{"id":"https://openalex.org/W2903777113","doi":"https://doi.org/10.1109/ickii.2018.8569065","title":"Stacked Convolutional Bidirectional LSTM Recurrent Neural Network for Bearing Anomaly Detection in Rotating Machinery Diagnostics","display_name":"Stacked Convolutional Bidirectional LSTM Recurrent Neural Network for Bearing Anomaly Detection in Rotating Machinery Diagnostics","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2903777113","doi":"https://doi.org/10.1109/ickii.2018.8569065","mag":"2903777113"},"language":"en","primary_location":{"id":"doi:10.1109/ickii.2018.8569065","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ickii.2018.8569065","pdf_url":null,"source":{"id":"https://openalex.org/S4306498242","display_name":"2018 1st IEEE International Conference on Knowledge Innovation and Invention (ICKII)","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":"2018 1st IEEE International Conference on Knowledge Innovation and Invention (ICKII)","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/A5101519040","display_name":"Kwangsuk Lee","orcid":"https://orcid.org/0000-0003-4315-1258"},"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":"Kwangsuk Lee","raw_affiliation_strings":["School of Electrical & Electronic Engineering, Yonsei University, 50 Yonsei-Ro Seodaemun-Gu, Seoul, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical & Electronic Engineering, Yonsei University, 50 Yonsei-Ro Seodaemun-Gu, Seoul, Korea","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036016494","display_name":"Jae-Kyeong Kim","orcid":"https://orcid.org/0000-0001-9486-2434"},"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":"Jae-Kyeong Kim","raw_affiliation_strings":["School of Electrical & Electronic Engineering, Yonsei University, 50 Yonsei-Ro Seodaemun-Gu, Seoul, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical & Electronic Engineering, Yonsei University, 50 Yonsei-Ro Seodaemun-Gu, Seoul, Korea","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081215575","display_name":"Jaehyong Kim","orcid":null},"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":"Jaehyong Kim","raw_affiliation_strings":["School of Electrical & Electronic Engineering, Yonsei University, 50 Yonsei-Ro Seodaemun-Gu, Seoul, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical & Electronic Engineering, Yonsei University, 50 Yonsei-Ro Seodaemun-Gu, Seoul, Korea","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056817921","display_name":"Kyeon Hur","orcid":"https://orcid.org/0000-0003-3726-7545"},"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":"Kyeon Hur","raw_affiliation_strings":["School of Electrical & Electronic Engineering, Yonsei University, 50 Yonsei-Ro Seodaemun-Gu, Seoul, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical & Electronic Engineering, Yonsei University, 50 Yonsei-Ro Seodaemun-Gu, Seoul, Korea","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033718123","display_name":"Hagbae Kim","orcid":null},"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":"Hagbae Kim","raw_affiliation_strings":["School of Electrical & Electronic Engineering, Yonsei University, 50 Yonsei-Ro Seodaemun-Gu, Seoul, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical & Electronic Engineering, Yonsei University, 50 Yonsei-Ro Seodaemun-Gu, Seoul, Korea","institution_ids":["https://openalex.org/I193775966"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I193775966"],"apc_list":null,"apc_paid":null,"fwci":4.0117,"has_fulltext":false,"cited_by_count":21,"citation_normalized_percentile":{"value":0.9574344,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"98","last_page":"101"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9973000288009644,"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":0.9973000288009644,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9950000047683716,"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/T14319","display_name":"Currency Recognition and Detection","score":0.947700023651123,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7970515489578247},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7288146018981934},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6828292608261108},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6713201403617859},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6537730693817139},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6435778141021729},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.6375113725662231},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6008527278900146},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.5527834892272949},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5137777924537659},{"id":"https://openalex.org/keywords/long-short-term-memory","display_name":"Long short term memory","score":0.4491698741912842},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3399748206138611}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7970515489578247},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7288146018981934},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6828292608261108},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6713201403617859},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6537730693817139},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6435778141021729},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.6375113725662231},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6008527278900146},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.5527834892272949},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5137777924537659},{"id":"https://openalex.org/C133488467","wikidata":"https://www.wikidata.org/wiki/Q6673524","display_name":"Long short term memory","level":4,"score":0.4491698741912842},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3399748206138611},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ickii.2018.8569065","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ickii.2018.8569065","pdf_url":null,"source":{"id":"https://openalex.org/S4306498242","display_name":"2018 1st IEEE International Conference on Knowledge Innovation and Invention (ICKII)","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":"2018 1st IEEE International Conference on Knowledge Innovation and Invention (ICKII)","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":10,"referenced_works":["https://openalex.org/W1538131130","https://openalex.org/W1689711448","https://openalex.org/W2064675550","https://openalex.org/W2079735306","https://openalex.org/W2112796928","https://openalex.org/W2131774270","https://openalex.org/W2144499799","https://openalex.org/W2580840020","https://openalex.org/W2607863109","https://openalex.org/W6632100814"],"related_works":["https://openalex.org/W2806741695","https://openalex.org/W4290647774","https://openalex.org/W3189286258","https://openalex.org/W3207797160","https://openalex.org/W3210364259","https://openalex.org/W4300558037","https://openalex.org/W2912112202","https://openalex.org/W2667207928","https://openalex.org/W4377864969","https://openalex.org/W3030345572"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"a":[3,39,92,133],"multi-layered":[4],"anomaly":[5,15,97,115],"detection":[6,116],"scheme":[7],"to":[8,13,70],"train":[9],"feature":[10,58,71,89],"extraction":[11,59],"and":[12,25,65,91],"test":[14],"prediction":[16],"by":[17],"using":[18],"Convolutional":[19],"Neural":[20,32],"Networks":[21,33],"(CNNs)":[22],"layer,":[23],"Bidirectional":[24],"Unidirectional":[26],"Long":[27],"Short-Term":[28],"Memory":[29],"(LSTM)":[30],"Recurrent":[31],"(RNNs),":[34],"which":[35],"is":[36,68],"one":[37],"of":[38,60,102,110],"novel":[40],"deep":[41],"architecture":[42],"named":[43],"stacked":[44,55,75,83],"convolutional":[45],"bidirectional":[46,76],"LSTM":[47],"network":[48],"(SCB-LSTM).":[49],"In":[50],"the":[51,54,66,74,82,88,108,111,124],"proposed":[52,112],"model,":[53],"CNNs":[56],"perform":[57],"vibration":[61],"sensor":[62],"signal":[63],"patterns,":[64],"result":[67],"used":[69],"learning":[72],"with":[73],"LSTMs":[77,85],"(SB-LSTMs).":[78],"After":[79],"this":[80],"procedure,":[81],"unidirectional":[84],"(SU-LSTMs)":[86],"enhance":[87],"learning,":[90],"regression":[93],"layer":[94],"finally":[95],"predicts":[96],"detections.":[98],"The":[99],"experimental":[100],"results":[101],"bearing":[103],"data":[104],"not":[105],"only":[106],"show":[107],"accuracy":[109],"model":[113],"in":[114],"for":[117],"rotating":[118],"machinery":[119],"diagnostics,":[120],"but":[121],"also":[122],"suggest":[123],"better":[125],"performance":[126],"than":[127],"other":[128],"state-of-the-art":[129],"algorithms":[130],"such":[131],"as":[132],"plain":[134],"uni-LSTM":[135],"or":[136],"Bi-LSTM.":[137]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}