Nonlinear Dynamic Process Monitoring Based on Discriminative Denoising Autoencoder and Canonical Variate Analysis
Modern industrial processes are characterized by increasing complexity, often exhibiting pronounced dynamic behaviors and significant nonlinearity. Addressing these dynamic and nonlinear characteristics is essential for effective process monitoring. However, many existing methods for process monitor...
Uložené v:
| Vydané v: | Actuators Ročník 13; číslo 11; s. 440 |
|---|---|
| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Basel
MDPI AG
01.11.2024
|
| Predmet: | |
| ISSN: | 2076-0825, 2076-0825 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Modern industrial processes are characterized by increasing complexity, often exhibiting pronounced dynamic behaviors and significant nonlinearity. Addressing these dynamic and nonlinear characteristics is essential for effective process monitoring. However, many existing methods for process monitoring and fault diagnosis are insufficient in handling these challenges. In this article, we present a novel process monitoring approach, CVA-DisDAE, which integrates an improved Denoising Autoencoder (DAE) with Canonical Variate Analysis (CVA) to address the challenges posed by dynamic behaviors and nonlinear relationships in industrial processes. First, CVA is employed to reduce data dimensionality and minimize information redundancy by maximizing correlations between past and future observations, thereby effectively capturing process dynamics. Following this, we introduce a discriminative DAE model (DisDAE) designed to serve as a semi-supervised denoising autoencoder for precise feature extraction. This is achieved by incorporating both between-class separability and within-class variability into the traditional DAE framework. The key distinction between the proposed DisDAE and the conventional DAE lies in the integration of a linear discriminant analysis (LDA) penalty into the DAE’s loss function, resulting in extracted features that are more conducive to fault classification. Finally, we validate the effectiveness of the proposed semi-supervised dynamic process monitoring approach through its application to the Tennessee Eastman benchmark process, demonstrating its superior performance. |
|---|---|
| AbstractList | Modern industrial processes are characterized by increasing complexity, often exhibiting pronounced dynamic behaviors and significant nonlinearity. Addressing these dynamic and nonlinear characteristics is essential for effective process monitoring. However, many existing methods for process monitoring and fault diagnosis are insufficient in handling these challenges. In this article, we present a novel process monitoring approach, CVA-DisDAE, which integrates an improved Denoising Autoencoder (DAE) with Canonical Variate Analysis (CVA) to address the challenges posed by dynamic behaviors and nonlinear relationships in industrial processes. First, CVA is employed to reduce data dimensionality and minimize information redundancy by maximizing correlations between past and future observations, thereby effectively capturing process dynamics. Following this, we introduce a discriminative DAE model (DisDAE) designed to serve as a semi-supervised denoising autoencoder for precise feature extraction. This is achieved by incorporating both between-class separability and within-class variability into the traditional DAE framework. The key distinction between the proposed DisDAE and the conventional DAE lies in the integration of a linear discriminant analysis (LDA) penalty into the DAE’s loss function, resulting in extracted features that are more conducive to fault classification. Finally, we validate the effectiveness of the proposed semi-supervised dynamic process monitoring approach through its application to the Tennessee Eastman benchmark process, demonstrating its superior performance. |
| Audience | Academic |
| Author | Liu, Daoguang Fan, Jiayu Zhan, Yinxiao Liang, Jun |
| Author_xml | – sequence: 1 givenname: Jun surname: Liang fullname: Liang, Jun – sequence: 2 givenname: Daoguang surname: Liu fullname: Liu, Daoguang – sequence: 3 givenname: Yinxiao orcidid: 0000-0002-4001-5384 surname: Zhan fullname: Zhan, Yinxiao – sequence: 4 givenname: Jiayu surname: Fan fullname: Fan, Jiayu |
| BookMark | eNptUcFuGyEQRVUqNU1z6g8g9Vg5gYXdhaNrN02ktMmh7RXNwmBhbSABXMl_H1xXVVSVOYBG7715zHtLTmKKSMh7zi6E0OwSbOWCcyYle0VOOzYOC6a6_uTF-w05L2XL2tFcKCZOydO3FOcQETJd7yM8BEvvc7JYCv2aYqgph7ihn6CgoynSdSg2h4cQoYZfSNcYUygHxHJXE0abHGYK0dEVNHfBwkx_Qg5QkS4jzPsSyjvy2sNc8PzPfUZ-XH3-vrpe3N59uVktbxdWMlEXfT-NUlrWo_Pa83EEDU57NXg5CSedllJLB-M09V6xwemBS40O5TT4SXkUZ-TmqOsSbM1jcw15bxIE87uR8sZArsHOaHiHXDlpLRdCIptAjF0nrULNNZu4bVofjlqPOT3tsFSzTbvcPlSM4KJTfNB6bKiLI2oDTTREn2oG28ph22sLy4fWXyo-6k4pcSB8PBJsTqVk9H9tcmYOmZoXmTY0_wdtQ205pNjGhPm_nGezXabT |
| CitedBy_id | crossref_primary_10_1080_00207543_2025_2513577 |
| Cites_doi | 10.1016/j.ces.2024.120196 10.1016/0169-7439(95)00076-3 10.1109/TAI.2021.3134186 10.1016/j.jprocont.2014.12.001 10.1016/j.automatica.2021.110148 10.1016/j.conengprac.2020.104500 10.1145/1390156.1390294 10.1109/TIFS.2015.2446438 10.1016/0098-1354(93)80018-I 10.1126/science.1127647 10.1021/acsomega.0c06039 10.1016/j.chemolab.2019.103814 10.3233/ICA-230728 10.1155/2016/6795352 10.1145/1273496.1273613 10.1021/acs.iecr.7b01721 10.1021/ie302069q 10.1002/cjce.23002 10.1016/j.knosys.2024.112093 10.1109/CISP.2015.7407967 10.1016/j.conengprac.2022.105156 10.1016/j.heliyon.2024.e27732 10.1016/j.asoc.2020.106525 10.1016/j.jprocont.2021.09.009 10.1016/0098-1354(94)00057-U 10.1109/34.908974 10.1109/ICEEPS62542.2024.10693186 10.1016/j.jprocont.2018.02.004 10.1016/j.chemolab.2020.104063 10.1016/j.ins.2019.03.068 10.1109/TIM.2020.3004681 10.1016/j.jfranklin.2021.07.041 10.1016/j.measurement.2023.113411 10.1016/j.jprocont.2017.03.004 10.1016/j.ifacol.2015.09.034 10.1016/j.psep.2019.05.018 10.1109/TCST.2019.2897946 10.1016/j.ces.2023.118581 10.1109/TNNLS.2021.3072491 10.1016/S0169-7439(00)00058-7 10.1109/TIE.2020.2972472 10.1002/aic.12392 10.1021/acsomega.2c01892 10.1007/s13369-021-05388-y 10.1016/j.neucom.2017.01.079 |
| ContentType | Journal Article |
| Copyright | COPYRIGHT 2024 MDPI AG 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: COPYRIGHT 2024 MDPI AG – notice: 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | AAYXX CITATION 3V. 7SP 7TB 7XB 8AL 8FD 8FE 8FG 8FK ABJCF ABUWG AFKRA ARAPS AZQEC BENPR BGLVJ CCPQU DWQXO FR3 GNUQQ HCIFZ JQ2 K7- L6V L7M M0N M7S P5Z P62 PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS PTHSS Q9U DOA |
| DOI | 10.3390/act13110440 |
| DatabaseName | CrossRef ProQuest Central (Corporate) Electronics & Communications Abstracts Mechanical & Transportation Engineering Abstracts ProQuest Central (purchase pre-March 2016) Computing Database (Alumni Edition) Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) (purchase pre-March 2016) Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials - QC ProQuest Central ProQuest Technology Collection ProQuest One ProQuest Central Korea Engineering Research Database ProQuest Central Student SciTech Premium Collection ProQuest Computer Science Collection Computer Science Database ProQuest Engineering Collection Advanced Technologies Database with Aerospace Computing Database Engineering Database ProQuest advanced technologies & aerospace journals ProQuest Advanced Technologies & Aerospace Collection Proquest Central Premium ProQuest One Academic (New) ProQuest Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China Engineering collection ProQuest Central Basic DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef Publicly Available Content Database Computer Science Database ProQuest Central Student Technology Collection Technology Research Database ProQuest One Academic Middle East (New) Mechanical & Transportation Engineering Abstracts ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest Engineering Collection ProQuest Central Korea ProQuest Central (New) Advanced Technologies Database with Aerospace Engineering Collection Advanced Technologies & Aerospace Collection ProQuest Computing Engineering Database ProQuest Central Basic ProQuest Computing (Alumni Edition) ProQuest One Academic Eastern Edition Electronics & Communications Abstracts ProQuest Technology Collection ProQuest SciTech Collection Advanced Technologies & Aerospace Database ProQuest One Academic UKI Edition Materials Science & Engineering Collection Engineering Research Database ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) |
| DatabaseTitleList | CrossRef Publicly Available Content 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: PIMPY name: ProQuest Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 2076-0825 |
| ExternalDocumentID | oai_doaj_org_article_12e18d4cc1334e0ba37224c8e9190b1c A817928837 10_3390_act13110440 |
| GeographicLocations | Tennessee United States--US |
| GeographicLocations_xml | – name: Tennessee – name: United States--US |
| GroupedDBID | 5VS 8FE 8FG AADQD AAFWJ AAYXX ABJCF ABUWG ACIWK ADBBV ADMLS AFFHD AFKRA AFPKN AFZYC ALMA_UNASSIGNED_HOLDINGS ARAPS AZQEC BCNDV BENPR BGLVJ BPHCQ CCPQU CITATION DWQXO GNUQQ GROUPED_DOAJ HCIFZ IAO ITC K6V K7- KQ8 L6V M7S MODMG M~E OK1 P62 PHGZM PHGZT PIMPY PQGLB PQQKQ PROAC PTHSS 3V. 7SP 7TB 7XB 8AL 8FD 8FK FR3 JQ2 L7M M0N PKEHL PQEST PQUKI PRINS Q9U |
| ID | FETCH-LOGICAL-c403t-55b744c05edf9f177a9ad9f86f4b3d4d94494da7bb5f806d96149ede4b6fb8fe3 |
| IEDL.DBID | M7S |
| ISICitedReferencesCount | 1 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001363486500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2076-0825 |
| IngestDate | Fri Oct 03 12:52:29 EDT 2025 Fri Jul 25 11:54:17 EDT 2025 Tue Nov 04 18:27:22 EST 2025 Sat Nov 29 07:11:02 EST 2025 Tue Nov 18 20:54:07 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 11 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c403t-55b744c05edf9f177a9ad9f86f4b3d4d94494da7bb5f806d96149ede4b6fb8fe3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0002-4001-5384 |
| OpenAccessLink | https://www.proquest.com/docview/3132816997?pq-origsite=%requestingapplication% |
| PQID | 3132816997 |
| PQPubID | 2032444 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_12e18d4cc1334e0ba37224c8e9190b1c proquest_journals_3132816997 gale_infotracacademiconefile_A817928837 crossref_primary_10_3390_act13110440 crossref_citationtrail_10_3390_act13110440 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-11-01 |
| PublicationDateYYYYMMDD | 2024-11-01 |
| PublicationDate_xml | – month: 11 year: 2024 text: 2024-11-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Basel |
| PublicationPlace_xml | – name: Basel |
| PublicationTitle | Actuators |
| PublicationYear | 2024 |
| Publisher | MDPI AG |
| Publisher_xml | – name: MDPI AG |
| References | Hu (ref_23) 2022; 123 Yin (ref_13) 2015; 4 He (ref_9) 2018; 2 Tang (ref_40) 2021; 106 Yu (ref_24) 2020; 28 Zhang (ref_43) 2021; 46 Yu (ref_26) 2020; 95 Samuel (ref_39) 2015; 48 Sheriffa (ref_8) 2017; 54 Alonso (ref_19) 2024; 31 Yu (ref_31) 2011; 57 Ji (ref_14) 2024; 295 Lou (ref_7) 2017; 46 Ge (ref_3) 2013; 52 Luo (ref_18) 2019; 192 Wang (ref_38) 2022; 7 Chen (ref_48) 2021; 33 Si (ref_1) 2021; 68 Fazai (ref_11) 2019; 128 Gao (ref_28) 2015; 10 Wu (ref_33) 2024; 5 Ma (ref_2) 2020; 69 Zhang (ref_20) 2023; 4 Shang (ref_16) 2023; 220 Downs (ref_45) 1993; 17 Zhu (ref_46) 2017; 51 Zhang (ref_22) 2018; 64 Peng (ref_10) 2020; 101 Martinez (ref_30) 2001; 23 Zhang (ref_25) 2020; 203 Wang (ref_17) 2021; 6 Benson (ref_36) 2024; 299 Hinton (ref_47) 2006; 313 ref_37 Li (ref_6) 2021; 15 Evan (ref_34) 2000; 51 Snoek (ref_21) 2013; 13 Lu (ref_32) 2023; 61 Zhang (ref_15) 2023; 271 Fu (ref_27) 2017; 241 Harkat (ref_12) 2019; 490 Lyman (ref_44) 1995; 19 Jiang (ref_35) 2015; 26 ref_42 ref_41 Ku (ref_5) 1995; 30 Huang (ref_29) 2016; 2016 ref_49 Lou (ref_4) 2022; 138 |
| References_xml | – volume: 295 start-page: 120196 year: 2024 ident: ref_14 article-title: Fault detection and isolation for dynamic non-stationary processes with stationary subspace-based canonical variate analysis publication-title: Chem. Eng. Sci. doi: 10.1016/j.ces.2024.120196 – volume: 30 start-page: 179 year: 1995 ident: ref_5 article-title: Disturbance detection and isolation by dynamic principal component analysis publication-title: Chemom. Intell. Lab. Syst. doi: 10.1016/0169-7439(95)00076-3 – volume: 4 start-page: 592 year: 2023 ident: ref_20 article-title: Feature-Aligned Stacked Autoencoder: A Novel Semisupervised Deep Learning Model for Pattern Classification of Industrial Faults publication-title: IEEE Trans. Artif. Intell. doi: 10.1109/TAI.2021.3134186 – volume: 26 start-page: 17 year: 2015 ident: ref_35 article-title: Canonical variate analysis-based contributions for fault identification publication-title: J. Process. Control doi: 10.1016/j.jprocont.2014.12.001 – volume: 138 start-page: 110148 year: 2022 ident: ref_4 article-title: A novel multivariate statistical process monitoring algorithm: Orthonormal subspace analysis publication-title: Automatica doi: 10.1016/j.automatica.2021.110148 – volume: 61 start-page: 1 year: 2023 ident: ref_32 article-title: Unsupervised Linear Discriminant Analysis for Feature Extraction and Classification of Hyperspectral Images publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 101 start-page: 104500 year: 2020 ident: ref_10 article-title: Distributed process monitoring based on canonical correlation analysis with partly-connected topology publication-title: Control Eng. Pract. doi: 10.1016/j.conengprac.2020.104500 – ident: ref_41 doi: 10.1145/1390156.1390294 – volume: 10 start-page: 2108 year: 2015 ident: ref_28 article-title: Single sample face recognition via learning deep supervised autoencoders publication-title: IEEE Trans. Inf. Forensics Secur. doi: 10.1109/TIFS.2015.2446438 – volume: 17 start-page: 245 year: 1993 ident: ref_45 article-title: A plant-wide industrial process control problem publication-title: Comput. Chem. Eng. doi: 10.1016/0098-1354(93)80018-I – volume: 313 start-page: 504 year: 2006 ident: ref_47 article-title: Reducing the dimensionality of data with neural networks publication-title: Science doi: 10.1126/science.1127647 – volume: 6 start-page: 9989 year: 2021 ident: ref_17 article-title: Efficient Iterative Dynamic Kernel Principal Component Analysis Monitoring Method for the Batch Process with Super-large-scale Data Sets publication-title: ACS Omega doi: 10.1021/acsomega.0c06039 – volume: 192 start-page: 103814 year: 2019 ident: ref_18 article-title: Discriminant autoencoder for feature extraction in fault diagnosis publication-title: Chemom. Intell. Lab. Syst. doi: 10.1016/j.chemolab.2019.103814 – volume: 51 start-page: 697 year: 2017 ident: ref_46 article-title: Fault classification based on modified active learning and weighted SVM publication-title: J. Zhejiang Univ. (Eng. Sci.) – volume: 31 start-page: 157 year: 2024 ident: ref_19 article-title: Gap imputation in related multivariate time series through recurrent neural network-based denoising autoencode publication-title: Integr. Comput.-Aid Eng. doi: 10.3233/ICA-230728 – volume: 2016 start-page: 6795352 year: 2016 ident: ref_29 article-title: Adaptive Deep Supervised Autoencoder Based Image Reconstruction for Face Recognition publication-title: Math. Probl. Eng. doi: 10.1155/2016/6795352 – ident: ref_37 doi: 10.1145/1273496.1273613 – volume: 46 start-page: 13800 year: 2017 ident: ref_7 article-title: Multimode continuous processes monitoring based on hidden semi-markov model and principal component analysis publication-title: Ind. Eng. Chem. Res. doi: 10.1021/acs.iecr.7b01721 – volume: 52 start-page: 3534 year: 2013 ident: ref_3 article-title: Review of Recent Research on Data-Based Process Monitoring publication-title: Ind. Eng. Chem. Res. doi: 10.1021/ie302069q – volume: 2 start-page: 444 year: 2018 ident: ref_9 article-title: Modified partial least square for diagnosing key-performance-indicator-related faults publication-title: Can. J. Chem. Eng. doi: 10.1002/cjce.23002 – volume: 4 start-page: 1480 year: 2015 ident: ref_13 article-title: Data-driven process monitoring based on modified orthogonal projections to latent structures publication-title: IEEE Trans. Control Syst. Technol. – volume: 299 start-page: 112093 year: 2024 ident: ref_36 article-title: Linear discriminant analysis with trimmed and difference distribution modeling publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2024.112093 – ident: ref_42 doi: 10.1109/CISP.2015.7407967 – volume: 123 start-page: 105156 year: 2022 ident: ref_23 article-title: Low-rank reconstruction-based autoencoder for robust fault detection publication-title: Control Eng. Pract. doi: 10.1016/j.conengprac.2022.105156 – volume: 5 start-page: 27732 year: 2024 ident: ref_33 article-title: Near-infrared spectroscopy combined with fuzzy fast pseudoinverse linear discriminant analysis to discriminate mee tea grades publication-title: Heliyon doi: 10.1016/j.heliyon.2024.e27732 – volume: 95 start-page: 106525 year: 2020 ident: ref_26 article-title: Multiscale intelligent fault detection system based on agglomerative hierarchical clustering using stacked denoising autoencoder with temporal information publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2020.106525 – volume: 106 start-page: 221 year: 2021 ident: ref_40 article-title: Dynamic process monitoring based on canonical global and local preserving projection analysis publication-title: J. Process. Control doi: 10.1016/j.jprocont.2021.09.009 – volume: 19 start-page: 321 year: 1995 ident: ref_44 article-title: Plant-wide control of the Tennessee Eastman problem publication-title: Comput. Chem. Eng. doi: 10.1016/0098-1354(94)00057-U – volume: 23 start-page: 228 year: 2001 ident: ref_30 article-title: PCA versus LDA publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/34.908974 – ident: ref_49 doi: 10.1109/ICEEPS62542.2024.10693186 – volume: 64 start-page: 49 year: 2018 ident: ref_22 article-title: Automated feature learning for nonlinear process monitoring – An approach using stacked denoising autoencoder and k-nearest neighbor rule publication-title: J. Process Control doi: 10.1016/j.jprocont.2018.02.004 – volume: 203 start-page: 104063 year: 2020 ident: ref_25 article-title: Noise reduction in the spectral domain of hyperspectral images using denoising autoencoder methods publication-title: Chemom. Intell. Lab. Syst. doi: 10.1016/j.chemolab.2020.104063 – volume: 490 start-page: 265 year: 2019 ident: ref_12 article-title: Fault detection of uncertain chemical processes using interval partial least squares-based generalized likelihood ratio test publication-title: Inf. Sci. doi: 10.1016/j.ins.2019.03.068 – volume: 13 start-page: 2567 year: 2013 ident: ref_21 article-title: Nonparametric Guidance of Autoencoder Representations using Label Information publication-title: J. Mach. Learn Res. – volume: 69 start-page: 9535 year: 2020 ident: ref_2 article-title: Multi- step dynamic slow feature analysis for industrial process monitoring publication-title: IEEE Trans. Instrum. Meas. doi: 10.1109/TIM.2020.3004681 – volume: 15 start-page: 7900 year: 2021 ident: ref_6 article-title: Multivariate statistical process monitoring based on principal discriminative component analysis publication-title: J. Frankl. Inst. doi: 10.1016/j.jfranklin.2021.07.041 – volume: 220 start-page: 113411 year: 2023 ident: ref_16 article-title: Recursive ensemble canonical variate analysis for online incipient fault detection in dynamic processes publication-title: Measurement doi: 10.1016/j.measurement.2023.113411 – volume: 54 start-page: 47 year: 2017 ident: ref_8 article-title: Fault detection using multiscale pca-based moving window GLRT publication-title: J. Process Control doi: 10.1016/j.jprocont.2017.03.004 – volume: 48 start-page: 605 year: 2015 ident: ref_39 article-title: Kernel Canonical Variate Analysis for Nonlinear Dynamic Process Monitoring publication-title: IFAC-PapersOnLine doi: 10.1016/j.ifacol.2015.09.034 – volume: 128 start-page: 228 year: 2019 ident: ref_11 article-title: Online reduced kernel pls combined with glrt for fault detection in chemical systems publication-title: Process Saf. Environ. Prot. doi: 10.1016/j.psep.2019.05.018 – volume: 28 start-page: 1083 year: 2020 ident: ref_24 article-title: Robust Monitoring and Fault Isolation of Nonlinear Industrial Processes Using Denoising Autoencoder and Elastic Net publication-title: IEEE Trans. Control Syst. Technol. doi: 10.1109/TCST.2019.2897946 – volume: 271 start-page: 118581 year: 2023 ident: ref_15 article-title: Common canonical variate analysis (CCVA) based modeling and monitoring for multimode processes publication-title: Chem. Eng. Sci. doi: 10.1016/j.ces.2023.118581 – volume: 33 start-page: 6158 year: 2021 ident: ref_48 article-title: A comparative study of deep neural network-aided canonical correlation analysis-based process monitoring and fault detection methods publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2021.3072491 – volume: 51 start-page: 81 year: 2000 ident: ref_34 article-title: Braatz, Fault detection in industrial processes using canonical variate analysis and dynamic principal component analysis publication-title: Chemom. Intell. Lab. Syst. doi: 10.1016/S0169-7439(00)00058-7 – volume: 68 start-page: 2626 year: 2021 ident: ref_1 article-title: Key-performance-indicator- related process monitoring based on improved kernel partial least squares publication-title: IEEE Trans. Ind. Electron. doi: 10.1109/TIE.2020.2972472 – volume: 57 start-page: 1817 year: 2011 ident: ref_31 article-title: Localized Fisher discriminant analysis based complex chemical process monitoring publication-title: AIChE J. doi: 10.1002/aic.12392 – volume: 7 start-page: 18904 year: 2022 ident: ref_38 article-title: Nonlinear Dynamic Process Monitoring Based on Ensemble Kernel Canonical Variate Analysis and Bayesian Inference publication-title: ACS Omega doi: 10.1021/acsomega.2c01892 – volume: 46 start-page: 10125 year: 2021 ident: ref_43 article-title: Incipient Fault Diagnosis of Batch Process Based on Deep Time Series Feature Extraction publication-title: Arab. J. Sci. Eng. doi: 10.1007/s13369-021-05388-y – volume: 241 start-page: 18 year: 2017 ident: ref_27 article-title: Combine HowNet lexicon to train phrase recursive autoencoder for sentence-level sentiment analysis publication-title: Neurocomputing doi: 10.1016/j.neucom.2017.01.079 |
| SSID | ssj0000913803 |
| Score | 2.2796266 |
| Snippet | Modern industrial processes are characterized by increasing complexity, often exhibiting pronounced dynamic behaviors and significant nonlinearity. Addressing... |
| SourceID | doaj proquest gale crossref |
| SourceType | Open Website Aggregation Database Enrichment Source Index Database |
| StartPage | 440 |
| SubjectTerms | canonical variate analysis (CVA) Classification Correlation analysis Data processing denoising autoencoder (DAE) Discriminant analysis dynamics Effectiveness Fault diagnosis Feature extraction Methods Monitoring Noise reduction Nonlinear dynamics nonlinear process Nonlinearity process monitoring Redundancy Variables |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrZ1LSxxBEICbIB7iQTRRXB-hD0JAGJzZrt7uPq5uJIew5JAEb00_YUFmdR3z-1M1PSsrKLl4HQqmp7q6HkPXV4yde8iYqMZcjQWWKCBUXTkpZSVgnCWQz_Q9Z_aHms_17a35uTHqi-6EFTxwUdxlM06NjhACFlOQau-EwqgTdDIYynwTyPvWymwUU70PNo3QtSgNeQLr-ksXOiLL0ITlFyGoJ_W_5Y_7IHOzx3aH7JBPy6r22YfUfmI7G8zAz-xhXuAWbsVnZZo8Hy7783I-SYxfYXCKfNny2YL8At13Ib_GZ6ldLuj3AJ8-dUuCWMa04q6N_Nq1y75Hkv_B8hkzUL7mlRyw3zfffl1_r4a5CVWAWnSVlF4BhFqmmE1ulHLGRZP1JIMXEaIBMBCd8l5mXU-iwRBtUkzgJ9nrnMQh28JXpiPGvYw6eOi5hBBk9DETcD5k30gdlRmxi7UqbRig4jTb4s5icUF6txt6H7HzZ-H7wtJ4XeyK9uRZhADY_QM0CzuYhf2fWYzYV9pRS8cUFxTc0G2An0XAKzvV6Ilo0rIasdP1ptvh_D5aAlrqZmKMOn6P1Zywj2NMhkoP4ynb6lZP6Yxth7_d4nH1pTfdf3jR83k priority: 102 providerName: Directory of Open Access Journals |
| Title | Nonlinear Dynamic Process Monitoring Based on Discriminative Denoising Autoencoder and Canonical Variate Analysis |
| URI | https://www.proquest.com/docview/3132816997 https://doaj.org/article/12e18d4cc1334e0ba37224c8e9190b1c |
| Volume | 13 |
| WOSCitedRecordID | wos001363486500001&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: 2076-0825 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913803 issn: 2076-0825 databaseCode: DOA dateStart: 20120101 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: 2076-0825 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913803 issn: 2076-0825 databaseCode: M~E dateStart: 20120101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Computer Science Database customDbUrl: eissn: 2076-0825 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913803 issn: 2076-0825 databaseCode: K7- dateStart: 20120101 isFulltext: true titleUrlDefault: http://search.proquest.com/compscijour providerName: ProQuest – providerCode: PRVPQU databaseName: Engineering Database customDbUrl: eissn: 2076-0825 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913803 issn: 2076-0825 databaseCode: M7S dateStart: 20120101 isFulltext: true titleUrlDefault: http://search.proquest.com providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest advanced technologies & aerospace journals customDbUrl: eissn: 2076-0825 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913803 issn: 2076-0825 databaseCode: P5Z dateStart: 20120101 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Proquest Central customDbUrl: eissn: 2076-0825 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913803 issn: 2076-0825 databaseCode: BENPR dateStart: 20120101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Publicly Available Content Database customDbUrl: eissn: 2076-0825 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913803 issn: 2076-0825 databaseCode: PIMPY dateStart: 20120101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwELag5QAH3oiFsvKhEhJS1DzstX1Cu91WIGAV8VLhEvmJVkJJm035_cw43qVIwIWLD_FIcTTjzzMTzzeEHBoWwFF1ISsrCFFYJfJMc86zipWBM8RME3lm34rVSp6dqTol3DbpWuUWEyNQu85ijvwIKQZlMVNKvDy_yLBrFP5dTS00rpN9ZEko4tW9D7scC3JeytgcuYRwPcNoaCzRqyDSP9J2QK4Z7Ln826EUufv_htDx2Dm9878LvktuJ4eTzkcLuUeu-fY-uXWFhvABuViNfBm6p8uxQT1N9QN03PIoRhdw3jnatXS5RqjBKzQIlXTp226NGQc6vxw65MV0vqe6dfRYt10su6SfISIHp5ZuKVAekk-nJx-PX2WpFUNmWV4NGedGMGZz7l1QoRBCK-1UkLPATOWYU4wp5rQwhgeZz5yCU19555mZBSODrx6RPXilf0yo4U5awyLVIbPcGReQw94GU3DphJqQF1tdNDbxlGO7jO8NxCuouOaK4ibkcCd8PtJz_FlsgUrdiSCndnzQ9d-atEWbovSFdMxaCNuZz42uBPg3VnoFTpMp7IQ8R5NocOfDgqxOBQzwWcih1cwlgBs2bxYTcrA1iSZBwqb5ZQ9P_j39lNwswXMaCx4PyN7QX_pn5Ib9Maw3_ZTsL05W9ftpTB7A-EZk02j1MNb8K8zXr9_VX34Ca74KDw |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VggQceCMWCvhQhIQUNYnttX1AaNularXLikNBvQU_0UqQtNkU1D_Fb8STx1Ik4NYD18TKyzPfzDie7wPYNizERNWFJKexRGFUpInmnCeU5YEzxEzT8szOxWIhj4_V-w34MfTC4LbKARNboHaVxTXyHaQYlNlYKfHm5DRB1Sj8uzpIaHRmMfPn32PJtnp9OI3z-yLP998e7R0kvapAYllKm4RzIxizKfcuqJAJoZV2KshxYIY65hRjijktjOFBpmOnYgBT3nlmxsHI4Gm87hW4yqgU6FczkazXdJBjU7ZizHkqYqEeq6-uJZBSle5o2yC3DWo8_xYEW62Av0WENszt3_7fPtAduNUn1GTSecBd2PDlPbh5gWbxPpwuOj4QXZPpeam_Li3p-yNIB2k4jOzGeO5IVZLpEqEUtwhhKCBTX1ZLXFEhk7OmQt5P52uiS0f2dFm1baXko45-3HgyULw8gA-X8s4PYTPe0j8CYriT1rCWypFZ7owLyNFvg8m4dEKN4NUw94XtedhRDuRLEesxNJTigqGMYHs9-KSjH_nzsF00ovUQ5AxvD1T156KHoCLLfSYdszajlPnUaCpi_malVzEpNJkdwUs0wQKRLT6Q1X2DRnwt5AgrJjKCN4pTixFsDSZY9JC3Kn7Z3-N_n34O1w-O3s2L-eFi9gRu5DFL7Jo7t2Czqc_8U7hmvzXLVf2s9S4Cny7bWn8CPudjFA |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwELZKQQgOPIu6UMCHIiSkaJPYXtsHhLYNK6pWqz0AqrgYP9FKNGmzKah_jV-HJ4-lSMCtB66JlcTJ529mnJlvENo1NERH1YUkJzFEoYSniWaMJYTmgVHgTNPqzB7x-VwcH8vFBvox1MJAWuXAiS1Ru8rCHvkYJAZFNpGSj0OfFrEoZm9OzxLoIAV_Wod2Gh1EDv3F9xi-rV4fFPFbv8jz2dv3---SvsNAYmlKmoQxwym1KfMuyJBxrqV2MohJoIY46iSlkjrNjWFBpBMnozGT3nlqJsGI4Em87jV0nccYE9IJF-zTen8H9DZF25g5T3kM2mMk1pUHEiLTsbYN6NxAv-ffDGLbN-Bv1qE1ebO7__PLuofu9I42nnYr4z7a8OUDdPuS_OJDdDbvdEJ0jYuLUp8sLe7rJnBHdTAM70U773BV4mIJFAupQ2AicOHLagk7LXh63lSgB-p8jXXp8L4uq7bcFH_UcX03Hg_SL1vow5XM-RHajLf02wgb5oQ1tJV4pJY54wJo99tgMiYclyP0asCBsr0-O7QJ-apinAagUZdAM0K768GnnSzJn4ftAaDWQ0BLvD1Q1V9UT00qy30mHLU2I4T61GjCo19nhZfRWTSZHaGXAEcFjBcfyOq-cCNOC7TD1FREUoem1XyEdgY4qp4KV-oXFh__-_RzdDOCVB0dzA-foFt5dB67ms8dtNnU5_4pumG_NctV_axdaBh9vmqw_gR9QWw4 |
| 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=Nonlinear+Dynamic+Process+Monitoring+Based+on+Discriminative+Denoising+Autoencoder+and+Canonical+Variate+Analysis&rft.jtitle=Actuators&rft.au=Liang%2C+Jun&rft.au=Liu%2C+Daoguang&rft.au=Zhan%2C+Yinxiao&rft.au=Fan%2C+Jiayu&rft.date=2024-11-01&rft.issn=2076-0825&rft.eissn=2076-0825&rft.volume=13&rft.issue=11&rft.spage=440&rft_id=info:doi/10.3390%2Fact13110440&rft.externalDBID=n%2Fa&rft.externalDocID=10_3390_act13110440 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2076-0825&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2076-0825&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2076-0825&client=summon |