_1016_j_engappai_2025_111218 crossref_primary_10_1088_1361_6501_ada6ee crossref_primary_10_1109_TII_2023_3242772 crossref_primary_10_1109_TIE_2024_3370955 crossref_primary_10_1016_j_automatica_2022_110468 crossref_primary_10_1016_j_engappai_2024_109409 crossref_primary_10_1016_j_conengprac_2020_104358 crossref_primary_10_1016_j_conengprac_2020_104633 crossref_primary_10_1002_cjce_25308 crossref_primary_10_1016_j_compchemeng_2024_108887 crossref_primary_10_1016_j_ifacol_2020_12_858 crossref_primary_10_1016_j_isatra_2021_01_002 crossref_primary_10_1109_TASE_2021_3129247 crossref_primary_10_1016_j_jprocont_2021_09_007 crossref_primary_10_1061_JLEED9_EYENG_4850 crossref_primary_10_1002_bit_29039 crossref_primary_10_1016_j_jprocont_2021_09_014 crossref_primary_10_3390_act13110440 crossref_primary_10_1016_j_ins_2021_01_036 crossref_primary_10_1016_j_ins_2020_08_025 crossref_primary_10_1016_j_asoc_2022_109889 crossref_primary_10_1016_j_jprocont_2023_102999 crossref_primary_10_1016_j_eswa_2024_123417 crossref_primary_10_1109_TNNLS_2023_3343937 crossref_primary_10_1016_j_cam_2021_113872 crossref_primary_10_1109_TII_2023_3240919 crossref_primary_10_1109_TCST_2023_3330443 crossref_primary_10_1016_j_isatra_2021_04_014 crossref_primary_10_3390_pr11020402 crossref_primary_10_3390_s24092730 crossref_primary_10_1016_j_ymssp_2024_111577 crossref_primary_10_1109_ACCESS_2020_3013957 crossref_primary_10_3390_electronics13112133 crossref_primary_10_3390_electronics13132662 crossref_primary_10_1002_cjce_25312 crossref_primary_10_1109_TII_2025_3528580 crossref_primary_10_3390_chemengineering8030045 crossref_primary_10_1002_cjce_25691 crossref_primary_10_1016_j_psep_2021_07_002 crossref_primary_10_1016_j_ifacol_2023_10_1415 crossref_primary_10_1109_JPHOT_2022_3167760 crossref_primary_10_1016_j_chemolab_2022_104528 crossref_primary_10_1109_TII_2022_3233664 crossref_primary_10_1016_j_compchemeng_2022_107694 crossref_primary_10_3390_machines9080166 crossref_primary_10_7717_peerj_cs_1501 crossref_primary_10_1016_j_psep_2025_106834 crossref_primary_10_1016_j_cjche_2022_12_013 crossref_primary_10_1016_j_compchemeng_2022_107853 crossref_primary_10_1109_TASE_2020_3013654 crossref_primary_10_1109_TIM_2022_3147887 crossref_primary_10_1016_j_jprocont_2024_103296 crossref_primary_10_1016_j_jprocont_2022_07_012 crossref_primary_10_1109_TIM_2025_3548769 crossref_primary_10_1007_s11771_023_5325_5 crossref_primary_10_1016_j_jprocont_2024_103176 crossref_primary_10_1016_j_jprocont_2021_10_003 crossref_primary_10_1016_j_conengprac_2022_105174 crossref_primary_10_1016_j_conengprac_2025_106468 crossref_primary_10_1016_j_engappai_2023_107237 crossref_primary_10_1016_j_jprocont_2021_10_008 crossref_primary_10_1109_ACCESS_2021_3132046 crossref_primary_10_1109_TNNLS_2023_3262277 crossref_primary_10_1109_TII_2019_2951622 crossref_primary_10_1109_TIM_2021_3127284 crossref_primary_10_3390_pr10020335 crossref_primary_10_1016_j_jprocont_2024_103261 crossref_primary_10_1016_j_knosys_2024_112182 crossref_primary_10_1109_TASE_2021_3132037 crossref_primary_10_1016_j_psep_2024_05_129 crossref_primary_10_1109_TASE_2020_3010536 crossref_primary_10_1002_cjce_24886 crossref_primary_10_1016_j_eswa_2024_125052 crossref_primary_10_1016_j_jfranklin_2023_03_041 crossref_primary_10_1016_j_jprocont_2021_03_001 crossref_primary_10_1016_j_psep_2022_06_003 crossref_primary_10_1016_j_jprocont_2024_103262 crossref_primary_10_1016_j_engappai_2025_110689 crossref_primary_10_1109_TCYB_2019_2948202 crossref_primary_10_1109_TNNLS_2022_3224804 crossref_primary_10_1016_j_arcontrol_2022_09_005 crossref_primary_10_1109_TCST_2020_2974147 crossref_primary_10_3390_machines12060383 crossref_primary_10_3390_s24103029 crossref_primary_10_1016_j_seta_2022_102013 crossref_primary_10_1007_s11771_022_5206_3 crossref_primary_10_1016_j_asoc_2024_111896 crossref_primary_10_1016_j_jprocont_2021_01_005 crossref_primary_10_1109_TCYB_2021_3050398 crossref_primary_10_1109_TSMC_2020_3005433 crossref_primary_10_1007_s11432_024_4273_8 crossref_primary_10_1109_TNNLS_2024_3360030
Cites_doi	10.1016/j.chemolab.2016.08.007 10.1016/S0959-1524(00)00022-6 10.1109/TIE.2017.2782232 10.1111/j.2517-6161.1996.tb02080.x 10.1109/TIE.2018.2856198 10.1016/j.jprocont.2003.09.004 10.1111/j.1467-9868.2005.00503.x 10.1002/aic.16048 10.1002/aic.690370209 10.1109/TII.2018.2878405 10.1109/TII.2017.2761852 10.1109/TCST.2015.2472999 10.1016/0967-0661(95)00014-L 10.1214/aoms/1177704472 10.1109/TIE.2015.2466557 10.1126/science.1127647 10.1109/TCST.2017.2789188 10.1016/j.ces.2003.09.012 10.1109/TCST.2013.2264723 10.1109/TCST.2010.2083664 10.1021/acs.iecr.6b03221 10.1016/j.jprocont.2014.04.002 10.1016/j.ces.2012.07.018 10.1109/TIE.2017.2745452 10.1016/j.chemolab.2015.05.019 10.1016/S0959-1524(00)00004-4 10.1007/BFb0020217 10.1109/TCST.2015.2464331 10.1145/1390156.1390294 10.1021/ie102048f 10.1016/j.jlp.2012.10.003 10.1109/TASE.2012.2230628 10.1214/009053604000000067 10.1109/TCST.2016.2576018 10.1016/j.ces.2010.05.010 10.1214/10-AOS799 10.1109/TIE.2018.2864703 10.1016/S0893-6080(00)00026-5 10.1109/72.761722
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 RIA RIE AAYXX CITATION 7SP 7TB 8FD FR3 L7M
DOI	10.1109/TCST.2019.2897946
DatabaseName	IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Electronics & Communications Abstracts Mechanical & Transportation Engineering Abstracts Technology Research Database Engineering Research Database Advanced Technologies Database with Aerospace
DatabaseTitle	CrossRef Engineering Research Database Technology Research Database Mechanical & Transportation Engineering Abstracts Advanced Technologies Database with Aerospace Electronics & Communications Abstracts
DatabaseTitleList	Engineering Research Database
Database_xml	– sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	1558-0865
EndPage	1091
ExternalDocumentID	10_1109_TCST_2019_2897946 8654023
Genre	orig-research
GrantInformation_xml	– fundername: National Natural Science Foundation of China grantid: U1709211 funderid: 10.13039/501100001809 – fundername: Zhejiang Key Research and Development Project grantid: 2019C03100; 2019C01048
GroupedDBID	-~X .DC 0R~ 29I 4.4 5GY 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACBEA ACGFO ACGFS ACIWK ACKIV AENEX AETIX AGQYO AGSQL AHBIQ AI. AIBXA AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD HZ~ H~9 ICLAB IFIPE IFJZH IPLJI JAVBF LAI M43 O9- OCL P2P RIA RIE RNS TN5 VH1 AAYXX CITATION 7SP 7TB 8FD FR3 L7M
ID	FETCH-LOGICAL-c293t-de46ad95acecc937de0b1bcaa7b7ff1b3db83aa8a18b259fb6a755bcde0e4a2a3
IEDL.DBID	RIE
ISICitedReferencesCount	177
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000528642200002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1063-6536
IngestDate	Sun Nov 30 04:53:25 EST 2025 Sat Nov 29 03:51:39 EST 2025 Tue Nov 18 21:31:34 EST 2025 Wed Aug 27 02:35:25 EDT 2025
IsPeerReviewed	true
IsScholarly	true
Issue	3
Language	English
License	https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c293t-de46ad95acecc937de0b1bcaa7b7ff1b3db83aa8a18b259fb6a755bcde0e4a2a3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0002-3927-5656 0000-0002-0254-5763
PQID	2391261013
PQPubID	85425
PageCount	9
ParticipantIDs	crossref_citationtrail_10_1109_TCST_2019_2897946 proquest_journals_2391261013 crossref_primary_10_1109_TCST_2019_2897946 ieee_primary_8654023
PublicationCentury	2000
PublicationDate	2020-May 2020-5-00 20200501
PublicationDateYYYYMMDD	2020-05-01
PublicationDate_xml	– month: 05 year: 2020 text: 2020-May
PublicationDecade	2020
PublicationPlace	New York
PublicationPlace_xml	– name: New York
PublicationTitle	IEEE transactions on control systems technology
PublicationTitleAbbrev	TCST
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	ref35 ref13 ref34 ref12 anderson (ref38) 2003 ref37 ref15 ref36 ref31 ref33 chun-chin (ref14) 2011; 19 ref10 ref2 ref1 ref39 ref17 ref16 ref19 ref18 chun-chin (ref5) 2011; 19 ref24 ref23 ref26 ref25 liu (ref11) 2013; 10 ref20 ref42 ref41 ref22 ref21 ref28 ref27 ref29 ref8 ref7 ref9 ref4 ref3 ref6 ref40 efron (ref32) 2004; 32 vincent (ref30) 2010; 11
References_xml	– ident: ref27 doi: 10.1016/j.chemolab.2016.08.007 – year: 2003 ident: ref38 publication-title: An Introduction to Multivariate Statistical Analysis – ident: ref20 doi: 10.1016/S0959-1524(00)00022-6 – ident: ref34 doi: 10.1109/TIE.2017.2782232 – ident: ref31 doi: 10.1111/j.2517-6161.1996.tb02080.x – ident: ref1 doi: 10.1109/TIE.2018.2856198 – ident: ref23 doi: 10.1016/j.jprocont.2003.09.004 – ident: ref33 doi: 10.1111/j.1467-9868.2005.00503.x – ident: ref17 doi: 10.1002/aic.16048 – ident: ref39 doi: 10.1002/aic.690370209 – ident: ref8 doi: 10.1109/TII.2018.2878405 – ident: ref10 doi: 10.1109/TII.2017.2761852 – ident: ref4 doi: 10.1109/TCST.2015.2472999 – ident: ref41 doi: 10.1016/0967-0661(95)00014-L – ident: ref35 doi: 10.1214/aoms/1177704472 – ident: ref16 doi: 10.1109/TIE.2015.2466557 – ident: ref28 doi: 10.1126/science.1127647 – ident: ref2 doi: 10.1109/TCST.2017.2789188 – ident: ref26 doi: 10.1016/j.ces.2003.09.012 – ident: ref13 doi: 10.1109/TCST.2013.2264723 – volume: 19 start-page: 1245 year: 2011 ident: ref5 article-title: An adaptive forecast-based chart for non-Gaussian processes monitoring: With application to equipment malfunctions detection in a thermal power plant publication-title: IEEE Trans Control Syst Technol doi: 10.1109/TCST.2010.2083664 – ident: ref42 doi: 10.1021/acs.iecr.6b03221 – ident: ref19 doi: 10.1016/j.jprocont.2014.04.002 – ident: ref12 doi: 10.1016/j.ces.2012.07.018 – ident: ref18 doi: 10.1109/TIE.2017.2745452 – ident: ref15 doi: 10.1016/j.chemolab.2015.05.019 – ident: ref40 doi: 10.1016/S0959-1524(00)00004-4 – ident: ref25 doi: 10.1007/BFb0020217 – volume: 19 start-page: 1245 year: 2011 ident: ref14 article-title: An adaptive forecast-based chart for non-Gaussian processes monitoring: With application to equipment malfunctions detection in a thermal power plant publication-title: IEEE Trans Control Syst Technol doi: 10.1109/TCST.2010.2083664 – ident: ref6 doi: 10.1109/TCST.2015.2464331 – ident: ref29 doi: 10.1145/1390156.1390294 – ident: ref7 doi: 10.1021/ie102048f – ident: ref37 doi: 10.1016/j.jlp.2012.10.003 – volume: 10 start-page: 687 year: 2013 ident: ref11 article-title: Decentralized fault diagnosis of continuous annealing processes based on multilevel PCA publication-title: IEEE Trans Autom Sci Eng doi: 10.1109/TASE.2012.2230628 – volume: 32 start-page: 407 year: 2004 ident: ref32 article-title: Least angle regression publication-title: Ann Statist doi: 10.1214/009053604000000067 – volume: 11 start-page: 3371 year: 2010 ident: ref30 article-title: Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion publication-title: J Mach Learn Res – ident: ref3 doi: 10.1109/TCST.2016.2576018 – ident: ref24 doi: 10.1016/j.ces.2010.05.010 – ident: ref36 doi: 10.1214/10-AOS799 – ident: ref9 doi: 10.1109/TIE.2018.2864703 – ident: ref22 doi: 10.1016/S0893-6080(00)00026-5 – ident: ref21 doi: 10.1109/72.761722
SSID	ssj0014527
Score	2.6387026
Snippet	Robust process monitoring and reliable fault isolation in industrial processes usually encounter different challenges, including process nonlinearity and noise...
SourceID	proquest crossref ieee
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	1083
SubjectTerms	Coders Data mining Denoising autoencoder (DAE) Discriminant analysis elastic net (EN) fault isolation Feature extraction Kernel kernel density estimation (KDE) Monitoring Neurons Noise reduction Nonlinearity Principal component analysis process monitoring Robustness Statistical methods
Title	Robust Monitoring and Fault Isolation of Nonlinear Industrial Processes Using Denoising Autoencoder and Elastic Net
URI	https://ieeexplore.ieee.org/document/8654023 https://www.proquest.com/docview/2391261013
Volume	28
WOSCitedRecordID	wos000528642200002&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: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 1558-0865 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014527 issn: 1063-6536 databaseCode: RIE dateStart: 19930101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3dS8MwED9UfNAHv6Y4nZIHn8RubdM17aNMh74M0Ql7K_m4wGC0srb-_SZpNxVF8C2USwj9Jb273t3vAK4CrVErip4f-8qLkKVewnXkxZhohZwKpaRrNsEmk2Q2S5824GZdC4OILvkM-3boYvmqkLX9VTZIYmNfhHQTNhljTa3WOmIQNe1ZjYdDvdiFJLstn-ZgOnqZ2iSutG-8C0uo_k0HuaYqP77ETr2M9_-3sQPYa81IctvgfggbmB_B7hdywQ6Uz4Woy4o0t9Y-IzxXZMzrRUUezZFzmJBCk0lDl8GX5LORB2krCLAkLquA3GFezN3otq4KS3-pcOlWvDcGuNkGmWB1DK_j--nowWtbLHjS6PnKUxjFXKVDLg2UxlJR6ItASM6ZYFoHgiqRUM4THiTCOEpaxJwNh0IaOYx4yOkJbOVFjqdAKMOQSVQxxjqKKDProJJpONS-lFokXfBXLz2TLf-4bYOxyJwf4qeZxSmzOGUtTl24Xk95a8g3_hLuWGDWgi0mXeitkM3a61lmIU0D4zoa8_fs91nnsBNax9plNvZgq1rWeAHb8r2al8tLd_I-AI-821w
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3da9swED-6dLDtoe3WlmX90kOfypzalvz1GNKGhKamdBn0zejjBIVil9je3z9JdrKNlkHfhDkJ4Z_ku_Pd_Q7gPNAataLo-bGvPIZJ5qVcMy_GVCvkVCglXbOJJM_Th4fsbgu-b2phENEln-HIDl0sX1Wytb_KLtPY2BchfQfbEWNh0FVrbWIGrGvQanwc6sUuKDnsGTUvl5MfS5vGlY2Mf2Ep1f_RQq6tyotvsVMw0923bW0PdnpDkow75D_DFpZf4NNf9IL7UN9Xoq0b0t1b-4zwUpEpb58aMjeHzqFCKk3yjjCDr8ifVh6kryHAmri8AnKFZfXoRuO2qSwBpsKVW_HamOBmGyTH5gB-Tq-Xk5nXN1nwpNH0jaeQxVxlEZcGTGOrKPRFICTniUi0DgRVIqWcpzxIhXGVtIh5EkVCGjlkPOT0EAZlVeJXIDTBMJGoYow1YzQx66CSWRhpX0ot0iH465deyJ6B3DbCeCqcJ-JnhcWpsDgVPU5DuNhMee7oN_4nvG-B2Qj2mAzheI1s0V_QughpFhjn0RjA316fdQYfZsvbRbGY5zdH8DG0brbLczyGQbNq8QTey1_NY706dafwNxwm3qM
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=Robust+Monitoring+and+Fault+Isolation+of+Nonlinear+Industrial+Processes+Using+Denoising+Autoencoder+and+Elastic+Net&rft.jtitle=IEEE+transactions+on+control+systems+technology&rft.au=Yu%2C+Wanke&rft.au=Zhao%2C+Chunhui&rft.date=2020-05-01&rft.issn=1063-6536&rft.eissn=1558-0865&rft.volume=28&rft.issue=3&rft.spage=1083&rft.epage=1091&rft_id=info:doi/10.1109%2FTCST.2019.2897946&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_TCST_2019_2897946
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1063-6536&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1063-6536&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1063-6536&client=summon