{"id":"https://openalex.org/W2989656089","doi":"https://doi.org/10.3390/s19235160","title":"A Comparative Study of Fault Diagnosis for Train Door System: Traditional versus Deep Learning Approaches","display_name":"A Comparative Study of Fault Diagnosis for Train Door System: Traditional versus Deep Learning Approaches","publication_year":2019,"publication_date":"2019-11-25","ids":{"openalex":"https://openalex.org/W2989656089","doi":"https://doi.org/10.3390/s19235160","mag":"2989656089","pmid":"https://pubmed.ncbi.nlm.nih.gov/31775338"},"language":"en","primary_location":{"id":"doi:10.3390/s19235160","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s19235160","pdf_url":"https://www.mdpi.com/1424-8220/19/23/5160/pdf?version=1574931220","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1424-8220/19/23/5160/pdf?version=1574931220","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5014196695","display_name":"Seokju Ham","orcid":null},"institutions":[{"id":"https://openalex.org/I24456540","display_name":"Korea Aerospace University","ror":"https://ror.org/05jmm0651","country_code":"KR","type":"education","lineage":["https://openalex.org/I24456540"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seokju Ham","raw_affiliation_strings":["Department of Aerospace & Mechanical Engineering, Korea Aerospace University, Goyang-City 10540, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Aerospace & Mechanical Engineering, Korea Aerospace University, Goyang-City 10540, Korea","institution_ids":["https://openalex.org/I24456540"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062289477","display_name":"Seok\u2010Youn Han","orcid":null},"institutions":[{"id":"https://openalex.org/I4210161173","display_name":"Korea Railroad Research Institute","ror":"https://ror.org/04gzcxt97","country_code":"KR","type":"facility","lineage":["https://openalex.org/I2801339556","https://openalex.org/I4210144908","https://openalex.org/I4210161173","https://openalex.org/I4387152098"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seok-Youn Han","raw_affiliation_strings":["Urban Transit Research Group, Korea Railroad Research Institute, Uiwang-City 16105, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Urban Transit Research Group, Korea Railroad Research Institute, Uiwang-City 16105, Korea","institution_ids":["https://openalex.org/I4210161173"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048359989","display_name":"Seokgoo Kim","orcid":"https://orcid.org/0000-0003-1205-7736"},"institutions":[{"id":"https://openalex.org/I24456540","display_name":"Korea Aerospace University","ror":"https://ror.org/05jmm0651","country_code":"KR","type":"education","lineage":["https://openalex.org/I24456540"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seokgoo Kim","raw_affiliation_strings":["Department of Aerospace & Mechanical Engineering, Korea Aerospace University, Goyang-City 10540, Korea"],"raw_orcid":"https://orcid.org/0000-0003-1205-7736","affiliations":[{"raw_affiliation_string":"Department of Aerospace & Mechanical Engineering, Korea Aerospace University, Goyang-City 10540, Korea","institution_ids":["https://openalex.org/I24456540"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Hyung Jun Park","orcid":"https://orcid.org/0000-0003-1081-3067"},"institutions":[{"id":"https://openalex.org/I24456540","display_name":"Korea Aerospace University","ror":"https://ror.org/05jmm0651","country_code":"KR","type":"education","lineage":["https://openalex.org/I24456540"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hyung Jun Park","raw_affiliation_strings":["Department of Aerospace & Mechanical Engineering, Korea Aerospace University, Goyang-City 10540, Korea"],"raw_orcid":"https://orcid.org/0000-0003-1081-3067","affiliations":[{"raw_affiliation_string":"Department of Aerospace & Mechanical Engineering, Korea Aerospace University, Goyang-City 10540, Korea","institution_ids":["https://openalex.org/I24456540"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089956517","display_name":"Kee-Jun Park","orcid":null},"institutions":[{"id":"https://openalex.org/I4210161173","display_name":"Korea Railroad Research Institute","ror":"https://ror.org/04gzcxt97","country_code":"KR","type":"facility","lineage":["https://openalex.org/I2801339556","https://openalex.org/I4210144908","https://openalex.org/I4210161173","https://openalex.org/I4387152098"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Kee-Jun Park","raw_affiliation_strings":["Urban Transit Research Group, Korea Railroad Research Institute, Uiwang-City 16105, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Urban Transit Research Group, Korea Railroad Research Institute, Uiwang-City 16105, Korea","institution_ids":["https://openalex.org/I4210161173"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001505783","display_name":"Joo-Ho Choi","orcid":"https://orcid.org/0000-0002-1709-4491"},"institutions":[{"id":"https://openalex.org/I24456540","display_name":"Korea Aerospace University","ror":"https://ror.org/05jmm0651","country_code":"KR","type":"education","lineage":["https://openalex.org/I24456540"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Joo-Ho Choi","raw_affiliation_strings":["School of Aerospace & Mechanical Engineering, Korea Aerospace University, Goyang-City 10540, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Aerospace & Mechanical Engineering, Korea Aerospace University, Goyang-City 10540, Korea","institution_ids":["https://openalex.org/I24456540"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5001505783"],"corresponding_institution_ids":["https://openalex.org/I24456540"],"apc_list":{"value":2400,"currency":"CHF","value_usd":2673},"apc_paid":{"value":2400,"currency":"CHF","value_usd":2673},"fwci":1.6768,"has_fulltext":false,"cited_by_count":27,"citation_normalized_percentile":{"value":0.83659479,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"19","issue":"23","first_page":"5160","last_page":"5160"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9940000176429749,"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"}},{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9807000160217285,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6573208570480347},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6455082297325134},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6421945095062256},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.6395213007926941},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5896328091621399},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.58708655834198},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5835672616958618},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5707514882087708},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.5495296716690063},{"id":"https://openalex.org/keywords/signal-processing","display_name":"Signal processing","score":0.5433465242385864},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.49791812896728516},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4937196671962738},{"id":"https://openalex.org/keywords/time-domain","display_name":"Time domain","score":0.44274261593818665},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.43509313464164734},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35963380336761475},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.16843417286872864},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.09227132797241211}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6573208570480347},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6455082297325134},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6421945095062256},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.6395213007926941},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5896328091621399},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.58708655834198},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5835672616958618},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5707514882087708},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.5495296716690063},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.5433465242385864},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.49791812896728516},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4937196671962738},{"id":"https://openalex.org/C103824480","wikidata":"https://www.wikidata.org/wiki/Q185889","display_name":"Time domain","level":2,"score":0.44274261593818665},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.43509313464164734},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35963380336761475},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.16843417286872864},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.09227132797241211},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","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":4,"locations":[{"id":"doi:10.3390/s19235160","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s19235160","pdf_url":"https://www.mdpi.com/1424-8220/19/23/5160/pdf?version=1574931220","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},{"id":"pmid:31775338","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/31775338","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:europepmc.org:5881573","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6928885","pdf_url":null,"source":{"id":"https://openalex.org/S4306400806","display_name":"Europe PMC (PubMed Central)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1303153112","host_organization_name":"European Bioinformatics Institute","host_organization_lineage":["https://openalex.org/I1303153112"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Text"},{"id":"pmh:oai:mdpi.com:/1424-8220/19/23/5160/","is_oa":true,"landing_page_url":"http://dx.doi.org/10.3390/s19235160","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/s19235160","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s19235160","pdf_url":"https://www.mdpi.com/1424-8220/19/23/5160/pdf?version=1574931220","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.47999998927116394,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"},{"score":0.4300000071525574,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W2989656089.pdf"},"referenced_works_count":22,"referenced_works":["https://openalex.org/W11840831","https://openalex.org/W351966126","https://openalex.org/W2024285454","https://openalex.org/W2030678516","https://openalex.org/W2079025339","https://openalex.org/W2225563181","https://openalex.org/W2325226967","https://openalex.org/W2337673319","https://openalex.org/W2461729787","https://openalex.org/W2479071027","https://openalex.org/W2569032810","https://openalex.org/W2607863109","https://openalex.org/W2805029945","https://openalex.org/W2805975258","https://openalex.org/W2810292802","https://openalex.org/W2857396412","https://openalex.org/W2896877450","https://openalex.org/W2907871993","https://openalex.org/W2953430873","https://openalex.org/W2973941913","https://openalex.org/W3163858678","https://openalex.org/W6669992824"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W4391621807","https://openalex.org/W2611989081","https://openalex.org/W4321487865","https://openalex.org/W4313906399","https://openalex.org/W4391621790","https://openalex.org/W4226493464","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983"],"abstract_inverted_index":{"A":[0,20],"fault":[1,28,53,183],"diagnosis":[2],"of":[3,44,78,153,158,181],"a":[4],"train":[5],"door":[6],"system":[7],"is":[8,23,59,90,115,122,139,148,164,195],"carried":[9],"out":[10],"using":[11],"the":[12,18,39,52,60,69,76,91,97,104,110,119,125,131,135,143,161,169,178,186,191,199],"motor":[13],"current":[14,120],"signal":[15,74,121],"that":[16,64,168],"operates":[17],"door.":[19],"test":[21],"rig":[22],"prepared,":[24],"in":[25,151,156,167,190],"which":[26,129,147,194],"various":[27],"modes":[29],"are":[30,49],"examined":[31],"by":[32,82,95,142,198],"applying":[33],"extreme":[34],"conditions,":[35],"as":[36,38,73],"well":[37],"natural":[40],"and":[41,85,184],"artificial":[42],"wears":[43],"critical":[45],"components.":[46],"Two":[47],"approaches":[48],"undertaken":[50],"toward":[51],"classification":[54],"for":[55,68],"comparative":[56],"purposes:":[57],"one":[58],"traditional":[61,111,162],"feature-based":[62],"method":[63],"requires":[65],"several":[66],"steps":[67],"processing":[70,171],"features":[71,106,170],"such":[72],"segmentation,":[75],"extraction":[77,107],"time-domain":[79],"features,":[80],"selection":[81],"Fisher's":[83],"discrimination,":[84],"K-nearest":[86],"neighbor.":[87],"The":[88],"other":[89],"deep":[92,200],"learning":[93,201],"approach":[94,163],"employing":[96],"convolutional":[98],"neural":[99],"network":[100],"(CNN)":[101],"to":[102,176],"skip":[103],"hand-crafted":[105],"process.":[108],"In":[109,134],"approach,":[112],"good":[113],"accuracy":[114,138],"found":[116],"only":[117],"after":[118],"segmented":[123],"into":[124],"three":[126],"velocity":[127],"regimes,":[128],"enhances":[130],"discrimination":[132],"capability.":[133],"CNN,":[136],"superior":[137],"obtained":[140],"even":[141],"original":[144],"raw":[145],"signal,":[146],"more":[149,165],"convenient":[150],"terms":[152],"implementation.":[154],"However,":[155],"view":[157],"practical":[159],"applications,":[160],"useful":[166],"can":[172],"be":[173],"easily":[174],"applied":[175],"assess":[177],"health":[179],"state":[180],"each":[182],"monitor":[185],"progression":[187],"over":[188],"time":[189],"real":[192],"operation,":[193],"not":[196],"enabled":[197],"approach.":[202]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":2}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}