{"id":"https://openalex.org/W3183862839","doi":"https://doi.org/10.23919/acc50511.2021.9482728","title":"MS-TCN: A Multiscale Temporal Convolutional Network for Fault Diagnosis in Industrial Processes","display_name":"MS-TCN: A Multiscale Temporal Convolutional Network for Fault Diagnosis in Industrial Processes","publication_year":2021,"publication_date":"2021-05-25","ids":{"openalex":"https://openalex.org/W3183862839","doi":"https://doi.org/10.23919/acc50511.2021.9482728","mag":"3183862839"},"language":"en","primary_location":{"id":"doi:10.23919/acc50511.2021.9482728","is_oa":false,"landing_page_url":"https://doi.org/10.23919/acc50511.2021.9482728","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 American Control Conference (ACC)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101869832","display_name":"Jiyang Zhang","orcid":"https://orcid.org/0000-0002-8788-2119"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiyang Zhang","raw_affiliation_strings":["School of Automation Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, Sichuan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, Sichuan, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039091156","display_name":"Yuxuan Wang","orcid":"https://orcid.org/0000-0001-9153-3512"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuxuan Wang","raw_affiliation_strings":["SHENYUAN Honors College, Beihang University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SHENYUAN Honors College, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103129509","display_name":"Jianxiong Tang","orcid":"https://orcid.org/0000-0001-6369-8774"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianxiong Tang","raw_affiliation_strings":["School of Automation Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, Sichuan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, Sichuan, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027642367","display_name":"Jianxiao Zou","orcid":"https://orcid.org/0000-0002-8676-8322"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianxiao Zou","raw_affiliation_strings":["School of Automation Engineering, Intelligent Terminal Key Laboratory of Sichuan Province, Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China (UESTC), China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation Engineering, Intelligent Terminal Key Laboratory of Sichuan Province, Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China (UESTC), China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019841300","display_name":"Shicai Fan","orcid":"https://orcid.org/0000-0003-2548-3689"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shicai Fan","raw_affiliation_strings":["School of Automation Engineering, Intelligent Terminal Key Laboratory of Sichuan Province, Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China (UESTC), China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation Engineering, Intelligent Terminal Key Laboratory of Sichuan Province, Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China (UESTC), China","institution_ids":["https://openalex.org/I150229711"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.2172,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.78716024,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1601","last_page":"1606"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12282","display_name":"Mineral Processing and Grinding","score":0.9840999841690063,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9728000164031982,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7392843961715698},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.6273309588432312},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6129322648048401},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5475337505340576},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.5338823199272156},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5239617824554443},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5231091976165771},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.5213631391525269},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.48209336400032043},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.4668135643005371},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.44036513566970825},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4333961606025696},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4229254126548767},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41016829013824463},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.059819042682647705}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7392843961715698},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.6273309588432312},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6129322648048401},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5475337505340576},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.5338823199272156},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5239617824554443},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5231091976165771},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.5213631391525269},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.48209336400032043},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.4668135643005371},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.44036513566970825},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4333961606025696},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4229254126548767},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41016829013824463},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.059819042682647705},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C165205528","wikidata":"https://www.wikidata.org/wiki/Q83371","display_name":"Seismology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/acc50511.2021.9482728","is_oa":false,"landing_page_url":"https://doi.org/10.23919/acc50511.2021.9482728","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 American Control Conference (ACC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5899999737739563,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[{"id":"https://openalex.org/G8909416960","display_name":null,"funder_award_id":"61872063,61973054","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W581956982","https://openalex.org/W1522301498","https://openalex.org/W1689711448","https://openalex.org/W1904365287","https://openalex.org/W2004186751","https://openalex.org/W2008616192","https://openalex.org/W2048407872","https://openalex.org/W2076063813","https://openalex.org/W2084327162","https://openalex.org/W2093598546","https://openalex.org/W2105693855","https://openalex.org/W2156387975","https://openalex.org/W2163922914","https://openalex.org/W2166042538","https://openalex.org/W2187089797","https://openalex.org/W2194775991","https://openalex.org/W2284050935","https://openalex.org/W2341973567","https://openalex.org/W2397271794","https://openalex.org/W2461729787","https://openalex.org/W2550143307","https://openalex.org/W2736225434","https://openalex.org/W2789515102","https://openalex.org/W2792764867","https://openalex.org/W2794081072","https://openalex.org/W2808496542","https://openalex.org/W2921973374","https://openalex.org/W2937484199","https://openalex.org/W2961333734","https://openalex.org/W2963685250","https://openalex.org/W2963840672","https://openalex.org/W2964121744","https://openalex.org/W2980928168","https://openalex.org/W2995351248","https://openalex.org/W3011246678","https://openalex.org/W4249625715","https://openalex.org/W6616837769","https://openalex.org/W6682889407","https://openalex.org/W6695676441"],"related_works":["https://openalex.org/W4299822940","https://openalex.org/W2279398222","https://openalex.org/W2773120646","https://openalex.org/W3011074480","https://openalex.org/W2059299633","https://openalex.org/W2738221750","https://openalex.org/W2760085659","https://openalex.org/W2732542196","https://openalex.org/W2942471066","https://openalex.org/W3156786002"],"abstract_inverted_index":{"Fault":[0],"diagnosis":[1,31,110],"is":[2],"an":[3],"important":[4],"way":[5],"to":[6,70,83,119],"ensure":[7],"the":[8,16,48,57,63,72,89,97,114],"operation":[9],"security":[10],"in":[11,42],"complex":[12],"industrial":[13,24],"processes.":[14],"Considering":[15],"inherent":[17],"multiscale":[18,35,58],"characteristics":[19],"and":[20],"time":[21],"dependency":[22],"about":[23],"process":[25,100],"monitoring":[26],"data,":[27],"a":[28,76],"novel":[29],"fault":[30,109],"method":[32,105],"based":[33],"on":[34,96],"temporal":[36,77,86],"convolutional":[37,78],"network":[38,79],"(MS-TCN)":[39],"was":[40,67,80],"proposed":[41,104],"this":[43],"paper.":[44],"Firstly,":[45],"different":[46],"from":[47,88],"widely":[49],"used":[50],"time-domain":[51],"features":[52],"with":[53,62],"one":[54],"single":[55],"scale,":[56],"time-frequency":[59],"information":[60],"extracted":[61],"discrete":[64],"wavelet":[65],"transform":[66],"also":[68],"introduced":[69],"represent":[71],"raw":[73],"data.":[74,92],"And":[75],"then":[81],"combined":[82],"capture":[84],"longer-term":[85],"feature":[87],"sequential":[90],"processing":[91],"The":[93],"experimental":[94],"results":[95],"Tennessee":[98],"Eastman":[99],"indicated":[101],"that,":[102],"our":[103],"outperformed":[106],"these":[107],"state-of-the-art":[108],"methods,":[111],"especially":[112],"for":[113],"3":[115],"incipient":[116],"faults":[117],"hard":[118],"classify.":[120]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-03T08:13:44.112507","created_date":"2025-10-10T00:00:00"}
