{"id":"https://openalex.org/W4387010813","doi":"https://doi.org/10.1109/tim.2023.3318686","title":"Sparse Sample Train Axle Bearing Fault Diagnosis: A Semi-Supervised Model Based on Prior Knowledge Embedding","display_name":"Sparse Sample Train Axle Bearing Fault Diagnosis: A Semi-Supervised Model Based on Prior Knowledge Embedding","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4387010813","doi":"https://doi.org/10.1109/tim.2023.3318686"},"language":"en","primary_location":{"id":"doi:10.1109/tim.2023.3318686","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2023.3318686","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"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 Instrumentation and Measurement","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/A5026781933","display_name":"Yaxin Li","orcid":"https://orcid.org/0000-0001-7039-8091"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yaxin Li","raw_affiliation_strings":["Key Laboratory of Traffic Safety on Track, Ministry of Education, Central South University, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0001-7039-8091","affiliations":[{"raw_affiliation_string":"Key Laboratory of Traffic Safety on Track, Ministry of Education, Central South University, Changsha, China","institution_ids":["https://openalex.org/I139660479"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020697507","display_name":"Suchao Xie","orcid":"https://orcid.org/0000-0003-2855-2774"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Suchao Xie","raw_affiliation_strings":["Key Laboratory of Traffic Safety on Track, Ministry of Education, Central South University, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0003-2855-2774","affiliations":[{"raw_affiliation_string":"Key Laboratory of Traffic Safety on Track, Ministry of Education, Central South University, Changsha, China","institution_ids":["https://openalex.org/I139660479"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073532299","display_name":"Jiacheng Wang","orcid":"https://orcid.org/0009-0000-9086-9911"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiacheng Wang","raw_affiliation_strings":["Key Laboratory of Traffic Safety on Track, Ministry of Education, Central South University, Changsha, China"],"raw_orcid":"https://orcid.org/0009-0000-9086-9911","affiliations":[{"raw_affiliation_string":"Key Laboratory of Traffic Safety on Track, Ministry of Education, Central South University, Changsha, China","institution_ids":["https://openalex.org/I139660479"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100619145","display_name":"Jing Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Zhang","raw_affiliation_strings":["Key Laboratory of Traffic Safety on Track, Ministry of Education, Central South University, Changsha, China"],"raw_orcid":"https://orcid.org/0009-0008-5227-0981","affiliations":[{"raw_affiliation_string":"Key Laboratory of Traffic Safety on Track, Ministry of Education, Central South University, Changsha, China","institution_ids":["https://openalex.org/I139660479"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076132813","display_name":"Hongyu Yan","orcid":"https://orcid.org/0009-0002-0454-2256"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongyu Yan","raw_affiliation_strings":["Key Laboratory of Traffic Safety on Track, Ministry of Education, Central South University, Changsha, China"],"raw_orcid":"https://orcid.org/0009-0002-0454-2256","affiliations":[{"raw_affiliation_string":"Key Laboratory of Traffic Safety on Track, Ministry of Education, Central South University, Changsha, China","institution_ids":["https://openalex.org/I139660479"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5026781933"],"corresponding_institution_ids":["https://openalex.org/I139660479"],"apc_list":null,"apc_paid":null,"fwci":3.5868,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.93337693,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"72","issue":null,"first_page":"1","last_page":"11"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9937999844551086,"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":0.9937999844551086,"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/T13891","display_name":"Engineering Diagnostics and Reliability","score":0.9251999855041504,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.6963208317756653},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6538393497467041},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5943491458892822},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5773594379425049},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.5673898458480835},{"id":"https://openalex.org/keywords/domain-knowledge","display_name":"Domain knowledge","score":0.5080170631408691},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5018386840820312},{"id":"https://openalex.org/keywords/sample-entropy","display_name":"Sample entropy","score":0.491733580827713},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.49090462923049927},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.42958804965019226},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.41300541162490845},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4125225245952606},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3665199875831604}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6963208317756653},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6538393497467041},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5943491458892822},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5773594379425049},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.5673898458480835},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.5080170631408691},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5018386840820312},{"id":"https://openalex.org/C66696666","wikidata":"https://www.wikidata.org/wiki/Q17105612","display_name":"Sample entropy","level":3,"score":0.491733580827713},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.49090462923049927},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.42958804965019226},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.41300541162490845},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4125225245952606},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3665199875831604},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C165205528","wikidata":"https://www.wikidata.org/wiki/Q83371","display_name":"Seismology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tim.2023.3318686","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2023.3318686","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"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 Instrumentation and Measurement","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.49000000953674316,"display_name":"Peace, Justice and strong institutions"},{"id":"https://metadata.un.org/sdg/10","score":0.44999998807907104,"display_name":"Reduced inequalities"}],"awards":[{"id":"https://openalex.org/G5508951709","display_name":null,"funder_award_id":"2022YFB4300300","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G6706739579","display_name":null,"funder_award_id":"52202455","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7026084839","display_name":null,"funder_award_id":"2023JJ31015","funder_id":"https://openalex.org/F4320322843","funder_display_name":"Natural Science Foundation of\u00a0Hunan Province"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322843","display_name":"Natural Science Foundation of\u00a0Hunan Province","ror":null},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W2077942936","https://openalex.org/W2530133016","https://openalex.org/W2551393996","https://openalex.org/W2734256217","https://openalex.org/W2768753204","https://openalex.org/W2896365146","https://openalex.org/W2933648389","https://openalex.org/W2961333734","https://openalex.org/W2967115638","https://openalex.org/W2989638106","https://openalex.org/W3002188287","https://openalex.org/W3005858530","https://openalex.org/W3009656642","https://openalex.org/W3041076719","https://openalex.org/W3094105523","https://openalex.org/W3094627074","https://openalex.org/W3104020727","https://openalex.org/W3108417453","https://openalex.org/W3110652625","https://openalex.org/W3120795858","https://openalex.org/W3126446664","https://openalex.org/W3128041782","https://openalex.org/W3128553897","https://openalex.org/W3162049936","https://openalex.org/W3162926177","https://openalex.org/W3167706095","https://openalex.org/W3173549566","https://openalex.org/W3190152617","https://openalex.org/W3201612248","https://openalex.org/W3203494009","https://openalex.org/W3208240709","https://openalex.org/W3209453546","https://openalex.org/W4200432631","https://openalex.org/W4206553297","https://openalex.org/W4210907964","https://openalex.org/W4283721935","https://openalex.org/W4285121196","https://openalex.org/W4288059551","https://openalex.org/W4295126433","https://openalex.org/W4304113722","https://openalex.org/W4312499228","https://openalex.org/W4319866461","https://openalex.org/W6728255800","https://openalex.org/W6783037059","https://openalex.org/W6795288823","https://openalex.org/W6801682309"],"related_works":["https://openalex.org/W2165375209","https://openalex.org/W2076258781","https://openalex.org/W2050076996","https://openalex.org/W229760109","https://openalex.org/W2093589470","https://openalex.org/W2006708776","https://openalex.org/W3088843374","https://openalex.org/W2415751681","https://openalex.org/W2889925888","https://openalex.org/W2022287597"],"abstract_inverted_index":{"Data-driven":[0],"fault":[1,90,144,151,168],"diagnosis":[2,145],"models":[3,37],"often":[4],"exhibit":[5],"limited":[6,147],"generalization":[7],"abilities":[8],"when":[9],"trained":[10],"on":[11,105],"small":[12,162],"sample":[13,163],"sizes,":[14],"as":[15,23],"commonly":[16],"encountered":[17],"in":[18,44,149,166],"complex":[19,150],"working":[20],"environments":[21],"such":[22],"train":[24],"bearing":[25,107],"systems.":[26],"To":[27],"address":[28],"this":[29,66],"challenge,":[30],"incorporating":[31],"domain":[32],"knowledge":[33,43],"into":[34],"machine":[35,113,167],"learning":[36,69,111,114],"has":[38],"been":[39],"proposed.":[40],"Specifically,":[41],"prior":[42,82],"the":[45,76,81,97,118,132,154],"form":[46],"of":[47],"wavelet":[48],"packet":[49],"decomposition":[50],"and":[51,112],"information":[52],"entropy":[53],"features":[54],"was":[55,86,103],"extracted":[56],"to":[57],"characterize":[58],"fault-related":[59],"physical":[60],"phenomena.":[61],"Convolutional":[62],"neural":[63],"networks":[64],"supplemented":[65],"approach":[67,102],"by":[68],"general":[70],"features.":[71],"An":[72],"attention":[73],"mechanism":[74],"fused":[75],"two":[77],"feature":[78],"types,":[79],"emphasizing":[80],"knowledge.":[83],"XGBoost":[84],"classification":[85],"then":[87],"applied":[88],"for":[89,142,160],"discrimination.":[91],"Additionally,":[92],"a":[93,140],"self-training":[94],"method":[95,119,159],"semi-supervised":[96,133],"model":[98],"during":[99],"training.":[100],"The":[101],"evaluated":[104],"three":[106],"datasets":[108],"against":[109],"deep":[110],"baselines.":[115],"Results":[116],"showed":[117],"achieved":[120],"over":[121],"70%":[122],"accuracy":[123],"with":[124],"only":[125],"30":[126],"labeled":[127],"samples":[128],"per":[129],"class,":[130],"outperforming":[131],"baseline.":[134],"This":[135],"lightweight,":[136],"knowledge-driven":[137],"solution":[138],"provides":[139],"foundation":[141],"efficient":[143],"from":[146],"data":[148],"scenarios.":[152],"Overall,":[153],"study":[155],"demonstrated":[156],"an":[157],"effective":[158],"addressing":[161],"size":[164],"challenges":[165],"diagnosis.":[169]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":11}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
