{"id":"https://openalex.org/W3091938983","doi":"https://doi.org/10.1109/tase.2020.3026065","title":"Process Monitoring and Fault Prediction in Multivariate Time Series Using Bag-of-Words","display_name":"Process Monitoring and Fault Prediction in Multivariate Time Series Using Bag-of-Words","publication_year":2020,"publication_date":"2020-10-07","ids":{"openalex":"https://openalex.org/W3091938983","doi":"https://doi.org/10.1109/tase.2020.3026065","mag":"3091938983"},"language":"en","primary_location":{"id":"doi:10.1109/tase.2020.3026065","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tase.2020.3026065","pdf_url":null,"source":{"id":"https://openalex.org/S34881539","display_name":"IEEE Transactions on Automation Science and Engineering","issn_l":"1545-5955","issn":["1545-5955","1558-3783"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Automation Science and Engineering","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/A5010845074","display_name":"Shenghan Guo","orcid":"https://orcid.org/0000-0002-2159-6128"},"institutions":[{"id":"https://openalex.org/I102322142","display_name":"Rutgers, The State University of New Jersey","ror":"https://ror.org/05vt9qd57","country_code":"US","type":"education","lineage":["https://openalex.org/I102322142"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Shenghan Guo","raw_affiliation_strings":["Department of Industrial and Systems Engineering, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA"],"affiliations":[{"raw_affiliation_string":"Department of Industrial and Systems Engineering, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA","institution_ids":["https://openalex.org/I102322142"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054810335","display_name":"Weihong Guo","orcid":"https://orcid.org/0000-0001-8433-6326"},"institutions":[{"id":"https://openalex.org/I102322142","display_name":"Rutgers, The State University of New Jersey","ror":"https://ror.org/05vt9qd57","country_code":"US","type":"education","lineage":["https://openalex.org/I102322142"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Weihong Guo","raw_affiliation_strings":["Department of Industrial and Systems Engineering, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA"],"affiliations":[{"raw_affiliation_string":"Department of Industrial and Systems Engineering, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA","institution_ids":["https://openalex.org/I102322142"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5010845074"],"corresponding_institution_ids":["https://openalex.org/I102322142"],"apc_list":null,"apc_paid":null,"fwci":1.3719,"has_fulltext":false,"cited_by_count":22,"citation_normalized_percentile":{"value":0.81805101,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"19","issue":"1","first_page":"230","last_page":"242"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9872999787330627,"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9620000123977661,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5895527601242065},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5796329975128174},{"id":"https://openalex.org/keywords/fault-detection-and-isolation","display_name":"Fault detection and isolation","score":0.5438601970672607},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5140398740768433},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5095632076263428},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5085486173629761},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5073637366294861},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.49820375442504883},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4776236414909363},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.4628230035305023},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.45400333404541016},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.3783729672431946}],"concepts":[{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5895527601242065},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5796329975128174},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.5438601970672607},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5140398740768433},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5095632076263428},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5085486173629761},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5073637366294861},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.49820375442504883},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4776236414909363},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.4628230035305023},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.45400333404541016},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3783729672431946},{"id":"https://openalex.org/C172707124","wikidata":"https://www.wikidata.org/wiki/Q423488","display_name":"Actuator","level":2,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tase.2020.3026065","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tase.2020.3026065","pdf_url":null,"source":{"id":"https://openalex.org/S34881539","display_name":"IEEE Transactions on Automation Science and Engineering","issn_l":"1545-5955","issn":["1545-5955","1558-3783"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Automation Science and Engineering","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":64,"referenced_works":["https://openalex.org/W1482770670","https://openalex.org/W1500750848","https://openalex.org/W1503994877","https://openalex.org/W1533693043","https://openalex.org/W1578440195","https://openalex.org/W1599611047","https://openalex.org/W1871385855","https://openalex.org/W1964601719","https://openalex.org/W1964905016","https://openalex.org/W1970023815","https://openalex.org/W1974561605","https://openalex.org/W1983483233","https://openalex.org/W1998886892","https://openalex.org/W2007442867","https://openalex.org/W2016944175","https://openalex.org/W2026909728","https://openalex.org/W2042591571","https://openalex.org/W2045931847","https://openalex.org/W2049677900","https://openalex.org/W2056503728","https://openalex.org/W2059304719","https://openalex.org/W2066796814","https://openalex.org/W2066921105","https://openalex.org/W2069810266","https://openalex.org/W2078474587","https://openalex.org/W2084500408","https://openalex.org/W2087610274","https://openalex.org/W2118813510","https://openalex.org/W2124192284","https://openalex.org/W2128061541","https://openalex.org/W2132311402","https://openalex.org/W2151570219","https://openalex.org/W2164274563","https://openalex.org/W2178055113","https://openalex.org/W2189716956","https://openalex.org/W2207008500","https://openalex.org/W2299893680","https://openalex.org/W2311865135","https://openalex.org/W2319521425","https://openalex.org/W2404533848","https://openalex.org/W2416278874","https://openalex.org/W2533339295","https://openalex.org/W2534987335","https://openalex.org/W2620027527","https://openalex.org/W2734575787","https://openalex.org/W2766117736","https://openalex.org/W2771712594","https://openalex.org/W2781844440","https://openalex.org/W2785441506","https://openalex.org/W2886736558","https://openalex.org/W2887791157","https://openalex.org/W2888009909","https://openalex.org/W2888477829","https://openalex.org/W2891582772","https://openalex.org/W2898265266","https://openalex.org/W2913543789","https://openalex.org/W2917825635","https://openalex.org/W2964328542","https://openalex.org/W3004804023","https://openalex.org/W4213251304","https://openalex.org/W4235958523","https://openalex.org/W4241604498","https://openalex.org/W6755276895","https://openalex.org/W6762056930"],"related_works":["https://openalex.org/W2090763504","https://openalex.org/W2406638334","https://openalex.org/W148178222","https://openalex.org/W4390961098","https://openalex.org/W2104657898","https://openalex.org/W1948992892","https://openalex.org/W1886884218","https://openalex.org/W1910826599","https://openalex.org/W2012353789","https://openalex.org/W1991765889"],"abstract_inverted_index":{"Multivariate":[0],"time":[1,120,230,252,259],"series":[2,260],"(MTS)":[3],"arise":[4],"due":[5],"to":[6,87,186,239,267,290,313,319],"multisensor":[7,79,192,309],"data":[8,13,164,310],"collection":[9],"in":[10,16,53,122,129,144,190,199,204,210,232,279,333],"manufacturing.":[11,200,334],"These":[12],"are":[14,51,116,311],"complex":[15],"the":[17,38,63,67,76,118,141,155,205,227,299,320,326],"sense":[18],"that":[19,317],"attributes":[20,72],"have":[21],"a":[22,96,123,191,196,241,244,274,330],"varying":[23],"scale,":[24],"volitivity,":[25],"continuity,":[26],"and":[27,30,111,135,169,217],"so":[28,325],"on,":[29],"interattribute":[31],"dependence":[32],"also":[33],"appears,":[34],"which":[35],"can":[36],"mask":[37],"inherent":[39],"information":[40,130],"about":[41],"system":[42,236],"health":[43],"status.":[44],"Conventional":[45],"machine":[46,179],"learning-based":[47,180],"process":[48],"monitoring":[49,189],"techniques":[50],"inefficient":[52],"predicting":[54],"faults":[55,237],"with":[56,91,148,235],"MTS\u2014their":[57],"detection":[58,208,256,300],"capability":[59,128],"heavily":[60],"relies":[61],"on":[62,103,161,258],"input":[64],"features,":[65],"yet":[66],"classification":[68,112],"power":[69,209,301],"of":[70,78,158,207,229,323],"MTS":[71,101,163,272,294],"is":[73,226],"weakened":[74],"by":[75],"complexity":[77],"data.":[80],"Effective":[81],"feature":[82,106,109],"extraction":[83],"is,":[84,262],"therefore,":[85,263],"necessary":[86],"facilitate":[88],"fault":[89,97,181],"prediction":[90,98],"MTS.":[92,149],"This":[93],"study":[94,153,281],"proposes":[95],"framework":[99,160,277,328],"for":[100,117,223],"based":[102,257],"bag-of-words":[104],"(BOW)":[105],"extraction,":[107],"statistical":[108],"selection,":[110],"analysis.":[113],"BOW":[114,283],"models":[115],"first":[119],"adopted":[121],"manufacturing":[124,146,168,308],"context.":[125],"Their":[126],"superior":[127],"preservation,":[131],"local":[132,303,315],"pattern":[133],"recognition,":[134],"temporal":[136],"effect":[137,231],"accommodation":[138],"has":[139,194,329],"overcome":[140],"major":[142],"limitations":[143],"current":[145],"practices":[147],"A":[150,220],"comprehensive":[151],"case":[152],"demonstrates":[154],"desirable":[156],"performance":[157],"this":[159,280],"two":[162],"sets":[165],"from":[166,271,293],"paper":[167],"automotive":[170],"manufacturing,":[171],"as":[172,174],"well":[173],"its":[175],"superiority":[176],"over":[177,305],"conventional":[178,212,245],"prediction.":[182],"<italic":[183],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[184],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">Note":[185],"Practitioners</i>":[187],"\u2014Process":[188],"environment":[193],"been":[195],"vital":[197],"interest":[198],"The":[201,276],"difficulty":[202],"lies":[203],"lack":[206],"many":[211],"techniques,":[213],"e.g.,":[214],"control":[215,246],"chart":[216,247],"logistic":[218],"regression.":[219],"critical":[221],"reason":[222],"such":[224,314],"failure":[225],"neglect":[228],"MTS\u2014patterns":[233],"associated":[234],"tend":[238],"stretch":[240],"period,":[242],"but":[243],"or":[248],"classifier":[249],"inspects":[250],"each":[251],"stamp":[253],"separately.":[254],"Fault":[255],"sequences":[261],"essential.":[264],"However,":[265],"how":[266],"effectively":[268],"extract":[269,291],"features":[270,292],"becomes":[273],"challenge.":[275],"proposed":[278,327],"adopts":[282],"models,":[284],"specifically":[285],"symbolic":[286],"aggregate":[287],"approximation":[288],"(SAX),":[289],"sequences,":[295],"thus":[296],"substantially":[297],"improves":[298],"against":[302],"patterns":[304,316],"time.":[306],"Many":[307],"subject":[312],"point":[318],"root":[321],"cause":[322],"fault,":[324],"wide":[331],"application":[332]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3}],"updated_date":"2026-03-30T08:08:38.191290","created_date":"2025-10-10T00:00:00"}