{"id":"https://openalex.org/W4360605232","doi":"https://doi.org/10.1109/icaiic57133.2023.10067129","title":"Interaction-based Fault Detection and Classification Using Randomly Masked 1D Convolutional Neural Network","display_name":"Interaction-based Fault Detection and Classification Using Randomly Masked 1D Convolutional Neural Network","publication_year":2023,"publication_date":"2023-02-20","ids":{"openalex":"https://openalex.org/W4360605232","doi":"https://doi.org/10.1109/icaiic57133.2023.10067129"},"language":"en","primary_location":{"id":"doi:10.1109/icaiic57133.2023.10067129","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icaiic57133.2023.10067129","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","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/A5101563697","display_name":"Jueun Kim","orcid":"https://orcid.org/0000-0001-7485-8235"},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Jueun Kim","raw_affiliation_strings":["Korea University,Department of Industrial and Management Engineering,Seoul,South Korea","Department of Industrial and Management Engineering, Korea University, Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"Korea University,Department of Industrial and Management Engineering,Seoul,South Korea","institution_ids":["https://openalex.org/I197347611"]},{"raw_affiliation_string":"Department of Industrial and Management Engineering, Korea University, Seoul, South Korea","institution_ids":["https://openalex.org/I197347611"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038575120","display_name":"Seunghwan Song","orcid":"https://orcid.org/0000-0002-2497-3785"},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seunghwan Song","raw_affiliation_strings":["Korea University,Department of Industrial and Management Engineering,Seoul,South Korea","Department of Industrial and Management Engineering, Korea University, Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"Korea University,Department of Industrial and Management Engineering,Seoul,South Korea","institution_ids":["https://openalex.org/I197347611"]},{"raw_affiliation_string":"Department of Industrial and Management Engineering, Korea University, Seoul, South Korea","institution_ids":["https://openalex.org/I197347611"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091180722","display_name":"Woo Young Hwang","orcid":null},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Woo Young Hwang","raw_affiliation_strings":["Korea University,Department of Industrial and Management Engineering,Seoul,South Korea","Department of Industrial and Management Engineering, Korea University, Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"Korea University,Department of Industrial and Management Engineering,Seoul,South Korea","institution_ids":["https://openalex.org/I197347611"]},{"raw_affiliation_string":"Department of Industrial and Management Engineering, Korea University, Seoul, South Korea","institution_ids":["https://openalex.org/I197347611"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021164528","display_name":"Jun\u2010Geol Baek","orcid":"https://orcid.org/0000-0002-7088-1478"},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jun-Geol Baek","raw_affiliation_strings":["Korea University,Department of Industrial and Management Engineering,Seoul,South Korea","Department of Industrial and Management Engineering, Korea University, Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"Korea University,Department of Industrial and Management Engineering,Seoul,South Korea","institution_ids":["https://openalex.org/I197347611"]},{"raw_affiliation_string":"Department of Industrial and Management Engineering, Korea University, Seoul, South Korea","institution_ids":["https://openalex.org/I197347611"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101563697"],"corresponding_institution_ids":["https://openalex.org/I197347611"],"apc_list":null,"apc_paid":null,"fwci":0.1746,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.51602836,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"479","last_page":"482"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9983000159263611,"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":0.9983000159263611,"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":0.996399998664856,"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9839000105857849,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7907014489173889},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7538825273513794},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6340172290802002},{"id":"https://openalex.org/keywords/fault-detection-and-isolation","display_name":"Fault detection and isolation","score":0.517973780632019},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.507789671421051},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.48410308361053467},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48127835988998413},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4393925964832306},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4061450660228729}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7907014489173889},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7538825273513794},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6340172290802002},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.517973780632019},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.507789671421051},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.48410308361053467},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48127835988998413},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4393925964832306},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4061450660228729},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C172707124","wikidata":"https://www.wikidata.org/wiki/Q423488","display_name":"Actuator","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icaiic57133.2023.10067129","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icaiic57133.2023.10067129","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W2097117768","https://openalex.org/W2147800946","https://openalex.org/W2194775991","https://openalex.org/W2594332903","https://openalex.org/W2768753204","https://openalex.org/W2804879845","https://openalex.org/W2906498146","https://openalex.org/W2997404503"],"related_works":["https://openalex.org/W3093612317","https://openalex.org/W4287776258","https://openalex.org/W3027997911","https://openalex.org/W2175746458","https://openalex.org/W2732542196","https://openalex.org/W2760085659","https://openalex.org/W2883200793","https://openalex.org/W2738221750","https://openalex.org/W3012978760","https://openalex.org/W2912288872"],"abstract_inverted_index":{"Fault":[0],"detection":[1],"and":[2,14,30,37,59,129,150,157],"classification":[3],"(FDC)":[4],"refers":[5],"to":[6,73,107,125],"a":[7,67,91,108,134,145,161],"data":[8,57],"analysis":[9],"technique":[10],"that":[11,69,99,132],"classifies":[12],"normal":[13],"abnormal":[15],"situations.":[16],"Data":[17],"for":[18],"FDC":[19,101,127],"is":[20,71],"being":[21],"collected":[22],"in":[23,42,63,80,83,122],"various":[24,31],"processes":[25],"such":[26,33],"as":[27,34],"manufacturing":[28],"processes,":[29],"algorithms":[32],"machine":[35],"learning":[36,39],"deep":[38],"are":[40,60],"used":[41,62,153],"FDC.":[43],"In":[44],"particular,":[45],"convolutional":[46,94],"neural":[47,95],"network":[48,96],"(CNN)":[49],"models":[50],"have":[51,133],"the":[52,76,81,118,123,164,166,172],"advantage":[53],"of":[54,136,163],"automatically":[55],"extracting":[56],"features":[58],"widely":[61],"analysis,":[64],"which":[65],"has":[66],"limitation":[68],"it":[70],"difficult":[72],"directly":[74,116],"reflect":[75,117],"relationship":[77,119],"between":[78,120],"variables":[79,121,131],"model":[82,98,124],"multivariate":[84],"time":[85],"series":[86],"data.":[87],"This":[88],"work":[89],"proposes":[90],"randomly":[92],"masked":[93],"(RM-CNN)":[97],"enhances":[100],"performance":[102,128],"by":[103,142],"adding":[104],"intervariable":[105],"relationships":[106],"1D":[109,147],"CNN":[110,148],"algorithm.":[111],"The":[112,138],"proposed":[113,167],"methodology":[114,168],"can":[115],"improve":[126],"identify":[130],"lot":[135],"influence.":[137],"experiments":[139],"were":[140],"conducted":[141],"comparison":[143],"with":[144],"typical":[146],"model,":[149],"evaluation":[151],"indicators":[152],"Accuracy,":[154],"Precision,":[155],"Recall,":[156],"F1":[158],"score.":[159],"As":[160],"result":[162],"experiment,":[165],"performed":[169],"better":[170],"than":[171],"comparative":[173],"models.":[174]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-12-23T23:11:35.936235","created_date":"2025-10-10T00:00:00"}