{"id":"https://openalex.org/W4400116362","doi":"https://doi.org/10.3233/idt-230756","title":"Pantograph fault prediction of urban rail transit vehicles based on edge feature extraction and detection","display_name":"Pantograph fault prediction of urban rail transit vehicles based on edge feature extraction and detection","publication_year":2024,"publication_date":"2024-06-28","ids":{"openalex":"https://openalex.org/W4400116362","doi":"https://doi.org/10.3233/idt-230756"},"language":"en","primary_location":{"id":"doi:10.3233/idt-230756","is_oa":false,"landing_page_url":"https://doi.org/10.3233/idt-230756","pdf_url":null,"source":{"id":"https://openalex.org/S119727669","display_name":"Intelligent Decision Technologies","issn_l":"1872-4981","issn":["1872-4981","1875-8843"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Intelligent Decision Technologies","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/A5101018471","display_name":"Xiaofang Feng","orcid":null},"institutions":[{"id":"https://openalex.org/I90090648","display_name":"Shijiazhuang Tiedao University","ror":"https://ror.org/022e9e065","country_code":"CN","type":"education","lineage":["https://openalex.org/I90090648"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaofang Feng","raw_affiliation_strings":["Railway Transportation Department, Hebei Vocational College of Rail Transportation, Shijiazhuang, Hebei, China"],"affiliations":[{"raw_affiliation_string":"Railway Transportation Department, Hebei Vocational College of Rail Transportation, Shijiazhuang, Hebei, China","institution_ids":["https://openalex.org/I90090648"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101486240","display_name":"Liping Li","orcid":"https://orcid.org/0000-0003-0947-757X"},"institutions":[{"id":"https://openalex.org/I90090648","display_name":"Shijiazhuang Tiedao University","ror":"https://ror.org/022e9e065","country_code":"CN","type":"education","lineage":["https://openalex.org/I90090648"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Liping Li","raw_affiliation_strings":["Railway Transportation Department, Hebei Vocational College of Rail Transportation, Shijiazhuang, Hebei, China"],"affiliations":[{"raw_affiliation_string":"Railway Transportation Department, Hebei Vocational College of Rail Transportation, Shijiazhuang, Hebei, China","institution_ids":["https://openalex.org/I90090648"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078610325","display_name":"Qing Chen","orcid":"https://orcid.org/0000-0002-2191-6558"},"institutions":[{"id":"https://openalex.org/I90090648","display_name":"Shijiazhuang Tiedao University","ror":"https://ror.org/022e9e065","country_code":"CN","type":"education","lineage":["https://openalex.org/I90090648"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qing Chen","raw_affiliation_strings":["Railway Transportation Department, Hebei Vocational College of Rail Transportation, Shijiazhuang, Hebei, China"],"affiliations":[{"raw_affiliation_string":"Railway Transportation Department, Hebei Vocational College of Rail Transportation, Shijiazhuang, Hebei, China","institution_ids":["https://openalex.org/I90090648"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101486240"],"corresponding_institution_ids":["https://openalex.org/I90090648"],"apc_list":null,"apc_paid":null,"fwci":0.429,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.56052775,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":"18","issue":"3","first_page":"2607","last_page":"2619"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12371","display_name":"Electrical Contact Performance and Analysis","score":0.9995999932289124,"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/T12371","display_name":"Electrical Contact Performance and Analysis","score":0.9995999932289124,"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/T11568","display_name":"Railway Systems and Energy Efficiency","score":0.9958000183105469,"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"}},{"id":"https://openalex.org/T10842","display_name":"Railway Engineering and Dynamics","score":0.9901999831199646,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/pantograph","display_name":"Pantograph","score":0.734052300453186},{"id":"https://openalex.org/keywords/urban-rail-transit","display_name":"Urban rail transit","score":0.6563257575035095},{"id":"https://openalex.org/keywords/extraction","display_name":"Extraction (chemistry)","score":0.6406674385070801},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6313124299049377},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.5557109713554382},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.5364452600479126},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5185316205024719},{"id":"https://openalex.org/keywords/fault-detection-and-isolation","display_name":"Fault detection and isolation","score":0.5051255822181702},{"id":"https://openalex.org/keywords/automotive-engineering","display_name":"Automotive engineering","score":0.45255908370018005},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3392762839794159},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.3106265664100647},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.30649179220199585},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.20876100659370422},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.10768759250640869},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.07663741707801819},{"id":"https://openalex.org/keywords/chromatography","display_name":"Chromatography","score":0.061058998107910156},{"id":"https://openalex.org/keywords/mechanical-engineering","display_name":"Mechanical engineering","score":0.051472246646881104}],"concepts":[{"id":"https://openalex.org/C20756127","wikidata":"https://www.wikidata.org/wiki/Q722757","display_name":"Pantograph","level":2,"score":0.734052300453186},{"id":"https://openalex.org/C2780434240","wikidata":"https://www.wikidata.org/wiki/Q3491904","display_name":"Urban rail transit","level":2,"score":0.6563257575035095},{"id":"https://openalex.org/C4725764","wikidata":"https://www.wikidata.org/wiki/Q844704","display_name":"Extraction (chemistry)","level":2,"score":0.6406674385070801},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6313124299049377},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.5557109713554382},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.5364452600479126},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5185316205024719},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.5051255822181702},{"id":"https://openalex.org/C171146098","wikidata":"https://www.wikidata.org/wiki/Q124192","display_name":"Automotive engineering","level":1,"score":0.45255908370018005},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3392762839794159},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3106265664100647},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.30649179220199585},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.20876100659370422},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.10768759250640869},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.07663741707801819},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.061058998107910156},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.051472246646881104},{"id":"https://openalex.org/C165205528","wikidata":"https://www.wikidata.org/wiki/Q83371","display_name":"Seismology","level":1,"score":0.0},{"id":"https://openalex.org/C172707124","wikidata":"https://www.wikidata.org/wiki/Q423488","display_name":"Actuator","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/idt-230756","is_oa":false,"landing_page_url":"https://doi.org/10.3233/idt-230756","pdf_url":null,"source":{"id":"https://openalex.org/S119727669","display_name":"Intelligent Decision Technologies","issn_l":"1872-4981","issn":["1872-4981","1875-8843"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Intelligent Decision Technologies","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.5099999904632568,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W2894549562","https://openalex.org/W2921725755","https://openalex.org/W3016977684","https://openalex.org/W4311122511","https://openalex.org/W4319862285","https://openalex.org/W4385345279","https://openalex.org/W4385893944","https://openalex.org/W6775938671"],"related_works":["https://openalex.org/W2573375108","https://openalex.org/W4252833549","https://openalex.org/W2378529747","https://openalex.org/W4387011711","https://openalex.org/W2372731345","https://openalex.org/W2057677727","https://openalex.org/W2360526382","https://openalex.org/W2379657325","https://openalex.org/W2136457064","https://openalex.org/W2787802451"],"abstract_inverted_index":{"Nowadays,":[0],"as":[1],"an":[2,62],"indispensable":[3],"part":[4],"of":[5,20,35,51,58,80,96,99,110,116,153,194,204,224,235,242],"urban":[6,8,67],"infrastructure,":[7],"rail":[9,68,81,136,168,176,212],"transit":[10,69,82,137,169,177,213],"(URT)":[11],"vehicles":[12,70,83],"have":[13],"also":[14],"developed":[15],"rapidly.":[16],"A":[17],"large":[18],"amount":[19],"manpower,":[21],"material":[22],"resources,":[23],"and":[24,54,107,127,143,201,237],"financial":[25],"resources":[26],"need":[27],"to":[28,71],"be":[29],"invested":[30],"in":[31,113],"the":[32,56,76,78,90,94,114,134,150,154,166,175,202,222,233,239],"construction":[33],"process":[34,241],"URT.":[36,243],"For":[37,92],"URT":[38,59,118,236],"vehicles,":[39],"research":[40],"on":[41,104],"more":[42],"accurate":[43],"fault":[44,100,111,121,140,172,180,185,205,216,227],"prediction":[45,112,122,141,173,181,186,206,217],"methods":[46],"can":[47,159,219,230],"save":[48],"a":[49,161],"lot":[50],"maintenance":[52],"costs":[53],"improve":[55,221],"reliability":[57],"construction.":[60],"As":[61],"important":[63],"electrical":[64],"equipment":[65],"for":[66,89],"obtain":[72],"electric":[73],"energy":[74],"from":[75],"catenary,":[77],"operation":[79],"puts":[84],"forward":[85],"higher":[86,184,197],"performance":[87],"requirements":[88],"pantograph.":[91],"solving":[93],"problems":[95],"low":[97],"accuracy":[98,203,223],"prediction,":[101,228],"over":[102],"reliance":[103],"practical":[105],"experience":[106],"high":[108],"cost":[109],"application":[115,199],"traditional":[117,135,167],"vehicle":[119,138,170,178,214,225],"pantograph":[120,139,171,179,195,215,226],"model.":[123],"Combining":[124],"sensor":[125],"network":[126],"artificial":[128],"intelligence":[129],"algorithm,":[130],"this":[131,157],"paper":[132,158],"analyzed":[133],"model,":[142,174],"verified":[144],"it":[145],"through":[146],"comparative":[147,151],"experiments.":[148],"Through":[149],"analysis":[152],"experimental":[155],"results,":[156],"draw":[160],"conclusion":[162],"that":[163],"compared":[164],"with":[165],"model":[182,189,198,218],"has":[183],"accuracy,":[187],"less":[188],"response":[190],"time,":[191],"lower":[192],"risk":[193],"failure,":[196],"satisfaction,":[200],"increased":[207],"by":[208],"about":[209],"6.6%.":[210],"The":[211],"effectively":[220],"which":[229],"greatly":[231],"promote":[232,238],"safety":[234],"intelligent":[240]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2026-03-25T14:56:36.534964","created_date":"2025-10-10T00:00:00"}
