{"id":"https://openalex.org/W7125944440","doi":"https://doi.org/10.1109/smc58881.2025.11342757","title":"RTART: A signal processing-informed neural network for cross-individual fault diagnosis*","display_name":"RTART: A signal processing-informed neural network for cross-individual fault diagnosis*","publication_year":2025,"publication_date":"2025-10-05","ids":{"openalex":"https://openalex.org/W7125944440","doi":"https://doi.org/10.1109/smc58881.2025.11342757"},"language":null,"primary_location":{"id":"doi:10.1109/smc58881.2025.11342757","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc58881.2025.11342757","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","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/A5059273157","display_name":"Yiming He","orcid":"https://orcid.org/0000-0002-6156-3507"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yiming He","raw_affiliation_strings":["Huazhong University of Science and Technology,State Key Laboratory of Intelligent Manufacturing Equipment and Technology,Wuhan,China,430074"],"affiliations":[{"raw_affiliation_string":"Huazhong University of Science and Technology,State Key Laboratory of Intelligent Manufacturing Equipment and Technology,Wuhan,China,430074","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084598007","display_name":"Chao Zhao","orcid":"https://orcid.org/0000-0002-4165-2123"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chao Zhao","raw_affiliation_strings":["Huazhong University of Science and Technology,State Key Laboratory of Intelligent Manufacturing Equipment and Technology,Wuhan,China,430074"],"affiliations":[{"raw_affiliation_string":"Huazhong University of Science and Technology,State Key Laboratory of Intelligent Manufacturing Equipment and Technology,Wuhan,China,430074","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5124053342","display_name":"Weiming Shen","orcid":null},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiming Shen","raw_affiliation_strings":["Huazhong University of Science and Technology,State Key Laboratory of Intelligent Manufacturing Equipment and Technology,Wuhan,China,430074"],"affiliations":[{"raw_affiliation_string":"Huazhong University of Science and Technology,State Key Laboratory of Intelligent Manufacturing Equipment and Technology,Wuhan,China,430074","institution_ids":["https://openalex.org/I47720641"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5059273157"],"corresponding_institution_ids":["https://openalex.org/I47720641"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.70161529,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"5980","last_page":"5985"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.988099992275238,"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":0.988099992275238,"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/T10534","display_name":"Structural Health Monitoring Techniques","score":0.002300000051036477,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/T12131","display_name":"Wireless Signal Modulation Classification","score":0.0017000000225380063,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.7236999869346619},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5135999917984009},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5102999806404114},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4666000008583069},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4657000005245209},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.44749999046325684},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4471000134944916},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.44339999556541443},{"id":"https://openalex.org/keywords/frequency-domain","display_name":"Frequency domain","score":0.421099990606308}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.7236999869346619},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6202999949455261},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5439000129699707},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5135999917984009},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5102999806404114},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4666000008583069},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4657000005245209},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.44749999046325684},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4471000134944916},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.44339999556541443},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.421099990606308},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4032999873161316},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.390500009059906},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3871999979019165},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.37470000982284546},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.3668000102043152},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.3549000024795532},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.35269999504089355},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.32440000772476196},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3068999946117401},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30230000615119934},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.29919999837875366},{"id":"https://openalex.org/C2775846686","wikidata":"https://www.wikidata.org/wiki/Q643012","display_name":"Condition monitoring","level":2,"score":0.29660001397132874},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.2946000099182129},{"id":"https://openalex.org/C112633086","wikidata":"https://www.wikidata.org/wiki/Q381287","display_name":"White noise","level":2,"score":0.2685000002384186},{"id":"https://openalex.org/C142433447","wikidata":"https://www.wikidata.org/wiki/Q7806653","display_name":"Time\u2013frequency analysis","level":3,"score":0.2660999894142151},{"id":"https://openalex.org/C45273575","wikidata":"https://www.wikidata.org/wiki/Q578970","display_name":"Spectrogram","level":2,"score":0.26260000467300415},{"id":"https://openalex.org/C47446073","wikidata":"https://www.wikidata.org/wiki/Q5165890","display_name":"Control theory (sociology)","level":3,"score":0.2531000077724457},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.2515999972820282}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smc58881.2025.11342757","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc58881.2025.11342757","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7109946012496948,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W243674440","https://openalex.org/W3128453436","https://openalex.org/W4200322140","https://openalex.org/W4200473862","https://openalex.org/W4310696200","https://openalex.org/W4313367665","https://openalex.org/W4317751798","https://openalex.org/W4360596056","https://openalex.org/W4379206867","https://openalex.org/W4391247724","https://openalex.org/W4392808143","https://openalex.org/W4395056627","https://openalex.org/W4398618421","https://openalex.org/W4398776830","https://openalex.org/W4399563287","https://openalex.org/W4401046592","https://openalex.org/W4403202847","https://openalex.org/W4404591835","https://openalex.org/W4404728392"],"related_works":[],"abstract_inverted_index":{"The":[0,174],"traditional":[1],"intelligent":[2],"models":[3],"developed":[4,158],"based":[5,114],"on":[6,115,179],"the":[7,70,89,93,104,143,161,171,185,190],"source":[8],"device":[9],"fault":[10,65],"diagnosis":[11,66],"(SDFD)":[12],"framework":[13],"have":[14],"achieved":[15],"high":[16],"accuracy,":[17],"but":[18],"may":[19],"lead":[20],"to":[21,137,148,159],"unreliable":[22],"diagnostic":[23,28],"results":[24],"for":[25,62,131,141],"unseen":[26],"individual":[27,34,64,149,203],"scenarios,":[29],"as":[30,37],"they":[31],"ignore":[32],"potential":[33],"differences":[35],"(such":[36],"assembly":[38],"changes,":[39],"noise":[40],"interference).":[41],"This":[42],"paper":[43],"proposes":[44],"a":[45,54,125,180,201],"refined":[46],"trigonometric":[47,152],"activation":[48,153],"representation":[49,154],"transformer":[50],"(RTART),":[51],"which":[52,135],"is":[53,157,177],"signal":[55],"processing":[56],"informed":[57],"neural":[58],"network":[59],"(SPINN)":[60],"tailored":[61],"cross":[63,202],"(CIFD).":[67],"By":[68],"integrating":[69],"theory":[71],"of":[72,88,95,145,165,206],"Sparse":[73],"Short":[74],"Time":[75],"Fourier":[76],"Transform":[77],"(DSTFT)":[78],"and":[79,99,121,169,199],"Kolmogorov":[80],"Arnold":[81],"Representation":[82],"Theorem":[83],"(KART)":[84],"into":[85,111,124],"structural":[86],"design":[87],"Transformer,":[90],"RTART":[91,194],"enhances":[92],"extraction":[94],"periodic":[96,166],"vibration":[97,106,167],"features":[98,140],"frequency":[100],"modulation":[101],"diversity.":[102],"Specifically,":[103],"original":[105],"signals":[107,168],"are":[108],"first":[109],"segmented":[110],"regional":[112],"patches":[113],"one-dimensional":[116],"convolutional":[117],"patch":[118],"(1-DCP)":[119],"tokenizer,":[120],"then":[122],"input":[123],"multi-scale":[126],"region":[127],"pruning":[128],"(MSRP)":[129],"module":[130,156],"global":[132],"feature":[133,163],"refinement,":[134],"aims":[136],"refine":[138],"decision":[139],"reducing":[142],"risk":[144],"overfitting":[146],"due":[147],"differences.":[150],"A":[151],"(TrigAR)":[155],"enhance":[160],"reliable":[162],"expression":[164],"improve":[170],"model":[172],"generalization.":[173],"proposed":[175],"method":[176],"validated":[178],"well-known":[181],"public":[182],"dataset":[183],"using":[184],"CIFD":[186],"benchmark.":[187],"Compared":[188],"with":[189],"most":[191],"advanced":[192],"methods,":[193],"has":[195],"better":[196],"generalization":[197],"ability":[198],"achieves":[200],"accuracy":[204],"rate":[205],"over":[207],"95%.":[208]},"counts_by_year":[],"updated_date":"2026-01-29T23:17:01.242718","created_date":"2026-01-29T00:00:00"}
