{"id":"https://openalex.org/W4210502853","doi":"https://doi.org/10.1109/safeprocess52771.2021.9693590","title":"A Novel Fault Diagnosis Method based on Contrastive Learning with Multi-views of Data","display_name":"A Novel Fault Diagnosis Method based on Contrastive Learning with Multi-views of Data","publication_year":2021,"publication_date":"2021-12-17","ids":{"openalex":"https://openalex.org/W4210502853","doi":"https://doi.org/10.1109/safeprocess52771.2021.9693590"},"language":"en","primary_location":{"id":"doi:10.1109/safeprocess52771.2021.9693590","is_oa":false,"landing_page_url":"https://doi.org/10.1109/safeprocess52771.2021.9693590","pdf_url":null,"source":{"id":"https://openalex.org/S4363605570","display_name":"2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes (SAFEPROCESS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes (SAFEPROCESS)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5100749127","display_name":"Yu Yao","orcid":"https://orcid.org/0000-0003-3995-5325"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Yao","raw_affiliation_strings":["College of Information Science and Engineering, Northeastern University, Shenyang, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering, Northeastern University, Shenyang, P. R. China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010565829","display_name":"Jian Feng","orcid":"https://orcid.org/0000-0001-6813-6754"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian Feng","raw_affiliation_strings":["College of Information Science and Engineering, Northeastern University, Shenyang, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering, Northeastern University, Shenyang, P. R. China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087894632","display_name":"Keqin Li","orcid":"https://orcid.org/0000-0001-5224-4048"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Keqin Li","raw_affiliation_strings":["College of Information Science and Engineering, Northeastern University, Shenyang, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering, Northeastern University, Shenyang, P. R. China","institution_ids":["https://openalex.org/I9224756"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I9224756"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.30366641,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"12","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9970999956130981,"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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9970999956130981,"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/T12169","display_name":"Non-Destructive Testing Techniques","score":0.9902999997138977,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9861999750137329,"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":0.7961771488189697},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7650015354156494},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6852558851242065},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.6460496783256531},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6089406609535217},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.5913918614387512},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5336642265319824},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.49882030487060547},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.48332881927490234},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4276973605155945},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.41084468364715576},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.39849284291267395}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7961771488189697},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7650015354156494},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6852558851242065},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.6460496783256531},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6089406609535217},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.5913918614387512},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5336642265319824},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.49882030487060547},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48332881927490234},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4276973605155945},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.41084468364715576},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39849284291267395},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","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},{"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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/safeprocess52771.2021.9693590","is_oa":false,"landing_page_url":"https://doi.org/10.1109/safeprocess52771.2021.9693590","pdf_url":null,"source":{"id":"https://openalex.org/S4363605570","display_name":"2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes (SAFEPROCESS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes (SAFEPROCESS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.6899999976158142}],"awards":[],"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":27,"referenced_works":["https://openalex.org/W243674440","https://openalex.org/W2101234009","https://openalex.org/W2158958729","https://openalex.org/W2194775991","https://openalex.org/W2258884143","https://openalex.org/W2317595875","https://openalex.org/W2463319845","https://openalex.org/W2768753204","https://openalex.org/W2808496542","https://openalex.org/W2809350318","https://openalex.org/W2898760173","https://openalex.org/W2908510526","https://openalex.org/W2976915729","https://openalex.org/W2979655715","https://openalex.org/W2990273315","https://openalex.org/W3005680577","https://openalex.org/W3014057330","https://openalex.org/W3016160231","https://openalex.org/W3022996714","https://openalex.org/W3037914312","https://openalex.org/W3085543912","https://openalex.org/W3089512998","https://openalex.org/W3090682168","https://openalex.org/W3131903265","https://openalex.org/W6675354045","https://openalex.org/W6757817989","https://openalex.org/W6774314701"],"related_works":["https://openalex.org/W2983142544","https://openalex.org/W2891059443","https://openalex.org/W4281663961","https://openalex.org/W3208888551","https://openalex.org/W4313561566","https://openalex.org/W3208386644","https://openalex.org/W4220682630","https://openalex.org/W3181622257","https://openalex.org/W4389832810","https://openalex.org/W3133533225"],"abstract_inverted_index":{"Deep":[0],"learning-based":[1],"fault":[2,14,65,135],"diagnosis":[3,15,140],"becomes":[4],"an":[5],"emerging":[6],"area":[7],"of":[8,27,40,55,79,98,124,147,169,182,186],"interests.":[9],"In":[10],"particular,":[11],"in":[12,68,184],"many":[13],"cases,":[16],"unsupervised":[17],"deep":[18,46,57,72],"learning":[19,128,132],"are":[20,149],"widely":[21],"studied":[22],"to":[23,43,108,152,165],"address":[24,116],"the":[25,56,71,117,122,167],"problem":[26],"large":[28],"demands":[29],"on":[30],"tagged":[31,188],"data.":[32,189],"A":[33,137],"common":[34],"way":[35],"is":[36,112,142],"using":[37],"a":[38,45,60,89,125],"amount":[39],"unlabeled":[41],"data":[42,99,148,154,161],"train":[44],"model,":[47],"like":[48],"auto":[49],"encoder,":[50],"and":[51,105],"then":[52],"treating":[53],"parts":[54],"model":[58,73],"as":[59,93],"feature":[61],"extractor":[62],"for":[63,103,134],"downstream":[64],"diagnosis.":[66,136],"However,":[67],"these":[69],"works,":[70],"usually":[74],"takes":[75],"just":[76],"one":[77,111],"view":[78],"data,":[80,85],"i.e.,":[81],"time-":[82],"or":[83],"frequency-domain":[84],"into":[86],"consideration":[87],"once":[88],"time.":[90],"As":[91],"far":[92],"we":[94],"know,":[95],"both":[96],"views":[97,146],"contains":[100],"discriminative":[101],"information":[102],"diagnosis,":[104],"it":[106],"difficult":[107],"tell":[109],"which":[110],"better.":[113],"To":[114],"help":[115],"issue,":[118],"this":[119],"paper":[120],"explores":[121],"application":[123],"relatively":[126],"new":[127],"mechanism":[129],"called":[130],"contrastive":[131],"(CL)":[133],"CL-based":[138,176],"hybrid":[139,177],"method":[141,178],"proposed,":[143],"where":[144],"different":[145],"considered":[150],"simultaneously":[151],"extract":[153],"representations.":[155],"The":[156,172],"case":[157],"western":[158],"reserve":[159],"university":[160],"(CWRU)":[162],"were":[163],"used":[164],"evaluate":[166],"performance":[168],"proposed":[170],"method.":[171],"results":[173],"show":[174],"that":[175],"produced":[179],"high":[180],"accuracy":[181],"98.44%":[183],"circumstance":[185],"insufficient":[187]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
