{"id":"https://openalex.org/W7119220648","doi":"https://doi.org/10.1109/indin64977.2025.11279627","title":"Time-Frequency Feature Fusion and Graph Convolutional Adversarial Network for Cross-Domain Fault Diagnosis","display_name":"Time-Frequency Feature Fusion and Graph Convolutional Adversarial Network for Cross-Domain Fault Diagnosis","publication_year":2025,"publication_date":"2025-07-12","ids":{"openalex":"https://openalex.org/W7119220648","doi":"https://doi.org/10.1109/indin64977.2025.11279627"},"language":null,"primary_location":{"id":"doi:10.1109/indin64977.2025.11279627","is_oa":false,"landing_page_url":"https://doi.org/10.1109/indin64977.2025.11279627","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 23rd International Conference on Industrial Informatics (INDIN)","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/A5122210238","display_name":"Haorui Hu","orcid":null},"institutions":[{"id":"https://openalex.org/I10660446","display_name":"Kunming University of Science and Technology","ror":"https://ror.org/00xyeez13","country_code":"CN","type":"education","lineage":["https://openalex.org/I10660446"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haorui Hu","raw_affiliation_strings":["Kunming University of Science and Technology,Faculty of Information Engineering and Automation,Kunming,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Kunming University of Science and Technology,Faculty of Information Engineering and Automation,Kunming,China","institution_ids":["https://openalex.org/I10660446"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120975869","display_name":"Huaiping Jin","orcid":null},"institutions":[{"id":"https://openalex.org/I10660446","display_name":"Kunming University of Science and Technology","ror":"https://ror.org/00xyeez13","country_code":"CN","type":"education","lineage":["https://openalex.org/I10660446"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huaiping Jin","raw_affiliation_strings":["Kunming University of Science and Technology,Faculty of Information Engineering and Automation,Kunming,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Kunming University of Science and Technology,Faculty of Information Engineering and Automation,Kunming,China","institution_ids":["https://openalex.org/I10660446"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028339258","display_name":"Sicong Liu","orcid":"https://orcid.org/0000-0003-1078-7006"},"institutions":[{"id":"https://openalex.org/I4210155232","display_name":"Fiberhome Technology Group (China)","ror":"https://ror.org/04yv20134","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210155232"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sicong Liu","raw_affiliation_strings":["Wuhan Maritime Communication Research Institute,Wuhan,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wuhan Maritime Communication Research Institute,Wuhan,China","institution_ids":["https://openalex.org/I4210155232"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5122265594","display_name":"Hao Fang","orcid":null},"institutions":[{"id":"https://openalex.org/I10660446","display_name":"Kunming University of Science and Technology","ror":"https://ror.org/00xyeez13","country_code":"CN","type":"education","lineage":["https://openalex.org/I10660446"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hao Fang","raw_affiliation_strings":["Kunming University of Science and Technology,Faculty of Information Engineering and Automation,Kunming,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Kunming University of Science and Technology,Faculty of Information Engineering and Automation,Kunming,China","institution_ids":["https://openalex.org/I10660446"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.58751802,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9740999937057495,"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":0.9740999937057495,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.006899999920278788,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.002400000113993883,"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/discriminative-model","display_name":"Discriminative model","score":0.7404000163078308},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5616000294685364},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5311999917030334},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4767000079154968},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.4462999999523163},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.43700000643730164},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.4318999946117401},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.42399999499320984},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.35519999265670776}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7404000163078308},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6711999773979187},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5616000294685364},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5311999917030334},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5109999775886536},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4767000079154968},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.4462999999523163},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.43700000643730164},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.4318999946117401},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.42730000615119934},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.42399999499320984},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.35519999265670776},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.32899999618530273},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.3285999894142151},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.31839999556541443},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.3165000081062317},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3010999858379364},{"id":"https://openalex.org/C162319229","wikidata":"https://www.wikidata.org/wiki/Q175263","display_name":"Data structure","level":2,"score":0.2863999903202057},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.28380000591278076},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.28369998931884766},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.2653999924659729},{"id":"https://openalex.org/C146380142","wikidata":"https://www.wikidata.org/wiki/Q1137726","display_name":"Directed graph","level":2,"score":0.2630000114440918},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.2624000012874603},{"id":"https://openalex.org/C32946077","wikidata":"https://www.wikidata.org/wiki/Q618079","display_name":"Network analysis","level":2,"score":0.2619999945163727},{"id":"https://openalex.org/C2988416141","wikidata":"https://www.wikidata.org/wiki/Q6031139","display_name":"Information loss","level":2,"score":0.2612000107765198},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.25920000672340393},{"id":"https://openalex.org/C2988224531","wikidata":"https://www.wikidata.org/wiki/Q20830730","display_name":"Network structure","level":2,"score":0.25920000672340393},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.25699999928474426},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2547999918460846},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.2502000033855438}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/indin64977.2025.11279627","is_oa":false,"landing_page_url":"https://doi.org/10.1109/indin64977.2025.11279627","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 23rd International Conference on Industrial Informatics (INDIN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.7202348113059998,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1731081199","https://openalex.org/W2898375427","https://openalex.org/W2907492528","https://openalex.org/W2946724317","https://openalex.org/W2964285681","https://openalex.org/W3011667710","https://openalex.org/W3213908205","https://openalex.org/W4200473862","https://openalex.org/W4295529416","https://openalex.org/W4307567853","https://openalex.org/W4309164266","https://openalex.org/W4321016476","https://openalex.org/W4327852039","https://openalex.org/W4365459836","https://openalex.org/W4375854801","https://openalex.org/W4379645274","https://openalex.org/W4384406400","https://openalex.org/W4384819582","https://openalex.org/W4388084380","https://openalex.org/W4388153530","https://openalex.org/W4388706355","https://openalex.org/W4389005597","https://openalex.org/W4389763620","https://openalex.org/W4400808515","https://openalex.org/W4403417265","https://openalex.org/W4404452017"],"related_works":[],"abstract_inverted_index":{"Unsupervised":[0],"Domain":[1],"Adaptation":[2],"(UDA)":[3],"technology":[4],"has":[5],"shown":[6],"significant":[7],"potential":[8],"in":[9,17,63,149,163],"multi-condition":[10],"mechanical":[11],"fault":[12,81],"diagnosis.":[13],"Its":[14],"core":[15],"lies":[16],"utilizing":[18],"class":[19],"labels,":[20,22],"domain":[21,54],"and":[23,53,129],"data":[24,59],"structure":[25,60,103],"information":[26,49,114,146],"to":[27,71,108,122,160],"achieve":[28],"cross-domain":[29],"knowledge":[30],"transfer.":[31],"However,":[32],"existing":[33],"methods":[34],"generally":[35],"suffer":[36],"from":[37,125],"two":[38,46],"key":[39],"issues:":[40],"they":[41,69],"only":[42],"model":[43],"the":[44,77,142,169,176,186],"first":[45],"types":[47,144],"of":[48,80,141,145,171],"through":[50,134],"classification":[51],"loss":[52,56],"adversarial":[55],"while":[57],"neglecting":[58],"information,":[61],"resulting":[62],"insufficient":[64],"feature":[65],"discriminative":[66],"capability;":[67],"meanwhile,":[68],"fail":[70],"effectively":[72],"integrate":[73],"multi-view":[74],"signals,":[75],"limiting":[76],"comprehensive":[78],"characterization":[79],"features.":[82],"To":[83],"address":[84],"these":[85],"issues,":[86],"this":[87,183],"paper":[88],"proposes":[89],"a":[90,100,135,150],"Time-Frequency":[91],"Feature":[92],"Fusion":[93],"Graph":[94,116],"Convolutional":[95,117],"Adversarial":[96],"Network":[97,118],"(TFFGCAN).":[98],"First,":[99],"dual-channel":[101],"graph":[102,110,127,164],"modeling":[104,140],"module":[105],"is":[106,147,158,173],"constructed":[107],"build":[109],"structures":[111,165],"for":[112],"time-frequency":[113,130],"separately.":[115],"(GCN)":[119],"are":[120,132],"employed":[121],"extract":[123],"features":[124,131],"both":[126],"data,":[128],"fused":[133],"cross-attention":[136],"mechanism.":[137],"Second,":[138],"joint":[139],"three":[143],"achieved":[148],"unified":[151],"framework,":[152],"where":[153],"multi-kernel":[154],"maximum":[155],"mean":[156],"discrepancy":[157],"used":[159],"measure":[161],"differences":[162],"across":[166],"domains.":[167],"Finally,":[168],"effectiveness":[170],"TFFGCAN":[172],"verified":[174],"on":[175],"XJTUSpurgear":[177],"dataset.":[178],"The":[179],"results":[180],"show":[181],"that":[182],"method":[184],"achieves":[185],"best":[187],"performance":[188],"among":[189],"comparative":[190],"methods.":[191]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-01-08T00:00:00"}
