{"id":"https://openalex.org/W4385502132","doi":"https://doi.org/10.1080/01969722.2023.2240651","title":"Multi-Instance Attention Network for Anomaly Detection from Multivariate Time Series","display_name":"Multi-Instance Attention Network for Anomaly Detection from Multivariate Time Series","publication_year":2023,"publication_date":"2023-08-03","ids":{"openalex":"https://openalex.org/W4385502132","doi":"https://doi.org/10.1080/01969722.2023.2240651"},"language":"en","primary_location":{"id":"doi:10.1080/01969722.2023.2240651","is_oa":false,"landing_page_url":"https://doi.org/10.1080/01969722.2023.2240651","pdf_url":null,"source":{"id":"https://openalex.org/S117436046","display_name":"Cybernetics & Systems","issn_l":"0196-9722","issn":["0196-9722","1087-6553"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Cybernetics and Systems","raw_type":"journal-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/A5022491669","display_name":"Gye-Bong Jang","orcid":"https://orcid.org/0000-0003-0717-1331"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Gye-Bong Jang","raw_affiliation_strings":["Department of Computer Science, Yonsei University, Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Yonsei University, Seoul, South Korea","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102775641","display_name":"Sung-Bae Cho","orcid":"https://orcid.org/0000-0002-0185-1769"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sung-Bae Cho","raw_affiliation_strings":["Department of Computer Science, Yonsei University, Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Yonsei University, Seoul, South Korea","institution_ids":["https://openalex.org/I193775966"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5022491669"],"corresponding_institution_ids":["https://openalex.org/I193775966"],"apc_list":null,"apc_paid":null,"fwci":1.269,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.82907341,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"55","issue":"6","first_page":"1417","last_page":"1440"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9965999722480774,"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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.987500011920929,"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":0.7329207062721252},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7134740948677063},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7042852640151978},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.6495414972305298},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.514965832233429},{"id":"https://openalex.org/keywords/soft-sensor","display_name":"Soft sensor","score":0.5122227072715759},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4720735549926758},{"id":"https://openalex.org/keywords/wireless-sensor-network","display_name":"Wireless sensor network","score":0.44684475660324097},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4330519139766693},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.42698273062705994},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.382857084274292},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.30978280305862427}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7329207062721252},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7134740948677063},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7042852640151978},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6495414972305298},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.514965832233429},{"id":"https://openalex.org/C115575686","wikidata":"https://www.wikidata.org/wiki/Q18822403","display_name":"Soft sensor","level":3,"score":0.5122227072715759},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4720735549926758},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.44684475660324097},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4330519139766693},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42698273062705994},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.382857084274292},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30978280305862427},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"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/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/01969722.2023.2240651","is_oa":false,"landing_page_url":"https://doi.org/10.1080/01969722.2023.2240651","pdf_url":null,"source":{"id":"https://openalex.org/S117436046","display_name":"Cybernetics & Systems","issn_l":"0196-9722","issn":["0196-9722","1087-6553"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Cybernetics and Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1984672166","https://openalex.org/W1995430360","https://openalex.org/W2019014808","https://openalex.org/W2317595875","https://openalex.org/W2461729787","https://openalex.org/W2606068338","https://openalex.org/W2612690371","https://openalex.org/W2743189459","https://openalex.org/W2744067593","https://openalex.org/W2783074568","https://openalex.org/W2785435926","https://openalex.org/W2786827964","https://openalex.org/W2792286928","https://openalex.org/W2796013264","https://openalex.org/W2808496542","https://openalex.org/W2901773810","https://openalex.org/W2902455138","https://openalex.org/W2911200746","https://openalex.org/W2913409415","https://openalex.org/W2917169831","https://openalex.org/W2942305957","https://openalex.org/W2948571212","https://openalex.org/W2962736999","https://openalex.org/W2963166639","https://openalex.org/W2963197901","https://openalex.org/W2989818023","https://openalex.org/W3106543020","https://openalex.org/W4235764643","https://openalex.org/W4292963524","https://openalex.org/W4297814361"],"related_works":["https://openalex.org/W2076520961","https://openalex.org/W2002718662","https://openalex.org/W2546942002","https://openalex.org/W2970216048","https://openalex.org/W2382607599","https://openalex.org/W4289533840","https://openalex.org/W3165446392","https://openalex.org/W190094394","https://openalex.org/W2896333468","https://openalex.org/W4226003894"],"abstract_inverted_index":{"Anomaly":[0],"detection":[1,247],"and":[2,41,77,96,116,122,156,160,207,219,254],"state":[3,142,162],"prediction":[4,58,103],"research":[5],"using":[6],"multivariate":[7],"data":[8,57,88,102,199,214],"is":[9,146,167],"being":[10],"actively":[11],"conducted":[12,188],"in":[13],"various":[14],"industrial":[15,22,81,263],"fields.":[16],"However,":[17],"since":[18],"most":[19],"dynamically":[20],"operating":[21,26,236],"machines":[23],"perform":[24],"different":[25,30],"conditions,":[27,34],"they":[28],"contain":[29],"types":[31,137,206],"of":[32,64,71,80,119,138,143,164,172,181,229],"abnormal":[33,75],"making":[35],"it":[36],"difficult":[37],"to":[38,91,105,112,123,177,198,250,258,261],"detect":[39],"anomalies":[40,221],"predict":[42],"the":[43,61,114,125,128,141,144,149,152,157,165,170,179,182,191,230,235,251],"remaining":[44,62],"life.":[45],"This":[46],"white":[47],"paper":[48],"proposes":[49],"a":[50,86,100],"condition":[51],"diagnosis":[52],"model":[53,90,104,134,153,210,243],"based":[54],"on":[55,190],"multi-sensor":[56,87],"for":[59],"estimating":[60],"lifespan":[63],"equipment":[65,201],"while":[66],"solving":[67],"two":[68],"complex":[69],"problems":[70],"detecting":[72],"four":[73],"typical":[74],"conditions":[76],"sensor":[78,101,194,213,231],"omissions":[79],"machines.":[82],"First,":[83],"we":[84,98,130],"use":[85,99],"generation":[89],"learn":[92,106],"relationships":[93],"between":[94,127,151],"sensors,":[95,129],"second,":[97],"sensor-specific":[107],"feature":[108],"information.":[109,174],"In":[110,175],"order":[111,176],"extract":[113],"temporal":[115],"spatial":[117],"characteristics":[118],"sensing":[120],"information":[121,163],"derive":[124],"relationship":[126],"propose":[131],"an":[132],"attention":[133],"with":[135,202,215,222],"three":[136],"cases.":[139],"Finally,":[140],"device":[145,166],"diagnosed":[147],"through":[148,169],"difference":[150],"predicted":[154,168],"value":[155],"actual":[158],"value,":[159],"future":[161],"accumulation":[171],"error":[173],"prove":[178],"robustness":[180],"proposed":[183,242],"model,":[184,253],"extensive":[185],"experiments":[186],"were":[187],"focusing":[189],"case":[192],"where":[193],"omission":[195],"occurred":[196],"due":[197],"from":[200],"more":[203],"than":[204],"4":[205],"conditions.":[208],"Our":[209],"produces":[211],"missing":[212,233],"about":[216,223],"92%":[217],"accuracy":[218,248],"detects":[220],"88%":[224],"accuracy,":[225],"even":[226],"if":[227],"parts":[228],"are":[232],"or":[234],"environments":[237],"have":[238],"been":[239,256],"changed.":[240],"The":[241],"has":[244,255],"improved":[245],"anomaly":[246],"compared":[249],"comparative":[252],"proven":[257],"be":[259],"applicable":[260],"real":[262],"problems.":[264]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6},{"year":2023,"cited_by_count":1}],"updated_date":"2026-06-15T08:34:33.830935","created_date":"2025-10-10T00:00:00"}