A multi-scale low rank convolutional autoencoder for process monitoring of nonlinear uncertain systems
In modern industrial process monitoring, due to equipment performance degradation and equipment working environment, process variable measurement can lead to uncertainty in measurement data. Traditional process monitoring methods based on uncertain data typically assume that variables have the same...
Saved in:
| Published in: | Process safety and environmental protection Vol. 188; pp. 53 - 63 |
|---|---|
| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier Ltd
01.08.2024
|
| Subjects: | |
| ISSN: | 0957-5820, 1744-3598 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | In modern industrial process monitoring, due to equipment performance degradation and equipment working environment, process variable measurement can lead to uncertainty in measurement data. Traditional process monitoring methods based on uncertain data typically assume that variables have the same level of uncertainty. However, factors such as the lifespan of different devices and different working environments result in varying levels of uncertainty in variables. To monitor such processes, a multi scale low-rank convolutional autoencoder (MLRCAE) for process monitoring based on uncertain measurement data is proposed. First, to extract robust multi scale features from uncertain input, a multi scale convolution (MSC) module is designed to reduce the impact of different levels of uncertainty on the model. Second, a low-rank constraint (LRC) loss function is used to prevent models from overfitting uncertain data by punishing the rank of hidden layer robust features. In conclusion, we apply this method to numerical simulation, specifically within the Tennessee Eastman process, and wastewater treatment plants to confirm the model’s efficacy and compare it with other advanced methods. The results show that MLRCAE not only reduces the impact of uncertain data, but also maintains stable performance of the model under different levels of uncertainty. |
|---|---|
| AbstractList | In modern industrial process monitoring, due to equipment performance degradation and equipment working environment, process variable measurement can lead to uncertainty in measurement data. Traditional process monitoring methods based on uncertain data typically assume that variables have the same level of uncertainty. However, factors such as the lifespan of different devices and different working environments result in varying levels of uncertainty in variables. To monitor such processes, a multi scale low-rank convolutional autoencoder (MLRCAE) for process monitoring based on uncertain measurement data is proposed. First, to extract robust multi scale features from uncertain input, a multi scale convolution (MSC) module is designed to reduce the impact of different levels of uncertainty on the model. Second, a low-rank constraint (LRC) loss function is used to prevent models from overfitting uncertain data by punishing the rank of hidden layer robust features. In conclusion, we apply this method to numerical simulation, specifically within the Tennessee Eastman process, and wastewater treatment plants to confirm the model’s efficacy and compare it with other advanced methods. The results show that MLRCAE not only reduces the impact of uncertain data, but also maintains stable performance of the model under different levels of uncertainty. |
| Author | Yan, Xuefeng Yin, Jiawei |
| Author_xml | – sequence: 1 givenname: Jiawei surname: Yin fullname: Yin, Jiawei organization: Key Laboratory of Smart Manufacturing in Energy Chemical Process,Ministry of Education,East China University of Science and Technology, Shanghai, 20023, PR China – sequence: 2 givenname: Xuefeng surname: Yan fullname: Yan, Xuefeng email: xfyan@ecust.edu.cn organization: Key Laboratory of Smart Manufacturing in Energy Chemical Process,Ministry of Education,East China University of Science and Technology, Shanghai, 20023, PR China |
| BookMark | eNp9kL1OwzAURi1UJNrCCzD5BRKunX-Jpar4kyqxwGy59g1ySezIdor69iQqE0OnO52r75wVWVhnkZB7BikDVj4c0iHgkHLgeQpFChVckSWr8jzJiqZekCU0RZUUNYcbsgrhAACMV2xJ2g3txy6aJCjZIe3cD_XSflPl7NF1YzTOyo7KMTq0ymn0tHWeDt4pDIH2zprovLFf1LV02tQZi9LT0Sr0URpLwylE7MMtuW5lF_Du767J5_PTx_Y12b2_vG03u0RlADFhWPO6rEE1mjcamVRQ6kxD0TQo2Z6ptkLMVA0la3NZZUpnUOU6n3yR455na8LPf5V3IXhsxeBNL_1JMBBzKXEQcykxlxJQiImcoPofpEyUs3r00nSX0cczipPU0aAXQZmpFGrjUUWhnbmE_wLlUInS |
| CitedBy_id | crossref_primary_10_3390_math13091371 crossref_primary_10_1016_j_psep_2025_106867 crossref_primary_10_1016_j_psep_2025_107272 crossref_primary_10_1088_1361_6501_addf6c crossref_primary_10_1088_1361_6501_add6c6 crossref_primary_10_1016_j_jtice_2025_106235 |
| Cites_doi | 10.1016/j.psep.2022.12.081 10.1016/j.chemolab.2017.09.021 10.1109/TII.2020.3011441 10.1016/j.isatra.2020.12.046 10.1016/j.jprocont.2020.06.011 10.1016/j.compchemeng.2021.107654 10.1109/JSEN.2020.2991508 10.1016/S0959-1524(00)00041-X 10.1016/0169-7439(95)00076-3 10.1016/j.psep.2022.05.039 10.1109/CVPR.2015.7298789 10.1016/j.compchemeng.2012.06.023 10.1109/CVPR.2015.7298594 10.1016/j.conengprac.2022.105156 10.1016/j.ces.2018.11.063 10.1016/S0959-1524(00)00022-6 10.1016/j.asoc.2019.105527 10.1109/TIE.2014.2301773 10.1016/j.chemolab.2013.02.001 10.1016/j.psep.2021.11.020 10.1109/TCYB.2016.2565898 10.1016/j.ins.2017.09.047 10.1007/s00170-019-03306-z 10.1016/0098-1354(93)80018-I 10.1016/S0959-1524(99)00028-1 10.1080/08982112.2017.1391288 10.1109/CVPR.2017.636 10.1049/smt2.12069 10.1145/1970392.1970395 10.1109/TIP.2017.2725580 10.1016/j.psep.2021.07.002 10.1016/j.asoc.2022.109570 |
| ContentType | Journal Article |
| Copyright | 2024 The Institution of Chemical Engineers |
| Copyright_xml | – notice: 2024 The Institution of Chemical Engineers |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.psep.2024.05.070 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Environmental Sciences |
| EISSN | 1744-3598 |
| EndPage | 63 |
| ExternalDocumentID | 10_1016_j_psep_2024_05_070 S0957582024005780 |
| GroupedDBID | --K --M -QF .~1 0R~ 123 1B1 1~. 1~5 3EH 4.4 457 4G. 4P2 53G 5VS 7-5 71M 8P~ 8WZ A6W AACTN AAEDT AAEDW AAHCO AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AARJD AAXUO ABFNM ABFRF ABFYP ABJNI ABLST ABMAC ABNUV ABXDB ACDAQ ACGFO ACRLP ADBBV ADEWK ADEZE ADMUD AEBSH AEFWE AEKER AENEX AFKWA AFTJW AFXIZ AGHFR AGUBO AGYEJ AHEUO AHIDL AHPOS AIAGR AIEXJ AIKHN AITUG AJOXV AKIFW AKRWK AKURH ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ ASPBG AVWKF AXJTR AZFZN BELTK BKOJK BLECG BLXMC CAG COF CS3 DU5 EBS EDH EFJIC EJD ENUVR EO9 EP2 EP3 FDB FEDTE FGOYB FIRID FNPLU FYGXN GBLVA HVGLF HZ~ I-F IHE J1W JARJE KCYFY KOM M41 ML. MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- ROL RPZ SDF SDG SES SJN SPC SPCBC SSG SSJ SSR SSZ T5K UNMZH ZE2 ~G- 9DU AATTM AAXKI AAYWO AAYXX ABWVN ACLOT ACRPL ACVFH ADCNI ADMLS ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKYEP ANKPU APXCP BANNL CITATION EFKBS EFLBG ~HD |
| ID | FETCH-LOGICAL-c300t-1e828680c9d29de1ac06d3d0599ea1b1cf7ee3c8061f4a73cd3074d4070e2eb23 |
| ISICitedReferencesCount | 7 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001246446200001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0957-5820 |
| IngestDate | Tue Nov 18 22:20:09 EST 2025 Sat Nov 29 01:50:07 EST 2025 Sat Aug 03 15:33:10 EDT 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Process monitoring Uncertainty process 1D convolutional neural network Low rank Multi scale |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c300t-1e828680c9d29de1ac06d3d0599ea1b1cf7ee3c8061f4a73cd3074d4070e2eb23 |
| PageCount | 11 |
| ParticipantIDs | crossref_primary_10_1016_j_psep_2024_05_070 crossref_citationtrail_10_1016_j_psep_2024_05_070 elsevier_sciencedirect_doi_10_1016_j_psep_2024_05_070 |
| PublicationCentury | 2000 |
| PublicationDate | August 2024 2024-08-00 |
| PublicationDateYYYYMMDD | 2024-08-01 |
| PublicationDate_xml | – month: 08 year: 2024 text: August 2024 |
| PublicationDecade | 2020 |
| PublicationTitle | Process safety and environmental protection |
| PublicationYear | 2024 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | Li, Gao, Song, Peng, Xu (bib17) 2021; 15 Chen, Zhang, Yi (bib3) 2018; 424 Xu, Z., Yang, Y., Hauptmann, A.G., A Discriminative Cnn Video Representation for Event Detection, IN: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1798-1807.2015. Candès, Li, Ma, Wright (bib2) 2011; 58 Yin, Ding, Xie, Luo (bib31) 2014; 61 Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A. Going Deeper with Convolutions, IN: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1-9.2014. Wu, Lu, Yan (bib26) 2022; 129 Yu, Yan (bib33) 2021; 153 Downs, Vogel (bib6) 1993; 17 Hu, Zhao, Peng (bib10) 2022; 123 Ding, Z., Shao, M., Fu, Y.,. Low-Rank Embedded Ensemble Semantic Dictionary for Zero-shot Learning, IN: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2050-2058.2017. Ge, Song (bib8) 2013; 123 Lee, Kwon (bib16) 2017; 26 Kesavan, Lee, Saucedo, Krishnagopalan (bib13) 2000; 10 Yong, Linzi (bib32) 2022; 163 Ge (bib7) 2017; 171 Yang, Zhang (bib30) 2020; 17 Zhang, Qiu (bib34) 2022; 158 Lahdhiri, Said, Abdellafou, Taouali, Harkat (bib15) 2019; 102 Li, Qin (bib19) 2001; 11 Ku, Storer, Georgakis (bib14) 1995; 30 Xiu, Yang, Kong, Liu (bib28) 2020; 92 Harkat, Mansouri, Nounou, Nounou (bib9) 2019; 205 Tang, Lu, Yan (bib23) 2023; 171 Huang, Wu, Long, Ji, Sun, Chen, Yang (bib11) 2021; 114 Li, Yue, Valle-Cervantes, Qin (bib20) 2000; 10 Jia, Xu, Liu, Wang (bib12) 2012; 46 Ait-Izem, Harkat, Djeghaba, Kratz (bib1) 2018; 30 Li, Wu, Zhao, Lu (bib18) 2016; 47 Wu, Yan (bib27) 2022; 72 Dhibi, Fezai, Mansouri, Kouadri, Harkat, Bouzara, Nounou, Nounou (bib4) 2020; 20 Song, Jiang (bib21) 2022; 159 Wang, Li, Dai, Lawrence, Yan (bib24) 2019; 82 WenHua, YePang, Chang, SC (bib25) 2015; 58 Yu (10.1016/j.psep.2024.05.070_bib33) 2021; 153 Xiu (10.1016/j.psep.2024.05.070_bib28) 2020; 92 Zhang (10.1016/j.psep.2024.05.070_bib34) 2022; 158 Ku (10.1016/j.psep.2024.05.070_bib14) 1995; 30 Li (10.1016/j.psep.2024.05.070_bib19) 2001; 11 Ge (10.1016/j.psep.2024.05.070_bib8) 2013; 123 Candès (10.1016/j.psep.2024.05.070_bib2) 2011; 58 Dhibi (10.1016/j.psep.2024.05.070_bib4) 2020; 20 Li (10.1016/j.psep.2024.05.070_bib20) 2000; 10 Wang (10.1016/j.psep.2024.05.070_bib24) 2019; 82 10.1016/j.psep.2024.05.070_bib22 Hu (10.1016/j.psep.2024.05.070_bib10) 2022; 123 10.1016/j.psep.2024.05.070_bib29 WenHua (10.1016/j.psep.2024.05.070_bib25) 2015; 58 Song (10.1016/j.psep.2024.05.070_bib21) 2022; 159 Harkat (10.1016/j.psep.2024.05.070_bib9) 2019; 205 Huang (10.1016/j.psep.2024.05.070_bib11) 2021; 114 Li (10.1016/j.psep.2024.05.070_bib17) 2021; 15 Yong (10.1016/j.psep.2024.05.070_bib32) 2022; 163 Chen (10.1016/j.psep.2024.05.070_bib3) 2018; 424 Wu (10.1016/j.psep.2024.05.070_bib27) 2022; 72 Yang (10.1016/j.psep.2024.05.070_bib30) 2020; 17 Tang (10.1016/j.psep.2024.05.070_bib23) 2023; 171 Ge (10.1016/j.psep.2024.05.070_bib7) 2017; 171 Yin (10.1016/j.psep.2024.05.070_bib31) 2014; 61 Li (10.1016/j.psep.2024.05.070_bib18) 2016; 47 Lahdhiri (10.1016/j.psep.2024.05.070_bib15) 2019; 102 Wu (10.1016/j.psep.2024.05.070_bib26) 2022; 129 Jia (10.1016/j.psep.2024.05.070_bib12) 2012; 46 Ait-Izem (10.1016/j.psep.2024.05.070_bib1) 2018; 30 Kesavan (10.1016/j.psep.2024.05.070_bib13) 2000; 10 10.1016/j.psep.2024.05.070_bib5 Lee (10.1016/j.psep.2024.05.070_bib16) 2017; 26 Downs (10.1016/j.psep.2024.05.070_bib6) 1993; 17 |
| References_xml | – volume: 424 start-page: 27 year: 2018 end-page: 38 ident: bib3 article-title: Subspace clustering using a low-rank constrained autoencoder publication-title: Inf. Sci. – volume: 171 start-page: 16 year: 2017 end-page: 25 ident: bib7 article-title: Review on data-driven modeling and monitoring for plant-wide industrial processes publication-title: Chemom. Intell. Lab. Syst. – volume: 82 year: 2019 ident: bib24 article-title: A probabilistic principal component analysis-based approach in process monitoring and fault diagnosis with application in wastewater treatment plant publication-title: Appl. Soft Comput. – volume: 58 start-page: 1 year: 2011 end-page: 37 ident: bib2 article-title: Robust principal component analysis? publication-title: J. ACM – volume: 26 start-page: 4843 year: 2017 end-page: 4855 ident: bib16 article-title: Going deeper with contextual cnn for hyperspectral image classification publication-title: IEEE Trans. Image Process. – volume: 10 start-page: 471 year: 2000 end-page: 486 ident: bib20 article-title: Recursive pca for adaptive process monitoring publication-title: J. Process Control – reference: Ding, Z., Shao, M., Fu, Y.,. Low-Rank Embedded Ensemble Semantic Dictionary for Zero-shot Learning, IN: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2050-2058.2017. – volume: 47 start-page: 3516 year: 2016 end-page: 3529 ident: bib18 article-title: Low-rank discriminant embedding for multiview learning publication-title: IEEE Trans. Cybern. – volume: 123 year: 2022 ident: bib10 article-title: Low-rank reconstruction-based autoencoder for robust fault detection publication-title: Control Eng. Pract. – volume: 163 start-page: 438 year: 2022 end-page: 452 ident: bib32 article-title: Robust deep auto-encoding network for real-time anomaly detection at nuclear power plants publication-title: Process Saf. Environ. Prot. – volume: 114 start-page: 399 year: 2021 end-page: 412 ident: bib11 article-title: Adaptive process monitoring via online dictionary learning and its industrial application publication-title: ISA Trans. – reference: Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A. Going Deeper with Convolutions, IN: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1-9.2014. – volume: 171 start-page: 214 year: 2023 end-page: 224 ident: bib23 article-title: Dual attention bidirectional generative adversarial network for dynamic uncertainty process monitoring and diagnosis publication-title: Process Saf. Environ. Prot. – volume: 123 start-page: 1 year: 2013 end-page: 8 ident: bib8 article-title: Performance-driven ensemble learning ica model for improved non-gaussian process monitoring publication-title: Chemom. Intell. Lab. Syst. – volume: 102 start-page: 2321 year: 2019 end-page: 2337 ident: bib15 article-title: Supervised process monitoring and fault diagnosis based on machine learning methods publication-title: Int. J. Adv. Manuf. Technol. – volume: 15 start-page: 690 year: 2021 end-page: 699 ident: bib17 article-title: Influence of thermally induced self-assembly shish-kebab crystal on charge transport behaviour in polypropylene/elastomer blends publication-title: IET Sci. Meas. Technol. – volume: 11 start-page: 661 year: 2001 end-page: 678 ident: bib19 article-title: Consistent dynamic pca based on errors-in-variables subspace identification publication-title: J. Process Control – volume: 72 start-page: 1 year: 2022 end-page: 10 ident: bib27 article-title: Interval-valued-based stacked attention autoencoder model for process monitoring and fault diagnosis of nonlinear uncertain systems publication-title: IEEE Trans. Instrum. Meas. – volume: 205 start-page: 36 year: 2019 end-page: 45 ident: bib9 article-title: Fault detection of uncertain nonlinear process using interval-valued data-driven approach publication-title: Chem. Eng. Sci. – volume: 159 start-page: 575 year: 2022 end-page: 584 ident: bib21 article-title: A multi-scale convolutional neural network based fault diagnosis model for complex chemical processes publication-title: Process Saf. Environ. Prot. – volume: 58 start-page: 1 year: 2015 end-page: 052102 ident: bib25 article-title: A survey on dependability improvement techniques for pervasive computing systems publication-title: Inf. Sci. – volume: 17 start-page: 245 year: 1993 end-page: 255 ident: bib6 article-title: A plant-wide industrial process control problem publication-title: Comput. Chem. Eng. – volume: 30 start-page: 635 year: 2018 end-page: 647 ident: bib1 article-title: Sensor fault detection based on principal component analysis for interval-valued data publication-title: Qual. Eng. – volume: 153 start-page: 47 year: 2021 end-page: 59 ident: bib33 article-title: A new deep model based on the stacked autoencoder with intensified iterative learning style for industrial fault detection publication-title: Process Saf. Environ. Prot. – reference: Xu, Z., Yang, Y., Hauptmann, A.G., A Discriminative Cnn Video Representation for Event Detection, IN: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1798-1807.2015. – volume: 46 start-page: 94 year: 2012 end-page: 104 ident: bib12 article-title: The optimization of the kind and parameters of kernel function in kpca for process monitoring publication-title: Comput. Chem. Eng. – volume: 30 start-page: 179 year: 1995 end-page: 196 ident: bib14 article-title: Disturbance detection and isolation by dynamic principal component analysis publication-title: Chemom. Intell. Lab. Syst. – volume: 10 start-page: 229 year: 2000 end-page: 236 ident: bib13 article-title: Partial least squares (pls) based monitoring and control of batch digesters publication-title: J. Process Control – volume: 17 start-page: 6390 year: 2020 end-page: 6398 ident: bib30 article-title: A conditional convolutional autoencoder-based method for monitoring wind turbine blade breakages publication-title: IEEE Trans. Ind. Inform. – volume: 92 start-page: 212 year: 2020 end-page: 219 ident: bib28 article-title: Laplacian regularized robust principal component analysis for process monitoring publication-title: J. Process Control – volume: 158 year: 2022 ident: bib34 article-title: A dynamic-inner convolutional autoencoder for process monitoring publication-title: Comput. Chem. Eng. – volume: 61 start-page: 6418 year: 2014 end-page: 6428 ident: bib31 article-title: A review on basic data-driven approaches for industrial process monitoring publication-title: IEEE Trans. Ind. Electron. – volume: 129 year: 2022 ident: bib26 article-title: Process monitoring of nonlinear uncertain systems based on part interval stacked autoencoder and support vector data description publication-title: Appl. Soft Comput. – volume: 20 start-page: 10228 year: 2020 end-page: 10239 ident: bib4 article-title: A hybrid approach for process monitoring: improving data-driven methodologies with dataset size reduction and interval-valued representation publication-title: IEEE Sens. J. – volume: 58 start-page: 1 year: 2015 ident: 10.1016/j.psep.2024.05.070_bib25 article-title: A survey on dependability improvement techniques for pervasive computing systems publication-title: Inf. Sci. – volume: 171 start-page: 214 year: 2023 ident: 10.1016/j.psep.2024.05.070_bib23 article-title: Dual attention bidirectional generative adversarial network for dynamic uncertainty process monitoring and diagnosis publication-title: Process Saf. Environ. Prot. doi: 10.1016/j.psep.2022.12.081 – volume: 171 start-page: 16 year: 2017 ident: 10.1016/j.psep.2024.05.070_bib7 article-title: Review on data-driven modeling and monitoring for plant-wide industrial processes publication-title: Chemom. Intell. Lab. Syst. doi: 10.1016/j.chemolab.2017.09.021 – volume: 72 start-page: 1 year: 2022 ident: 10.1016/j.psep.2024.05.070_bib27 article-title: Interval-valued-based stacked attention autoencoder model for process monitoring and fault diagnosis of nonlinear uncertain systems publication-title: IEEE Trans. Instrum. Meas. – volume: 17 start-page: 6390 year: 2020 ident: 10.1016/j.psep.2024.05.070_bib30 article-title: A conditional convolutional autoencoder-based method for monitoring wind turbine blade breakages publication-title: IEEE Trans. Ind. Inform. doi: 10.1109/TII.2020.3011441 – volume: 114 start-page: 399 year: 2021 ident: 10.1016/j.psep.2024.05.070_bib11 article-title: Adaptive process monitoring via online dictionary learning and its industrial application publication-title: ISA Trans. doi: 10.1016/j.isatra.2020.12.046 – volume: 92 start-page: 212 year: 2020 ident: 10.1016/j.psep.2024.05.070_bib28 article-title: Laplacian regularized robust principal component analysis for process monitoring publication-title: J. Process Control doi: 10.1016/j.jprocont.2020.06.011 – volume: 158 year: 2022 ident: 10.1016/j.psep.2024.05.070_bib34 article-title: A dynamic-inner convolutional autoencoder for process monitoring publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2021.107654 – volume: 20 start-page: 10228 year: 2020 ident: 10.1016/j.psep.2024.05.070_bib4 article-title: A hybrid approach for process monitoring: improving data-driven methodologies with dataset size reduction and interval-valued representation publication-title: IEEE Sens. J. doi: 10.1109/JSEN.2020.2991508 – volume: 11 start-page: 661 year: 2001 ident: 10.1016/j.psep.2024.05.070_bib19 article-title: Consistent dynamic pca based on errors-in-variables subspace identification publication-title: J. Process Control doi: 10.1016/S0959-1524(00)00041-X – volume: 30 start-page: 179 year: 1995 ident: 10.1016/j.psep.2024.05.070_bib14 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: 163 start-page: 438 year: 2022 ident: 10.1016/j.psep.2024.05.070_bib32 article-title: Robust deep auto-encoding network for real-time anomaly detection at nuclear power plants publication-title: Process Saf. Environ. Prot. doi: 10.1016/j.psep.2022.05.039 – ident: 10.1016/j.psep.2024.05.070_bib29 doi: 10.1109/CVPR.2015.7298789 – volume: 46 start-page: 94 year: 2012 ident: 10.1016/j.psep.2024.05.070_bib12 article-title: The optimization of the kind and parameters of kernel function in kpca for process monitoring publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2012.06.023 – ident: 10.1016/j.psep.2024.05.070_bib22 doi: 10.1109/CVPR.2015.7298594 – volume: 123 year: 2022 ident: 10.1016/j.psep.2024.05.070_bib10 article-title: Low-rank reconstruction-based autoencoder for robust fault detection publication-title: Control Eng. Pract. doi: 10.1016/j.conengprac.2022.105156 – volume: 205 start-page: 36 year: 2019 ident: 10.1016/j.psep.2024.05.070_bib9 article-title: Fault detection of uncertain nonlinear process using interval-valued data-driven approach publication-title: Chem. Eng. Sci. doi: 10.1016/j.ces.2018.11.063 – volume: 10 start-page: 471 year: 2000 ident: 10.1016/j.psep.2024.05.070_bib20 article-title: Recursive pca for adaptive process monitoring publication-title: J. Process Control doi: 10.1016/S0959-1524(00)00022-6 – volume: 82 year: 2019 ident: 10.1016/j.psep.2024.05.070_bib24 article-title: A probabilistic principal component analysis-based approach in process monitoring and fault diagnosis with application in wastewater treatment plant publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2019.105527 – volume: 61 start-page: 6418 year: 2014 ident: 10.1016/j.psep.2024.05.070_bib31 article-title: A review on basic data-driven approaches for industrial process monitoring publication-title: IEEE Trans. Ind. Electron. doi: 10.1109/TIE.2014.2301773 – volume: 123 start-page: 1 year: 2013 ident: 10.1016/j.psep.2024.05.070_bib8 article-title: Performance-driven ensemble learning ica model for improved non-gaussian process monitoring publication-title: Chemom. Intell. Lab. Syst. doi: 10.1016/j.chemolab.2013.02.001 – volume: 159 start-page: 575 year: 2022 ident: 10.1016/j.psep.2024.05.070_bib21 article-title: A multi-scale convolutional neural network based fault diagnosis model for complex chemical processes publication-title: Process Saf. Environ. Prot. doi: 10.1016/j.psep.2021.11.020 – volume: 47 start-page: 3516 year: 2016 ident: 10.1016/j.psep.2024.05.070_bib18 article-title: Low-rank discriminant embedding for multiview learning publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2016.2565898 – volume: 424 start-page: 27 year: 2018 ident: 10.1016/j.psep.2024.05.070_bib3 article-title: Subspace clustering using a low-rank constrained autoencoder publication-title: Inf. Sci. doi: 10.1016/j.ins.2017.09.047 – volume: 102 start-page: 2321 year: 2019 ident: 10.1016/j.psep.2024.05.070_bib15 article-title: Supervised process monitoring and fault diagnosis based on machine learning methods publication-title: Int. J. Adv. Manuf. Technol. doi: 10.1007/s00170-019-03306-z – volume: 17 start-page: 245 year: 1993 ident: 10.1016/j.psep.2024.05.070_bib6 article-title: A plant-wide industrial process control problem publication-title: Comput. Chem. Eng. doi: 10.1016/0098-1354(93)80018-I – volume: 10 start-page: 229 year: 2000 ident: 10.1016/j.psep.2024.05.070_bib13 article-title: Partial least squares (pls) based monitoring and control of batch digesters publication-title: J. Process Control doi: 10.1016/S0959-1524(99)00028-1 – volume: 30 start-page: 635 year: 2018 ident: 10.1016/j.psep.2024.05.070_bib1 article-title: Sensor fault detection based on principal component analysis for interval-valued data publication-title: Qual. Eng. doi: 10.1080/08982112.2017.1391288 – ident: 10.1016/j.psep.2024.05.070_bib5 doi: 10.1109/CVPR.2017.636 – volume: 15 start-page: 690 year: 2021 ident: 10.1016/j.psep.2024.05.070_bib17 article-title: Influence of thermally induced self-assembly shish-kebab crystal on charge transport behaviour in polypropylene/elastomer blends publication-title: IET Sci. Meas. Technol. doi: 10.1049/smt2.12069 – volume: 58 start-page: 1 year: 2011 ident: 10.1016/j.psep.2024.05.070_bib2 article-title: Robust principal component analysis? publication-title: J. ACM doi: 10.1145/1970392.1970395 – volume: 26 start-page: 4843 year: 2017 ident: 10.1016/j.psep.2024.05.070_bib16 article-title: Going deeper with contextual cnn for hyperspectral image classification publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2017.2725580 – volume: 153 start-page: 47 year: 2021 ident: 10.1016/j.psep.2024.05.070_bib33 article-title: A new deep model based on the stacked autoencoder with intensified iterative learning style for industrial fault detection publication-title: Process Saf. Environ. Prot. doi: 10.1016/j.psep.2021.07.002 – volume: 129 year: 2022 ident: 10.1016/j.psep.2024.05.070_bib26 article-title: Process monitoring of nonlinear uncertain systems based on part interval stacked autoencoder and support vector data description publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2022.109570 |
| SSID | ssj0001271 |
| Score | 2.4055824 |
| Snippet | In modern industrial process monitoring, due to equipment performance degradation and equipment working environment, process variable measurement can lead to... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 53 |
| SubjectTerms | 1D convolutional neural network Low rank Multi scale Process monitoring Uncertainty process |
| Title | A multi-scale low rank convolutional autoencoder for process monitoring of nonlinear uncertain systems |
| URI | https://dx.doi.org/10.1016/j.psep.2024.05.070 |
| Volume | 188 |
| WOSCitedRecordID | wos001246446200001&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: 1744-3598 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001271 issn: 0957-5820 databaseCode: AIEXJ dateStart: 19961101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3Pb9MwFLZKxwEOaAwmxgbygVtkFCdOnByrqWhMaEJiSOUUOf4hdSpt1bTd_nzeq500AzQBEpeoiuLa8vv8_PLyvc-EvFN1xl2uHFggN0yonLNa6oKZPEviWmbGeUt_kldXxWRSfh4Mtm0tzHYm5_Pi7q5c_ldTwz0wNpbO_oW5uz-FG_AbjA5XMDtc_8jwI08SZA3Mvo1mi9sIz2Xf0ctDvygPsFkvUMISlSSQaLj09QLR990SXwUq9NzraKhVBLuf5w4E6eemH9SGYoOoUQ4JoJiK79XPYamXF4PoffL_Fg4Bm6pbO-1u-mTsZGOdDRtqyEckomPDhSRZWyizZyX5bKNkWZH4TzDW-1opBEMBwfvOuOi5U68jHDZm7wh_cfk--3DzftlY1B9NxE6JVcb7Da6jHX7BYeAokDgLrip-RA4SCSMYkoPRx_HkstvDebJ7Ve-GHcqtPDPw555-H9L0wpTrQ_IsvF_QkcfFczKw8yPytKc6eUSOx33j0ODdmxfEjWgPOhSgQxE69B50aA86FKBDA3ToHjp04WgHHdpBhwbovCRfP4yvzy9YOIeD6TSO14xb1BooYl2apDSWKw3rOjWo7GMVr7l20tpUFxAaOqFkqg1sHMIImBqb2DpJj8kQerWvCLUY37tClFwowXWsVOkkt3ktRVZqo08Ib6ey0kGkHs9KmVUtG_GmwumvcPqrOKugjxMSdW2WXqLlwaez1kJVCDJ98FgBoB5o9_of252SJ_tVckaG69XGviGP9XY9bVZvA-5-ACj0qEk |
| 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=A+multi-scale+low+rank+convolutional+autoencoder+for+process+monitoring+of+nonlinear+uncertain+systems&rft.jtitle=Process+safety+and+environmental+protection&rft.au=Yin%2C+Jiawei&rft.au=Yan%2C+Xuefeng&rft.date=2024-08-01&rft.pub=Elsevier+Ltd&rft.issn=0957-5820&rft.eissn=1744-3598&rft.volume=188&rft.spage=53&rft.epage=63&rft_id=info:doi/10.1016%2Fj.psep.2024.05.070&rft.externalDocID=S0957582024005780 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0957-5820&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0957-5820&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0957-5820&client=summon |