{"id":"https://openalex.org/W3205719659","doi":"https://doi.org/10.1109/tits.2021.3116966","title":"Fault Prediction of Railway Turnout Systems Based on Improved Sparse Auto Encoder and Gated Recurrent Unit Network","display_name":"Fault Prediction of Railway Turnout Systems Based on Improved Sparse Auto Encoder and Gated Recurrent Unit Network","publication_year":2021,"publication_date":"2021-10-19","ids":{"openalex":"https://openalex.org/W3205719659","doi":"https://doi.org/10.1109/tits.2021.3116966","mag":"3205719659"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2021.3116966","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2021.3116966","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"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 Intelligent Transportation Systems","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/A5100447000","display_name":"Yao Zhang","orcid":"https://orcid.org/0000-0002-9602-452X"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yao Zhang","raw_affiliation_strings":["State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-9602-452X","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101553132","display_name":"Yu Cheng","orcid":"https://orcid.org/0000-0001-9746-3068"},"institutions":[{"id":"https://openalex.org/I4210141966","display_name":"China Academy of Railway Sciences","ror":"https://ror.org/051wv2j09","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210141966"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Cheng","raw_affiliation_strings":["Infrastructure Inspection Research Institute, China Academy of Railway Sciences Corporation Ltd., Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Infrastructure Inspection Research Institute, China Academy of Railway Sciences Corporation Ltd., Beijing, China","institution_ids":["https://openalex.org/I4210141966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101987278","display_name":"Tianhua Xu","orcid":"https://orcid.org/0000-0003-3720-4130"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianhua Xu","raw_affiliation_strings":["State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-3720-4130","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100451759","display_name":"Guang Wang","orcid":"https://orcid.org/0000-0002-7739-7945"},"institutions":[{"id":"https://openalex.org/I102322142","display_name":"Rutgers, The State University of New Jersey","ror":"https://ror.org/05vt9qd57","country_code":"US","type":"education","lineage":["https://openalex.org/I102322142"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Guang Wang","raw_affiliation_strings":["Department of Computer Science, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA"],"raw_orcid":"https://orcid.org/0000-0002-7739-7945","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA","institution_ids":["https://openalex.org/I102322142"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100427094","display_name":"Cong Chen","orcid":"https://orcid.org/0000-0001-5035-1026"},"institutions":[{"id":"https://openalex.org/I4210118977","display_name":"Shanghai Tunnel Engineering Rail Transit Design & Research Institute","ror":"https://ror.org/02zznv955","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210118977"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cong Chen","raw_affiliation_strings":["CRSC Urban Rail Transit Technology Company, Ltd., Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CRSC Urban Rail Transit Technology Company, Ltd., Beijing, China","institution_ids":["https://openalex.org/I4210118977"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103168967","display_name":"Tiantian Yang","orcid":"https://orcid.org/0000-0002-0148-396X"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tiantian Yang","raw_affiliation_strings":["State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.2057,"has_fulltext":false,"cited_by_count":28,"citation_normalized_percentile":{"value":0.86947215,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"23","issue":"8","first_page":"12711","last_page":"12723"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10842","display_name":"Railway Engineering and Dynamics","score":0.9972000122070312,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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/T10842","display_name":"Railway Engineering and Dynamics","score":0.9972000122070312,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9944000244140625,"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/T11568","display_name":"Railway Systems and Energy Efficiency","score":0.972000002861023,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/computer-science","display_name":"Computer science","score":0.5691474676132202},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5222357511520386},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4705377519130707},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4449813961982727},{"id":"https://openalex.org/keywords/deep-belief-network","display_name":"Deep belief network","score":0.42513740062713623},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.41893208026885986},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.3921474814414978},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39022213220596313},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38397714495658875},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.25429585576057434}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5691474676132202},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5222357511520386},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4705377519130707},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4449813961982727},{"id":"https://openalex.org/C97385483","wikidata":"https://www.wikidata.org/wiki/Q16954980","display_name":"Deep belief network","level":3,"score":0.42513740062713623},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.41893208026885986},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3921474814414978},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39022213220596313},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38397714495658875},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.25429585576057434},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","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/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","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},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2021.3116966","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2021.3116966","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"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 Intelligent Transportation Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.5799999833106995}],"awards":[{"id":"https://openalex.org/G4014571494","display_name":null,"funder_award_id":"U1934219","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W1863471034","https://openalex.org/W1973665104","https://openalex.org/W1980513886","https://openalex.org/W1981064558","https://openalex.org/W1995245587","https://openalex.org/W2006169073","https://openalex.org/W2008981782","https://openalex.org/W2019505419","https://openalex.org/W2026980120","https://openalex.org/W2110658845","https://openalex.org/W2164092415","https://openalex.org/W2341973567","https://openalex.org/W2342099476","https://openalex.org/W2539054576","https://openalex.org/W2558109502","https://openalex.org/W2560612936","https://openalex.org/W2575828067","https://openalex.org/W2582724488","https://openalex.org/W2735432884","https://openalex.org/W2743340515","https://openalex.org/W2761699219","https://openalex.org/W2767251665","https://openalex.org/W2773549135","https://openalex.org/W2798854001","https://openalex.org/W2892598691","https://openalex.org/W2899717669","https://openalex.org/W2904009550","https://openalex.org/W2904518811","https://openalex.org/W2911097190","https://openalex.org/W2917169831","https://openalex.org/W2944451552","https://openalex.org/W2964150627","https://openalex.org/W2981680696","https://openalex.org/W3017805480","https://openalex.org/W3042826099","https://openalex.org/W6722441410","https://openalex.org/W6725733176","https://openalex.org/W6756688618"],"related_works":["https://openalex.org/W2906214541","https://openalex.org/W4367367217","https://openalex.org/W2133034788","https://openalex.org/W2897826427","https://openalex.org/W2792607040","https://openalex.org/W1996690921","https://openalex.org/W2888309058","https://openalex.org/W3135822842","https://openalex.org/W3004931343","https://openalex.org/W3195479793"],"abstract_inverted_index":{"Railway":[0,223],"turnout":[1],"systems":[2],"(RTS),":[3],"as":[4],"one":[5,104],"of":[6,12,38,51,59,74,105,110,147,172,197,256],"the":[7,13,19,48,71,75,95,106,125,139,145,148,159,169,175,185,191,202,221,231,236,240,243],"most":[8,107],"critical":[9],"ground":[10],"infrastructures":[11],"high-speed":[14],"rail,":[15],"which":[16,102,165,227],"directly":[17],"impact":[18],"railway":[20],"operation":[21,49],"safety,":[22],"are":[23,40,199,211],"also":[24],"susceptible":[25],"to":[26,42,62,137,143,157,183,230,251],"frequent":[27,108],"faults.":[28],"Hence,":[29],"efficient":[30],"fault":[31,57,97,192,233],"prediction":[32,58,193,248],"and":[33,46,64,77,87,112,162,189,239],"intelligent":[34],"maintenance":[35],"decision":[36],"support":[37],"RTS":[39,60,216],"significant":[41],"avoid":[43],"unexpected":[44],"outages":[45],"guarantee":[47],"safety":[50],"trains.":[52],"A":[53],"key":[54],"challenge":[55],"for":[56],"is":[61,103,135,155,181,228],"extract":[63],"select":[65],"effective":[66],"feature":[67,120,160],"combinations":[68],"that":[69],"describe":[70,168],"degradation":[72,170,217],"status":[73,171],"system":[76],"then":[78],"generate":[79,163],"health":[80],"indicators":[81],"(HI)":[82],"with":[83],"high":[84],"monotonicity,":[85],"correlation,":[86],"robustness.":[88],"In":[89],"this":[90],"paper,":[91],"we":[92,113],"focus":[93],"on":[94,124,213],"jam":[96,232],"caused":[98,234],"by":[99,201,235],"insufficient":[100],"lubrication,":[101],"faults":[109],"RTS,":[111],"present":[114],"a":[115,214],"novel":[116],"unsupervised":[117],"adaptive":[118],"latent":[119,141],"extraction":[121],"method":[122,245],"based":[123],"improved":[126,203],"sparse":[127],"auto-encoder":[128],"(ISAE).":[129],"The":[130,195],"locally":[131],"weighted":[132],"regression":[133,153],"(LOESS)":[134],"performed":[136],"smooth":[138],"extracted":[140],"features":[142],"overcome":[144],"influence":[146],"noise.":[149],"Improved":[150],"auto-associative":[151],"kernel":[152],"(IAAKR)":[154],"utilized":[156],"fuse":[158],"combination":[161],"HI,":[164],"can":[166],"intuitively":[167],"RTS.":[173],"Finally,":[174],"gated":[176],"recurrent":[177],"unit":[178],"(GRU)":[179],"network":[180],"introduced":[182],"enhance":[184],"correlation":[186],"data":[187],"process":[188],"construct":[190],"models.":[194],"parameters":[196],"GRU":[198],"optimized":[200],"particle":[204],"swarm":[205],"optimization":[206],"(IPSO)":[207],"algorithm.":[208],"Extensive":[209],"experiments":[210],"conducted":[212],"real-world":[215],"dataset":[218],"collected":[219],"from":[220],"Changsha":[222],"Station":[224],"in":[225,254],"China,":[226],"related":[229],"lubrication":[237],"reduction,":[238],"results":[241],"show":[242],"proposed":[244],"achieves":[246],"better":[247],"performance":[249],"compared":[250],"state-of-the-art":[252],"methods":[253],"terms":[255],"different":[257],"evaluation":[258],"metrics.":[259]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}