{"id":"https://openalex.org/W2938959211","doi":"https://doi.org/10.1109/vtcfall.2018.8690710","title":"Diagnostic and Prediction of Machines Health Status as Exemplary Best Practice for Vehicle Production System","display_name":"Diagnostic and Prediction of Machines Health Status as Exemplary Best Practice for Vehicle Production System","publication_year":2018,"publication_date":"2018-08-01","ids":{"openalex":"https://openalex.org/W2938959211","doi":"https://doi.org/10.1109/vtcfall.2018.8690710","mag":"2938959211"},"language":"en","primary_location":{"id":"doi:10.1109/vtcfall.2018.8690710","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtcfall.2018.8690710","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE 88th Vehicular Technology Conference (VTC-Fall)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5113503581","display_name":"Ren C. Luo","orcid":null},"institutions":[{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Ren C. Luo","raw_affiliation_strings":["International Center of Excellence in Intelligent Robotics and Automation Research, National Taiwan University, Taipei, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"International Center of Excellence in Intelligent Robotics and Automation Research, National Taiwan University, Taipei, Taiwan","institution_ids":["https://openalex.org/I16733864"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012720408","display_name":"Hao Wang","orcid":"https://orcid.org/0000-0001-7961-7588"},"institutions":[{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Hao Wang","raw_affiliation_strings":["International Center of Excellence in Intelligent Robotics and Automation Research, National Taiwan University, Taipei, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"International Center of Excellence in Intelligent Robotics and Automation Research, National Taiwan University, Taipei, Taiwan","institution_ids":["https://openalex.org/I16733864"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I16733864"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9871000051498413,"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"}},"topics":[{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9871000051498413,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9671000242233276,"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/T13344","display_name":"Industrial Automation and Control Systems","score":0.9498000144958496,"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-neural-network","display_name":"Artificial neural network","score":0.6860624551773071},{"id":"https://openalex.org/keywords/production-line","display_name":"Production line","score":0.6756316423416138},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.6118776202201843},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5835850834846497},{"id":"https://openalex.org/keywords/production","display_name":"Production (economics)","score":0.5519713759422302},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5444411635398865},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4988546371459961},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.30450239777565},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.29622682929039}],"concepts":[{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6860624551773071},{"id":"https://openalex.org/C99862985","wikidata":"https://www.wikidata.org/wiki/Q10858068","display_name":"Production line","level":2,"score":0.6756316423416138},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.6118776202201843},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5835850834846497},{"id":"https://openalex.org/C2778348673","wikidata":"https://www.wikidata.org/wiki/Q739302","display_name":"Production (economics)","level":2,"score":0.5519713759422302},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5444411635398865},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4988546371459961},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.30450239777565},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.29622682929039},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C139719470","wikidata":"https://www.wikidata.org/wiki/Q39680","display_name":"Macroeconomics","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vtcfall.2018.8690710","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtcfall.2018.8690710","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE 88th Vehicular Technology Conference (VTC-Fall)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.46000000834465027,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2029608738","https://openalex.org/W2072559058","https://openalex.org/W2148704750","https://openalex.org/W2167580470","https://openalex.org/W2276864128","https://openalex.org/W2285057661","https://openalex.org/W2440500672","https://openalex.org/W2547081821","https://openalex.org/W2548936536","https://openalex.org/W2609808309","https://openalex.org/W2770172507","https://openalex.org/W2770547749","https://openalex.org/W2773638716","https://openalex.org/W2785946419","https://openalex.org/W2787243182","https://openalex.org/W2810575300"],"related_works":["https://openalex.org/W4390608645","https://openalex.org/W4247566972","https://openalex.org/W2960264696","https://openalex.org/W3090563135","https://openalex.org/W4313286066","https://openalex.org/W2370727940","https://openalex.org/W2357205996","https://openalex.org/W3196672582","https://openalex.org/W2359700625","https://openalex.org/W2043374901"],"abstract_inverted_index":{"Diagnosis":[0],"and":[1,41,141,164,168],"prediction":[2,169],"of":[3,7,24,26,34,48,65,88,103,112,123,133,145,170],"the":[4,14,22,62,85,93,97,109,113,131,134,137,142,146,162],"health":[5,66,86,110,172],"status":[6,87,111],"vehicle":[8,69],"components":[9],"production":[10,35,70,89],"line":[11,36,90],"machine":[12],"is":[13,149],"core":[15],"requirement":[16,64],"for":[17,68,166],"global":[18],"manufacturing":[19],"system.":[20],"With":[21],"development":[23,47],"Internet":[25],"things":[27],"(IoT),":[28],"there":[29],"are":[30,105],"enormous":[31],"big":[32,57],"data":[33,58,94,139],"could":[37],"be":[38],"collected":[39],"quickly":[40],"stored":[42],"in":[43,115],"large":[44,138],"quantities.":[45],"The":[46,99],"artificial":[49,124],"intelligence":[50],"makes":[51],"it":[52,148,152],"possible":[53],"to":[54,61,83,107,129,160],"deal":[55],"with":[56],"efficiently.":[59],"Due":[60],"industrial":[63],"self-diagnosis":[67],"line,":[71],"this":[72],"paper":[73],"presents":[74],"a":[75,116],"method":[76],"based":[77],"on":[78],"Artificial":[79],"Neural":[80],"Network":[81],"(ANN)":[82,127],"diagnose":[84],"machines":[91,114,171],"using":[92],"produced":[95],"by":[96],"machines.":[98],"PID":[100],"control":[101],"parameters":[102],"motors":[104],"segmented":[106],"simulate":[108],"long":[117],"duration.":[118],"We":[119],"use":[120],"three":[121],"kinds":[122],"neural":[125],"network":[126],"methods":[128],"train":[130],"model":[132],"relationship":[135],"between":[136],"trend":[140],"diagnostic":[143,167],"score":[144],"machine,":[147],"demonstrated":[150],"that":[151],"becomes":[153],"more":[154],"efficient":[155],"than":[156],"traditional":[157],"empirical":[158],"analysis":[159],"improve":[161],"speed":[163],"accuracy":[165],"status.":[173]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
