A deep learning model for process fault prognosis
Early fault detection and fault prognosis are crucial functions to ensure safe process operations. Fault prognosis can detect and isolate early developing faults as well as predict fault propagation. To promptly detect potential faults in process systems, it is important to examine the fault symptom...
Uloženo v:
| Vydáno v: | Process safety and environmental protection Ročník 154; s. 467 - 479 |
|---|---|
| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Rugby
Elsevier B.V
01.10.2021
Elsevier Science Ltd |
| Témata: | |
| ISSN: | 0957-5820, 1744-3598 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Early fault detection and fault prognosis are crucial functions to ensure safe process operations. Fault prognosis can detect and isolate early developing faults as well as predict fault propagation. To promptly detect potential faults in process systems, it is important to examine the fault symptoms as early as possible. In recent years, fault prognosis approaches have led to the remaining useful life prediction. Therefore, in a process system, advancing prognosis approaches will be beneficial for early fault detection in terms of process safety, and to predict the remaining useful life, targeting the system's reliability. In data-driven models, early fault detection is regarded as a time-dependent sequence learning problem; the future data sequence is predicted using the previous data pattern. Studying recent years' research shows that a recurrent neural network (RNN) can solve the sequence learning problem. This paper proposes an early potential fault detection approach by examining the fault symptoms in multivariate complex process systems. The proposed model has been developed using the Convolutional Neural Network (CNN)- Long Short-Term Memory (LSTM) approach to forecast the system parameters for future sampling windows' recognition and an unsupervised One-class-SVM used for fault symptoms' detection using forecasted data window. The performance of the proposed method is assessed using Tennessee Eastman process time-series data. The results confirm that the proposed method effectively detects potential fault conditions in multivariate dynamic systems by detecting the fault symptoms early as possible. |
|---|---|
| AbstractList | Early fault detection and fault prognosis are crucial functions to ensure safe process operations. Fault prognosis can detect and isolate early developing faults as well as predict fault propagation. To promptly detect potential faults in process systems, it is important to examine the fault symptoms as early as possible. In recent years, fault prognosis approaches have led to the remaining useful life prediction. Therefore, in a process system, advancing prognosis approaches will be beneficial for early fault detection in terms of process safety, and to predict the remaining useful life, targeting the system's reliability. In data-driven models, early fault detection is regarded as a time-dependent sequence learning problem; the future data sequence is predicted using the previous data pattern. Studying recent years' research shows that a recurrent neural network (RNN) can solve the sequence learning problem. This paper proposes an early potential fault detection approach by examining the fault symptoms in multivariate complex process systems. The proposed model has been developed using the Convolutional Neural Network (CNN)- Long Short-Term Memory (LSTM) approach to forecast the system parameters for future sampling windows' recognition and an unsupervised One-class-SVM used for fault symptoms' detection using forecasted data window. The performance of the proposed method is assessed using Tennessee Eastman process time-series data. The results confirm that the proposed method effectively detects potential fault conditions in multivariate dynamic systems by detecting the fault symptoms early as possible. |
| Author | Imtiaz, Syed Khan, Faisal Ahmed, Salim Arunthavanathan, Rajeevan |
| Author_xml | – sequence: 1 givenname: Rajeevan surname: Arunthavanathan fullname: Arunthavanathan, Rajeevan organization: Centre for Risk, Integrity, and Safety Engineering (C-RISE), Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's NL, A1B 3X5, Canada – sequence: 2 givenname: Faisal orcidid: 0000-0002-5638-4299 surname: Khan fullname: Khan, Faisal email: fikhan@mun.ca, fikhan@tamu.edu organization: Centre for Risk, Integrity, and Safety Engineering (C-RISE), Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's NL, A1B 3X5, Canada – sequence: 3 givenname: Salim surname: Ahmed fullname: Ahmed, Salim organization: Centre for Risk, Integrity, and Safety Engineering (C-RISE), Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's NL, A1B 3X5, Canada – sequence: 4 givenname: Syed surname: Imtiaz fullname: Imtiaz, Syed organization: Centre for Risk, Integrity, and Safety Engineering (C-RISE), Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's NL, A1B 3X5, Canada |
| BookMark | eNp9kE1rwzAMhs3oYG23P7BTYOdksuykCexSyr6gsMt2No6tFIc0zux0sH-_hO60w9BBCN5HQs-KLXrfE2O3HDIOvLhvsyHSkCEgz6DMAPGCLflGylTkVblgS6jyTZqXCFdsFWMLABw3fMn4NrFEQ9KRDr3rD8nRW-qSxodkCN5QjEmjT904T4feRxev2WWju0g3v33NPp4e33cv6f7t-XW33adGYDmmtrBaWBSVJl0ggKxLKREFmpxPVfESpZUNt0Vdk8CCy2aKoRZYN9bUlVizu_Pe6fLnieKoWn8K_XRSYQEgZCVATCk8p0zwMQZq1BDcUYdvxUHNalSrZjVqVqOgVJOaCSr_QMaNenS-H4N23f_owxml6fUvR0FF46g3ZF0gMyrr3X_4D_YTf7A |
| CitedBy_id | crossref_primary_10_1016_j_psep_2024_12_068 crossref_primary_10_1016_j_jii_2023_100490 crossref_primary_10_1016_j_psep_2024_12_066 crossref_primary_10_1016_j_compchemeng_2025_109394 crossref_primary_10_1016_j_psep_2022_03_023 crossref_primary_10_3390_pr10030435 crossref_primary_10_1016_j_psep_2025_107156 crossref_primary_10_1016_j_psep_2023_05_025 crossref_primary_10_1016_j_psep_2025_107438 crossref_primary_10_1016_j_eswa_2023_121434 crossref_primary_10_1016_j_psep_2024_06_082 crossref_primary_10_1016_j_compchemeng_2024_108855 crossref_primary_10_1016_j_psep_2024_08_115 crossref_primary_10_1016_j_psep_2024_06_122 crossref_primary_10_1016_j_pnucene_2025_106038 crossref_primary_10_3390_electronics12020312 crossref_primary_10_1108_BPMJ_07_2021_0484 crossref_primary_10_1016_j_compind_2022_103771 crossref_primary_10_1016_j_psep_2023_06_004 crossref_primary_10_1016_j_ssci_2023_106381 crossref_primary_10_3390_pr12071432 crossref_primary_10_1007_s11063_024_11577_1 crossref_primary_10_1016_j_psep_2025_106871 crossref_primary_10_1016_j_psep_2023_02_078 crossref_primary_10_1016_j_psep_2023_02_079 crossref_primary_10_1016_j_psep_2024_10_075 crossref_primary_10_1016_j_ress_2022_109079 crossref_primary_10_1016_j_jprocont_2022_01_002 crossref_primary_10_1109_TASE_2023_3325565 crossref_primary_10_1007_s41872_025_00341_6 crossref_primary_10_1007_s11814_025_00524_y crossref_primary_10_1016_j_psep_2023_01_077 crossref_primary_10_1016_j_psep_2023_04_007 crossref_primary_10_1016_j_psep_2022_07_019 crossref_primary_10_1016_j_psep_2022_12_047 crossref_primary_10_1109_JSYST_2022_3205179 crossref_primary_10_1016_j_oceaneng_2022_112455 crossref_primary_10_1016_j_petrol_2021_109817 crossref_primary_10_3390_s23198096 crossref_primary_10_1016_j_psep_2024_03_001 crossref_primary_10_1016_j_psep_2025_107385 crossref_primary_10_1016_j_psep_2025_107781 crossref_primary_10_1016_j_jlp_2023_105185 crossref_primary_10_26599_BDMA_2023_9020002 crossref_primary_10_1016_j_psep_2024_04_012 crossref_primary_10_1016_j_compchemeng_2023_108359 crossref_primary_10_1016_j_engappai_2025_111218 crossref_primary_10_1002_cjce_24961 crossref_primary_10_1016_j_dche_2025_100227 crossref_primary_10_3390_e27070736 crossref_primary_10_1016_j_psep_2023_12_071 crossref_primary_10_1016_j_psep_2024_11_057 crossref_primary_10_1016_j_psep_2025_107272 crossref_primary_10_1016_j_psep_2024_09_033 crossref_primary_10_1016_j_psep_2022_05_012 crossref_primary_10_1016_j_cherd_2025_07_041 crossref_primary_10_1016_j_engappai_2024_108046 crossref_primary_10_1016_j_cherd_2022_06_029 crossref_primary_10_1016_j_compchemeng_2023_108346 crossref_primary_10_1016_j_psep_2025_107704 crossref_primary_10_1002_cjce_25701 crossref_primary_10_1109_JSEN_2022_3233585 crossref_primary_10_1016_j_psep_2022_05_073 crossref_primary_10_1016_j_ces_2024_120699 crossref_primary_10_1016_j_psep_2024_07_086 crossref_primary_10_1016_j_psep_2023_02_023 crossref_primary_10_1007_s12572_023_00327_6 crossref_primary_10_1016_j_psep_2023_07_083 crossref_primary_10_1016_j_psep_2025_106942 crossref_primary_10_1016_j_psep_2022_12_018 crossref_primary_10_1016_j_rsase_2023_101117 crossref_primary_10_1016_j_psep_2022_11_062 crossref_primary_10_1109_ACCESS_2023_3334012 crossref_primary_10_1109_TIM_2024_3480200 crossref_primary_10_1109_JIOT_2022_3230945 crossref_primary_10_1016_j_psep_2022_04_039 crossref_primary_10_1016_j_psep_2022_10_086 crossref_primary_10_2118_215831_PA crossref_primary_10_1016_j_psep_2022_01_062 crossref_primary_10_1016_j_psep_2025_107883 crossref_primary_10_1016_j_psep_2025_107885 crossref_primary_10_1016_j_petsci_2025_04_006 crossref_primary_10_1016_j_psep_2025_107400 crossref_primary_10_1016_j_psep_2025_107884 crossref_primary_10_1016_j_psep_2022_11_076 crossref_primary_10_1016_j_psep_2023_07_094 crossref_primary_10_1016_j_psep_2023_11_055 crossref_primary_10_1016_j_psep_2025_106834 crossref_primary_10_1002_cjce_25206 crossref_primary_10_1016_j_psep_2023_04_020 crossref_primary_10_1002_cjce_25460 crossref_primary_10_3390_pr11061608 crossref_primary_10_1002_cjce_24770 crossref_primary_10_1016_j_asoc_2022_109427 crossref_primary_10_1016_j_psep_2024_11_086 crossref_primary_10_1108_RIA_03_2024_0061 crossref_primary_10_1016_j_apenergy_2023_121030 crossref_primary_10_1016_j_psep_2025_107066 crossref_primary_10_1016_j_psep_2024_05_125 crossref_primary_10_1016_j_psep_2024_10_043 crossref_primary_10_1016_j_psep_2025_107579 crossref_primary_10_1016_j_psep_2025_107578 crossref_primary_10_1016_j_psep_2025_107217 crossref_primary_10_3390_pr12122824 crossref_primary_10_1016_j_psep_2024_06_060 crossref_primary_10_1016_j_wear_2023_204793 crossref_primary_10_1016_j_psep_2022_06_003 crossref_primary_10_1016_j_psep_2022_09_039 crossref_primary_10_1002_cjce_25350 crossref_primary_10_3390_pr12112589 crossref_primary_10_1016_j_psep_2024_09_114 crossref_primary_10_1016_j_ces_2024_120565 crossref_primary_10_1016_j_psep_2024_05_143 crossref_primary_10_1016_j_psep_2024_09_079 crossref_primary_10_1016_j_psep_2021_11_034 crossref_primary_10_1016_j_compchemeng_2023_108386 crossref_primary_10_1016_j_compchemeng_2024_108600 crossref_primary_10_1016_j_psep_2025_107468 crossref_primary_10_1016_j_psep_2023_02_058 crossref_primary_10_1016_j_cjche_2025_05_020 crossref_primary_10_1016_j_oceaneng_2023_114318 crossref_primary_10_1016_j_ces_2023_118900 |
| Cites_doi | 10.1016/j.jprocont.2009.07.011 10.1021/acs.iecr.9b00524 10.1162/neco.1997.9.8.1735 10.1109/TIE.2015.2417501 10.3390/s19214612 10.1016/j.ress.2017.11.021 10.1109/ACCESS.2020.2971348 10.1016/j.compchemeng.2019.106697 10.1016/j.isatra.2019.07.001 10.1016/j.neucom.2018.06.078 10.1002/aic.16497 10.1016/j.ress.2021.107646 10.1016/j.engappai.2019.09.002 10.1016/0098-1354(88)87015-7 10.1016/0005-1098(93)90090-G 10.1109/TNNLS.2016.2582798 10.1109/ACCESS.2018.2794765 10.1016/j.bspc.2018.08.035 10.1016/j.compchemeng.2020.107197 10.1016/j.jprocont.2020.06.005 10.1016/0098-1354(93)80018-I 10.1016/j.ymssp.2017.01.050 10.1016/j.compind.2019.103182 10.1109/TII.2018.2866549 |
| ContentType | Journal Article |
| Copyright | 2021 Institution of Chemical Engineers Copyright Elsevier Science Ltd. Oct 2021 |
| Copyright_xml | – notice: 2021 Institution of Chemical Engineers – notice: Copyright Elsevier Science Ltd. Oct 2021 |
| DBID | AAYXX CITATION 7ST 7TB 7U7 8FD C1K FR3 KR7 SOI |
| DOI | 10.1016/j.psep.2021.08.022 |
| DatabaseName | CrossRef Environment Abstracts Mechanical & Transportation Engineering Abstracts Toxicology Abstracts Technology Research Database Environmental Sciences and Pollution Management Engineering Research Database Civil Engineering Abstracts Environment Abstracts |
| DatabaseTitle | CrossRef Civil Engineering Abstracts Technology Research Database Toxicology Abstracts Mechanical & Transportation Engineering Abstracts Engineering Research Database Environment Abstracts Environmental Sciences and Pollution Management |
| DatabaseTitleList | Civil Engineering Abstracts |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Environmental Sciences |
| EISSN | 1744-3598 |
| EndPage | 479 |
| ExternalDocumentID | 10_1016_j_psep_2021_08_022 S0957582021004481 |
| 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 AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AARJD AAXUO ABFNM ABFRF ABFYP ABJNI ABLST ABMAC ABNUV ABXDB ABYKQ ACDAQ ACGFO ACRLP ADBBV ADEWK ADEZE ADMUD AEBSH AEFWE AEKER AENEX AFKWA AFTJW AFXIZ AGHFR AGUBO AGYEJ AHEUO AHIDL AHPOS AIAGR AIEXJ AIKHN AITUG AJBFU AJOXV AKIFW AKURH ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ ASPBG AVWKF AXJTR AZFZN BELTK BKOJK BLECG BLXMC CAG COF CS3 DU5 EBS EDH EFJIC EFLBG 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 XFK ZE2 ~G- 9DU AATTM AAXKI AAYWO AAYXX ABWVN ACLOT ACRPL ACVFH ADCNI ADMLS ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP BANNL CITATION EFKBS ~HD 7ST 7TB 7U7 8FD AGCQF C1K FR3 KR7 SOI |
| ID | FETCH-LOGICAL-c328t-d6da3d239aea62004b8442232c5151591824d4f1d6bbe32614f6202a32bfdcb93 |
| ISICitedReferencesCount | 131 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000704392700013&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 | Wed Aug 13 09:50:10 EDT 2025 Sat Nov 29 07:05:40 EST 2025 Tue Nov 18 22:00:43 EST 2025 Fri Feb 23 02:42:24 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Fault diagnosis Process safety Data-driven model LSTM model Fault prognosis |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c328t-d6da3d239aea62004b8442232c5151591824d4f1d6bbe32614f6202a32bfdcb93 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0002-5638-4299 |
| PQID | 2600349303 |
| PQPubID | 2047550 |
| PageCount | 13 |
| ParticipantIDs | proquest_journals_2600349303 crossref_primary_10_1016_j_psep_2021_08_022 crossref_citationtrail_10_1016_j_psep_2021_08_022 elsevier_sciencedirect_doi_10_1016_j_psep_2021_08_022 |
| PublicationCentury | 2000 |
| PublicationDate | October 2021 2021-10-00 20211001 |
| PublicationDateYYYYMMDD | 2021-10-01 |
| PublicationDate_xml | – month: 10 year: 2021 text: October 2021 |
| PublicationDecade | 2020 |
| PublicationPlace | Rugby |
| PublicationPlace_xml | – name: Rugby |
| PublicationTitle | Process safety and environmental protection |
| PublicationYear | 2021 |
| Publisher | Elsevier B.V Elsevier Science Ltd |
| Publisher_xml | – name: Elsevier B.V – name: Elsevier Science Ltd |
| References | Arunthavanathan, Khan, Ahmed, Imtiaz (bib0015) 2021 Biagetti, Sciubba (bib0020) 2004 Hoang, Kang (bib0055) 2019; 335 Verstraete, Droguett, Modarres (bib0130) 2020; 20 Li, Hua, Wu (bib0080) 2020; 8 Pan, He, Tang, Meng (bib0110) 2018; 64 Zheng, Xue, Chen, Guo, Chen, Chen, Gao (bib0165) 2019 Schölkopf, Williamson, Smola, Shawe-Taylor, Piatt (bib0120) 2000 Yue, Ping, Lanxin (bib0145) 2018 Hochreiter, Schmidhuber (bib0060) 1997; 9 Mahadevan, Shah (bib0100) 2009; 19 Galagedarage Don, Khan (bib0040) 2019; 58 Gao, Cecati, Ding (bib0045) 2015; 62 Zhao, Sun, Jin (bib0155) 2018; 6 Downs, Vogel (bib0035) 1993; 17 Djeziri, Benmoussa, Benbouzid (bib0030) 2019; 86 Cheng, Zhu, Wu, Shao (bib0025) 2019; 15 Sorsa, Koivo (bib0125) 1993; 29 Onel, Kieslich, Pistikopoulos (bib0105) 2019; 65 Zhao, Mao, Chen (bib0160) 2019; 47 Abdel-Hamid, Mohamed, Jiang, Penn (bib0005) 2012 Han, Ding, Geng, Wang, Chu (bib0050) 2020; 92 Liu, Pan, Lei, Hu, Zuo (bib0095) 2021; 214 Li, Ding, Sun (bib0075) 2018; 172 Javed, Gouriveau, Zerhouni (bib0070) 2017 Liu, Yao, Zhang, Liao (bib0090) 2019 Hoskins, Himmelblau (bib0065) 1988; 12 Zhang, Lim, Qin, Tan (bib0150) 2017; 28 Lin, Tao (bib0085) 2019 Arunthavanathan, Khan, Ahmed, Imtiaz, Rusli (bib0010) 2020; 134 Wang, Pan, Yuan, Yang, Gui (bib0135) 2020; 96 Park, Di Marco, Shin, Bang (bib0115) 2019; 19 Xia, Song, Zheng, Pan, Xi (bib0140) 2020; 115 Zhao (10.1016/j.psep.2021.08.022_bib0155) 2018; 6 Li (10.1016/j.psep.2021.08.022_bib0080) 2020; 8 Zhang (10.1016/j.psep.2021.08.022_bib0150) 2017; 28 Downs (10.1016/j.psep.2021.08.022_bib0035) 1993; 17 Yue (10.1016/j.psep.2021.08.022_bib0145) 2018 Zheng (10.1016/j.psep.2021.08.022_bib0165) 2019 Mahadevan (10.1016/j.psep.2021.08.022_bib0100) 2009; 19 Arunthavanathan (10.1016/j.psep.2021.08.022_bib0010) 2020; 134 Liu (10.1016/j.psep.2021.08.022_bib0095) 2021; 214 Hoang (10.1016/j.psep.2021.08.022_bib0055) 2019; 335 Sorsa (10.1016/j.psep.2021.08.022_bib0125) 1993; 29 Pan (10.1016/j.psep.2021.08.022_bib0110) 2018; 64 Lin (10.1016/j.psep.2021.08.022_bib0085) 2019 Abdel-Hamid (10.1016/j.psep.2021.08.022_bib0005) 2012 Javed (10.1016/j.psep.2021.08.022_bib0070) 2017 Verstraete (10.1016/j.psep.2021.08.022_bib0130) 2020; 20 Wang (10.1016/j.psep.2021.08.022_bib0135) 2020; 96 Liu (10.1016/j.psep.2021.08.022_bib0090) 2019 Cheng (10.1016/j.psep.2021.08.022_bib0025) 2019; 15 Gao (10.1016/j.psep.2021.08.022_bib0045) 2015; 62 Xia (10.1016/j.psep.2021.08.022_bib0140) 2020; 115 Onel (10.1016/j.psep.2021.08.022_bib0105) 2019; 65 Schölkopf (10.1016/j.psep.2021.08.022_bib0120) 2000 Park (10.1016/j.psep.2021.08.022_bib0115) 2019; 19 Galagedarage Don (10.1016/j.psep.2021.08.022_bib0040) 2019; 58 Biagetti (10.1016/j.psep.2021.08.022_bib0020) 2004 Arunthavanathan (10.1016/j.psep.2021.08.022_bib0015) 2021 Zhao (10.1016/j.psep.2021.08.022_bib0160) 2019; 47 Hochreiter (10.1016/j.psep.2021.08.022_bib0060) 1997; 9 Djeziri (10.1016/j.psep.2021.08.022_bib0030) 2019; 86 Hoskins (10.1016/j.psep.2021.08.022_bib0065) 1988; 12 Li (10.1016/j.psep.2021.08.022_bib0075) 2018; 172 Han (10.1016/j.psep.2021.08.022_bib0050) 2020; 92 |
| References_xml | – volume: 96 start-page: 457 year: 2020 end-page: 467 ident: bib0135 article-title: A novel deep learning based fault diagnosis approach for chemical process with extended deep belief network publication-title: ISA Trans. – volume: 214 start-page: 107646 year: 2021 ident: bib0095 article-title: Fault prediction of bearings based on LSTM and statistical process analysis publication-title: Reliab. Eng. Syst. Saf. – volume: 47 start-page: 312 year: 2019 end-page: 323 ident: bib0160 article-title: Speech emotion recognition using deep 1D & 2D CNN LSTM networks publication-title: Biomed. Signal Process. Control – start-page: 1 year: 2019 end-page: 9 ident: bib0085 article-title: A novel bearing health indicator construction method based on ensemble stacked autoencoder publication-title: 2019 IEEE International Conference on Prognostics and Health Management – start-page: 4277 year: 2012 end-page: 4280 ident: bib0005 article-title: Applying convolutional neural networks concepts to hybrid NN-HMM model for speech recognition publication-title: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings – volume: 92 start-page: 161 year: 2020 end-page: 168 ident: bib0050 article-title: An optimized long short-term memory network based fault diagnosis model for chemical processes publication-title: J. Process Control – start-page: 489 year: 2019 end-page: 493 ident: bib0090 article-title: Attention based Echo state Network: A novel approach for fault prognosis publication-title: 11th International Conference on Machine Learning and Computing, ICMLC 2019 – volume: 64 start-page: 443 year: 2018 end-page: 452 ident: bib0110 article-title: An improved bearing fault diagnosis method using one-dimensional CNN and LSTM publication-title: J. Mech. Eng. – volume: 58 start-page: 12041 year: 2019 end-page: 12053 ident: bib0040 article-title: Process fault prognosis using hidden markov model-bayesian networks hybrid model publication-title: Ind. Eng. Chem. Res. – volume: 65 start-page: 992 year: 2019 end-page: 1005 ident: bib0105 article-title: A nonlinear support vector machine-based feature selection approach for fault detection and diagnosis: application to the Tennessee Eastman process publication-title: Aiche J. – volume: 62 start-page: 3757 year: 2015 end-page: 3767 ident: bib0045 article-title: A survey of fault diagnosis and fault-tolerant techniques-part I: fault diagnosis with model-based and signal-based approaches publication-title: IEEE Trans. Ind. Electron. – volume: 19 start-page: 1 year: 2019 end-page: 17 ident: bib0115 article-title: Fault detection and diagnosis using combined autoencoder and long short-term memory network publication-title: Sensors (Switzerland) – start-page: 582 year: 2000 end-page: 588 ident: bib0120 article-title: Support vector method for novelty detection publication-title: Adv. Neural Inf. Process. Syst. – volume: 86 start-page: 154 year: 2019 end-page: 164 ident: bib0030 article-title: Data-driven approach augmented in simulation for robust fault prognosis publication-title: Eng. Appl. Artif. Intell. – year: 2017 ident: bib0070 article-title: State of the art and taxonomy of prognostics approaches, trends of prognostics applications and open issues towards maturity at different technology readiness levels publication-title: Mech. Syst. Signal Process. – volume: 9 start-page: 1735 year: 1997 end-page: 1780 ident: bib0060 article-title: Long short-term memory publication-title: Neural Comput. – start-page: 537 year: 2019 end-page: 541 ident: bib0165 article-title: A fault prediction of equipment based on CNN-LSTM network publication-title: Proceedings - IEEE International Conference on Energy Internet – volume: 20 year: 2020 ident: bib0130 article-title: A deep adversarial approach based on multi-sensor fusion for semi-supervised remaining useful life prognostics publication-title: Sensors (Switzerland) – start-page: 274 year: 2018 end-page: 278 ident: bib0145 article-title: An End-to-End model based on CNN-LSTM for industrial fault diagnosis and prognosis publication-title: Proceedings of 2018 6th IEEE International Conference on Network Infrastructure and Digital Content, IC-NIDC 2018 – volume: 335 start-page: 327 year: 2019 end-page: 335 ident: bib0055 article-title: A survey on Deep Learning based bearing fault diagnosis publication-title: Neurocomputing – volume: 6 start-page: 12929 year: 2018 end-page: 12939 ident: bib0155 article-title: Sequential fault diagnosis based on LSTM neural network publication-title: IEEE Access – volume: 115 year: 2020 ident: bib0140 article-title: An ensemble framework based on convolutional bi-directional LSTM with multiple time windows for remaining useful life estimation publication-title: Comput. Ind. – start-page: 2553 year: 2004 end-page: 2572 ident: bib0020 article-title: Automatic diagnostics and prognostics of energy conversion processes via knowledge-based systems publication-title: Energy – volume: 15 start-page: 987 year: 2019 end-page: 997 ident: bib0025 article-title: Machine health monitoring using adaptive kernel spectral clustering and deep long short-term memory recurrent neural networks publication-title: IEEE Trans. Industr. Inform. – volume: 29 start-page: 843 year: 1993 end-page: 849 ident: bib0125 article-title: Application of artificial neural networks in process fault diagnosis publication-title: Automatica – volume: 28 start-page: 2306 year: 2017 end-page: 2318 ident: bib0150 article-title: Multiobjective deep belief networks ensemble for remaining useful life estimation in prognostics publication-title: IEEE Trans. Neural Netw. Learn. Syst. – year: 2021 ident: bib0015 article-title: An analysis of process fault diagnosis methods from safety perspectives publication-title: Comput. Chem. Eng. – volume: 134 start-page: 106697 year: 2020 ident: bib0010 article-title: Fault detection and diagnosis in process system using artificial intelligence-based cognitive technique publication-title: Comput. Chem. Eng. – volume: 19 start-page: 1627 year: 2009 end-page: 1639 ident: bib0100 article-title: Fault detection and diagnosis in process data using one-class support vector machines publication-title: J. Process Control – volume: 8 start-page: 26933 year: 2020 end-page: 26940 ident: bib0080 article-title: A hybrid CNN-LSTM model for forecasting particulate matter (PM2.5) publication-title: IEEE Access – volume: 17 start-page: 245 year: 1993 end-page: 255 ident: bib0035 article-title: A plant-wide industrial problem process publication-title: Comput. Chem. Eng. – volume: 172 start-page: 1 year: 2018 end-page: 11 ident: bib0075 article-title: Remaining useful life estimation in prognostics using deep convolution neural networks publication-title: Reliab. Eng. Syst. Saf. – volume: 12 start-page: 881 year: 1988 end-page: 890 ident: bib0065 article-title: Artificial neural network models of knowledge representation in chemical engineering publication-title: Comput. Chem. Eng. – volume: 19 start-page: 1627 year: 2009 ident: 10.1016/j.psep.2021.08.022_bib0100 article-title: Fault detection and diagnosis in process data using one-class support vector machines publication-title: J. Process Control doi: 10.1016/j.jprocont.2009.07.011 – volume: 20 year: 2020 ident: 10.1016/j.psep.2021.08.022_bib0130 article-title: A deep adversarial approach based on multi-sensor fusion for semi-supervised remaining useful life prognostics publication-title: Sensors (Switzerland) – volume: 58 start-page: 12041 year: 2019 ident: 10.1016/j.psep.2021.08.022_bib0040 article-title: Process fault prognosis using hidden markov model-bayesian networks hybrid model publication-title: Ind. Eng. Chem. Res. doi: 10.1021/acs.iecr.9b00524 – start-page: 582 year: 2000 ident: 10.1016/j.psep.2021.08.022_bib0120 article-title: Support vector method for novelty detection publication-title: Adv. Neural Inf. Process. Syst. – start-page: 489 year: 2019 ident: 10.1016/j.psep.2021.08.022_bib0090 article-title: Attention based Echo state Network: A novel approach for fault prognosis – volume: 9 start-page: 1735 year: 1997 ident: 10.1016/j.psep.2021.08.022_bib0060 article-title: Long short-term memory publication-title: Neural Comput. doi: 10.1162/neco.1997.9.8.1735 – volume: 62 start-page: 3757 year: 2015 ident: 10.1016/j.psep.2021.08.022_bib0045 article-title: A survey of fault diagnosis and fault-tolerant techniques-part I: fault diagnosis with model-based and signal-based approaches publication-title: IEEE Trans. Ind. Electron. doi: 10.1109/TIE.2015.2417501 – volume: 19 start-page: 1 year: 2019 ident: 10.1016/j.psep.2021.08.022_bib0115 article-title: Fault detection and diagnosis using combined autoencoder and long short-term memory network publication-title: Sensors (Switzerland) doi: 10.3390/s19214612 – start-page: 274 year: 2018 ident: 10.1016/j.psep.2021.08.022_bib0145 article-title: An End-to-End model based on CNN-LSTM for industrial fault diagnosis and prognosis publication-title: Proceedings of 2018 6th IEEE International Conference on Network Infrastructure and Digital Content, IC-NIDC 2018 – volume: 172 start-page: 1 year: 2018 ident: 10.1016/j.psep.2021.08.022_bib0075 article-title: Remaining useful life estimation in prognostics using deep convolution neural networks publication-title: Reliab. Eng. Syst. Saf. doi: 10.1016/j.ress.2017.11.021 – volume: 8 start-page: 26933 year: 2020 ident: 10.1016/j.psep.2021.08.022_bib0080 article-title: A hybrid CNN-LSTM model for forecasting particulate matter (PM2.5) publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2971348 – start-page: 4277 year: 2012 ident: 10.1016/j.psep.2021.08.022_bib0005 article-title: Applying convolutional neural networks concepts to hybrid NN-HMM model for speech recognition publication-title: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings – volume: 134 start-page: 106697 year: 2020 ident: 10.1016/j.psep.2021.08.022_bib0010 article-title: Fault detection and diagnosis in process system using artificial intelligence-based cognitive technique publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2019.106697 – volume: 96 start-page: 457 year: 2020 ident: 10.1016/j.psep.2021.08.022_bib0135 article-title: A novel deep learning based fault diagnosis approach for chemical process with extended deep belief network publication-title: ISA Trans. doi: 10.1016/j.isatra.2019.07.001 – volume: 335 start-page: 327 year: 2019 ident: 10.1016/j.psep.2021.08.022_bib0055 article-title: A survey on Deep Learning based bearing fault diagnosis publication-title: Neurocomputing doi: 10.1016/j.neucom.2018.06.078 – volume: 65 start-page: 992 year: 2019 ident: 10.1016/j.psep.2021.08.022_bib0105 article-title: A nonlinear support vector machine-based feature selection approach for fault detection and diagnosis: application to the Tennessee Eastman process publication-title: Aiche J. doi: 10.1002/aic.16497 – start-page: 1 year: 2019 ident: 10.1016/j.psep.2021.08.022_bib0085 article-title: A novel bearing health indicator construction method based on ensemble stacked autoencoder – volume: 214 start-page: 107646 year: 2021 ident: 10.1016/j.psep.2021.08.022_bib0095 article-title: Fault prediction of bearings based on LSTM and statistical process analysis publication-title: Reliab. Eng. Syst. Saf. doi: 10.1016/j.ress.2021.107646 – volume: 86 start-page: 154 year: 2019 ident: 10.1016/j.psep.2021.08.022_bib0030 article-title: Data-driven approach augmented in simulation for robust fault prognosis publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2019.09.002 – volume: 12 start-page: 881 year: 1988 ident: 10.1016/j.psep.2021.08.022_bib0065 article-title: Artificial neural network models of knowledge representation in chemical engineering publication-title: Comput. Chem. Eng. doi: 10.1016/0098-1354(88)87015-7 – start-page: 2553 year: 2004 ident: 10.1016/j.psep.2021.08.022_bib0020 article-title: Automatic diagnostics and prognostics of energy conversion processes via knowledge-based systems – volume: 29 start-page: 843 year: 1993 ident: 10.1016/j.psep.2021.08.022_bib0125 article-title: Application of artificial neural networks in process fault diagnosis publication-title: Automatica doi: 10.1016/0005-1098(93)90090-G – volume: 28 start-page: 2306 year: 2017 ident: 10.1016/j.psep.2021.08.022_bib0150 article-title: Multiobjective deep belief networks ensemble for remaining useful life estimation in prognostics publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2016.2582798 – volume: 6 start-page: 12929 year: 2018 ident: 10.1016/j.psep.2021.08.022_bib0155 article-title: Sequential fault diagnosis based on LSTM neural network publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2794765 – volume: 47 start-page: 312 year: 2019 ident: 10.1016/j.psep.2021.08.022_bib0160 article-title: Speech emotion recognition using deep 1D & 2D CNN LSTM networks publication-title: Biomed. Signal Process. Control doi: 10.1016/j.bspc.2018.08.035 – year: 2021 ident: 10.1016/j.psep.2021.08.022_bib0015 article-title: An analysis of process fault diagnosis methods from safety perspectives publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2020.107197 – volume: 92 start-page: 161 year: 2020 ident: 10.1016/j.psep.2021.08.022_bib0050 article-title: An optimized long short-term memory network based fault diagnosis model for chemical processes publication-title: J. Process Control doi: 10.1016/j.jprocont.2020.06.005 – volume: 17 start-page: 245 issue: 3 year: 1993 ident: 10.1016/j.psep.2021.08.022_bib0035 article-title: A plant-wide industrial problem process publication-title: Comput. Chem. Eng. doi: 10.1016/0098-1354(93)80018-I – year: 2017 ident: 10.1016/j.psep.2021.08.022_bib0070 article-title: State of the art and taxonomy of prognostics approaches, trends of prognostics applications and open issues towards maturity at different technology readiness levels publication-title: Mech. Syst. Signal Process. doi: 10.1016/j.ymssp.2017.01.050 – volume: 64 start-page: 443 year: 2018 ident: 10.1016/j.psep.2021.08.022_bib0110 article-title: An improved bearing fault diagnosis method using one-dimensional CNN and LSTM publication-title: J. Mech. Eng. – volume: 115 year: 2020 ident: 10.1016/j.psep.2021.08.022_bib0140 article-title: An ensemble framework based on convolutional bi-directional LSTM with multiple time windows for remaining useful life estimation publication-title: Comput. Ind. doi: 10.1016/j.compind.2019.103182 – start-page: 537 year: 2019 ident: 10.1016/j.psep.2021.08.022_bib0165 article-title: A fault prediction of equipment based on CNN-LSTM network – volume: 15 start-page: 987 year: 2019 ident: 10.1016/j.psep.2021.08.022_bib0025 article-title: Machine health monitoring using adaptive kernel spectral clustering and deep long short-term memory recurrent neural networks publication-title: IEEE Trans. Industr. Inform. doi: 10.1109/TII.2018.2866549 |
| SSID | ssj0001271 |
| Score | 2.5968819 |
| Snippet | Early fault detection and fault prognosis are crucial functions to ensure safe process operations. Fault prognosis can detect and isolate early developing... |
| SourceID | proquest crossref elsevier |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 467 |
| SubjectTerms | Artificial neural networks Data-driven model Deep learning Fault detection Fault diagnosis Fault prognosis Life prediction Long short-term memory LSTM model Mathematical models Multivariate analysis Neural networks Process safety Prognosis Recurrent neural networks System effectiveness Time dependence Useful life |
| Title | A deep learning model for process fault prognosis |
| URI | https://dx.doi.org/10.1016/j.psep.2021.08.022 https://www.proquest.com/docview/2600349303 |
| Volume | 154 |
| WOSCitedRecordID | wos000704392700013&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/eLvHCXMwtV3Nb9MwFLfKxgEOCAbTNgbyAXGpjIjjxMkxQq34qAoSHerNshOHtWqzsGTVducP5zl20myICg5cosjNR_Xezy_Pz-_9HkKv3uYy57GUBCZTRJhUOYkplyRT3PNkrmnaEJh-m_DpNJrP4y-Dwc-2Fmaz4kURXV_H5X9VNYyBsk3p7D-ou3soDMA5KB2OoHY4_pXik2Gmddm2g_hue9002YSlLQoY5vJqVTeZWcVFtaj6_qmrGxhWIMDaUjP1SuFM1Zbldejt3ieGu-BcmubMJgzf6Ewutd5scffJjY_lopJdSkdyvtYuKr1arDuMrsHmNFHtrzeu8MoFJajXpbe5SFlbLdMaqG2Kkg09chJE1O7HaGt4OWPEsAnesswB69lWZvt2_Gbzbfhh-aastCEgpZaT1VY73ybYnn4W47PJRMxG89nr8gcxvcfMHr1rxHIP7VMexGDe95MPo_nH7ovu0Wbh3v1vV3xl8wTvvvZPDs6dT33jv8weo0du4YETC5gnaKCLA_SwR0d5gA5HfVVjJ9XqKfISbDCFW0zhBlMYMIUdpnCDKdxh6hk6G49m794T12uDpD6NapKFmfQz6sdSy9BMJxUxBq4jzNXG5YVlKMtY7mWhUhpcfo_lcBmVPlV5lqrYP0R7xUWhjxAOaKpCX4WcppSlaRj5IawKlGHm4x7X6THyWgGJ1BHRm34oK9FmHC6FEaowQhWmSSqlx2jY3VNaGpadVwet3IVzJK2DKAAzO-87bZUk3IyuhOng4LMYXL2T3T8_Rw-2U-EU7dWXV_oFup9u6kV1-dJh6hdfEZxB |
| 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+deep+learning+model+for+process+fault+prognosis&rft.jtitle=Process+safety+and+environmental+protection&rft.au=Arunthavanathan%2C+Rajeevan&rft.au=Khan%2C+Faisal&rft.au=Ahmed%2C+Salim&rft.au=Imtiaz%2C+Syed&rft.date=2021-10-01&rft.pub=Elsevier+Science+Ltd&rft.issn=0957-5820&rft.eissn=1744-3598&rft.volume=154&rft.spage=467&rft_id=info:doi/10.1016%2Fj.psep.2021.08.022&rft.externalDBID=NO_FULL_TEXT |
| 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 |