{"id":"https://openalex.org/W4413074197","doi":"https://doi.org/10.1109/tcsii.2025.3590139","title":"Contrastive Learning-Based Dual Autoencoder for Anomaly Detection in Loader Gearboxes","display_name":"Contrastive Learning-Based Dual Autoencoder for Anomaly Detection in Loader Gearboxes","publication_year":2025,"publication_date":"2025-07-17","ids":{"openalex":"https://openalex.org/W4413074197","doi":"https://doi.org/10.1109/tcsii.2025.3590139"},"language":"en","primary_location":{"id":"doi:10.1109/tcsii.2025.3590139","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcsii.2025.3590139","pdf_url":null,"source":{"id":"https://openalex.org/S93916849","display_name":"IEEE Transactions on Circuits & Systems II Express Briefs","issn_l":"1549-7747","issn":["1549-7747","1558-3791"],"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 Circuits and Systems II: Express Briefs","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/A5114180786","display_name":"Ruonan Lu","orcid":"https://orcid.org/0000-0001-8434-8087"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ruonan Lu","raw_affiliation_strings":["College of Control Science and Engineering, Zhejiang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-8434-8087","affiliations":[{"raw_affiliation_string":"College of Control Science and Engineering, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070502980","display_name":"Da Zheng","orcid":"https://orcid.org/0000-0003-3013-0722"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Da Zheng","raw_affiliation_strings":["College of Control Science and Engineering, Zhejiang University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Control Science and Engineering, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038863144","display_name":"Chengyuan Zhu","orcid":"https://orcid.org/0000-0002-6196-7683"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chengyuan Zhu","raw_affiliation_strings":["College of Control Science and Engineering, Zhejiang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-6196-7683","affiliations":[{"raw_affiliation_string":"College of Control Science and Engineering, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075456218","display_name":"Weiwei Cao","orcid":"https://orcid.org/0000-0002-2295-6340"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiwei Cao","raw_affiliation_strings":["College of Control Science and Engineering, Zhejiang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-2295-6340","affiliations":[{"raw_affiliation_string":"College of Control Science and Engineering, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062534500","display_name":"Qinmin Yang","orcid":"https://orcid.org/0000-0002-1602-8986"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qinmin Yang","raw_affiliation_strings":["College of Control Science and Engineering, Zhejiang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-1602-8986","affiliations":[{"raw_affiliation_string":"College of Control Science and Engineering, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5114180786"],"corresponding_institution_ids":["https://openalex.org/I76130692"],"apc_list":null,"apc_paid":null,"fwci":6.2854,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.96240421,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":"72","issue":"9","first_page":"1223","last_page":"1227"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9995999932289124,"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":0.9995999932289124,"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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9930999875068665,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.98089998960495,"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/autoencoder","display_name":"Autoencoder","score":0.9225672483444214},{"id":"https://openalex.org/keywords/loader","display_name":"Loader","score":0.7169629335403442},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6641623973846436},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.5473307967185974},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.5070931911468506},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.45949840545654297},{"id":"https://openalex.org/keywords/automotive-engineering","display_name":"Automotive engineering","score":0.4368641674518585},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4111681282520294},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.2738713026046753},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.2588716149330139},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.11897292733192444}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.9225672483444214},{"id":"https://openalex.org/C2779041774","wikidata":"https://www.wikidata.org/wiki/Q650550","display_name":"Loader","level":2,"score":0.7169629335403442},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6641623973846436},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.5473307967185974},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.5070931911468506},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.45949840545654297},{"id":"https://openalex.org/C171146098","wikidata":"https://www.wikidata.org/wiki/Q124192","display_name":"Automotive engineering","level":1,"score":0.4368641674518585},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4111681282520294},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2738713026046753},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2588716149330139},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.11897292733192444},{"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/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tcsii.2025.3590139","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcsii.2025.3590139","pdf_url":null,"source":{"id":"https://openalex.org/S93916849","display_name":"IEEE Transactions on Circuits & Systems II Express Briefs","issn_l":"1549-7747","issn":["1549-7747","1558-3791"],"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 Circuits and Systems II: Express Briefs","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1893049888","display_name":null,"funder_award_id":"U21A20478","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"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":22,"referenced_works":["https://openalex.org/W4206147254","https://openalex.org/W4220900860","https://openalex.org/W4221002311","https://openalex.org/W4285489620","https://openalex.org/W4292290851","https://openalex.org/W4360770751","https://openalex.org/W4377235375","https://openalex.org/W4385562572","https://openalex.org/W4389987972","https://openalex.org/W4390024371","https://openalex.org/W4391468021","https://openalex.org/W4392263116","https://openalex.org/W4392944417","https://openalex.org/W4393034022","https://openalex.org/W4400314830","https://openalex.org/W4401357727","https://openalex.org/W4402830560","https://openalex.org/W4403025174","https://openalex.org/W4405787076","https://openalex.org/W4406063242","https://openalex.org/W4408017423","https://openalex.org/W4410249929"],"related_works":["https://openalex.org/W3186512740","https://openalex.org/W4363671829","https://openalex.org/W2780476542","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/W2667207928"],"abstract_inverted_index":{"Anomaly":[0],"detection":[1,179],"(AD)":[2],"of":[3,14,67,112,138,145,153],"gearboxes":[4],"is":[5,61,74,95],"essential":[6],"for":[7,53,105,160],"ensuring":[8],"the":[9,15,30,57,78,98,103,110,118,123,132,135,142,146,150,154,172],"operational":[10],"safety":[11],"and":[12,36,108,149,186],"reliability":[13],"loader.":[16],"However,":[17],"identifying":[18],"anomalies":[19,26],"in":[20],"non-stationary":[21,68],"signals":[22],"remains":[23],"challenging":[24],"as":[25,158],"often":[27],"emerge":[28],"within":[29],"normal":[31,35,115,139],"fluctuation,":[32],"especially":[33],"when":[34],"abnormal":[37],"samples":[38,116,128],"exhibit":[39],"high":[40,177],"similarity.":[41],"This":[42],"brief":[43],"proposes":[44],"a":[45,87,91,176,181],"contrastive":[46,106,124,143],"learning-based":[47],"dual":[48],"autoencoder":[49],"(AE)":[50],"AD":[51],"method":[52,174],"loader":[54,167],"gearboxes.":[55],"Specifically,":[56],"continuous":[58],"wavelet":[59],"transform":[60],"employed":[62],"to":[63,81],"capture":[64],"dynamic":[65],"characteristics":[66],"signals.":[69],"A":[70],"compound":[71],"scaling":[72],"network":[73],"then":[75],"designed":[76],"into":[77,97],"unified":[79],"encoder":[80],"extract":[82],"complex":[83],"features":[84],"while":[85],"maintaining":[86],"lightweight":[88],"architecture.":[89],"Subsequently,":[90],"sparse":[92],"representation":[93],"channel":[94],"integrated":[96],"second":[99,147],"AE":[100,148,156],"framework,":[101],"complementing":[102],"basis":[104],"mechanisms":[107],"promoting":[109],"learning":[111],"consistency":[113,137],"across":[114],"with":[117],"reconstruction":[119,151],"channel.":[120],"By":[121],"minimizing":[122],"loss":[125,144],"between":[126],"two":[127],"from":[129],"different":[130],"channels,":[131],"model":[133],"learns":[134],"inherent":[136],"samples.":[140],"Finally,":[141],"error":[152],"first":[155],"serve":[157],"indicators":[159],"detecting":[161],"abnormalities.":[162],"Experimental":[163],"results":[164],"on":[165],"real-world":[166],"gearbox":[168],"data":[169],"demonstrate":[170],"that":[171],"proposed":[173],"achieves":[175],"fault":[178],"rate,":[180,185],"low":[182],"false":[183],"alarm":[184],"robust":[187],"reliability,":[188],"validating":[189],"its":[190],"effectiveness.":[191]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2}],"updated_date":"2026-05-05T08:41:31.759640","created_date":"2025-10-10T00:00:00"}
