{"id":"https://openalex.org/W2813428987","doi":"https://doi.org/10.1109/ccdc.2018.8407419","title":"Improved DAE and application in fault diagnosis","display_name":"Improved DAE and application in fault diagnosis","publication_year":2018,"publication_date":"2018-06-01","ids":{"openalex":"https://openalex.org/W2813428987","doi":"https://doi.org/10.1109/ccdc.2018.8407419","mag":"2813428987"},"language":"en","primary_location":{"id":"doi:10.1109/ccdc.2018.8407419","doi":"https://doi.org/10.1109/ccdc.2018.8407419","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ccdc.2018.8407419","pdf_url":null,"source":{"id":"https://openalex.org/S4363607715","display_name":"Chinese Control and Decision Conference (CCDC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 Chinese Control And Decision Conference (CCDC)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5079950677","display_name":"Funa Zhou","orcid":"https://orcid.org/0000-0003-3592-9664","observed_orcids":["https://orcid.org/0000-0003-3592-9664"]},"institutions":[{"id":"https://openalex.org/I173899330","display_name":"Henan University","ror":"https://ror.org/003xyzq10","country_code":"CN","type":"education","lineage":["https://openalex.org/I173899330"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Funa Zhou","raw_affiliation_strings":["School of Computer and Information Engineering, Henan University, Kaifeng, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer and Information Engineering, Henan University, Kaifeng, China","institution_ids":["https://openalex.org/I173899330"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086263105","display_name":"Shuai Yang","orcid":"https://orcid.org/0000-0001-8836-4236","observed_orcids":["https://orcid.org/0000-0001-8836-4236"]},"institutions":[{"id":"https://openalex.org/I173899330","display_name":"Henan University","ror":"https://ror.org/003xyzq10","country_code":"CN","type":"education","lineage":["https://openalex.org/I173899330"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuai Yang","raw_affiliation_strings":["School of Computer and Information Engineering, Henan University, Kaifeng, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer and Information Engineering, Henan University, Kaifeng, China","institution_ids":["https://openalex.org/I173899330"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086718724","display_name":"Chenglin Wen","orcid":"https://orcid.org/0000-0003-2471-2849","observed_orcids":["https://orcid.org/0000-0003-2471-2849"]},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenglin Wen","raw_affiliation_strings":["School of Automation, Hangzhou Dianzi University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation, Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5114233761","display_name":"Haixiang Ju","orcid":"https://orcid.org/0009-0009-3778-3495","observed_orcids":["https://orcid.org/0009-0009-3778-3495"]},"institutions":[{"id":"https://openalex.org/I55240360","display_name":"Yeungnam University","ror":"https://ror.org/05yc6p159","country_code":"KR","type":"education","lineage":["https://openalex.org/I55240360"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"H. Park Ju","raw_affiliation_strings":["Yeungnam University, Gyeongsan, Gyeongsangbuk-do, KR"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yeungnam University, Gyeongsan, Gyeongsangbuk-do, KR","institution_ids":["https://openalex.org/I55240360"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.05073171,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":93},"biblio":{"volume":"pp","issue":null,"first_page":"1801","last_page":"1806"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":1.0,"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":1.0,"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/T10876","display_name":"Fault Detection and Control Systems","score":9.999999747378752e-05,"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/T10320","display_name":"Neural Networks and Applications","score":0.0,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/stacked-denoising-autoencoder","display_name":"stacked denoising autoencoder","score":0.9279999732971191},{"id":"https://openalex.org/keywords/time-varying-learning-rate","display_name":"time-varying learning rate","score":0.9399999976158142},{"id":"https://openalex.org/keywords/learning-rate-optimization","display_name":"learning rate optimization","score":0.5960000157356262},{"id":"https://openalex.org/keywords/fault-diagnosis","display_name":"fault diagnosis","score":0.8180000185966492},{"id":"https://openalex.org/keywords/fault-classification","display_name":"fault classification","score":0.9629999995231628},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"feature extraction","score":0.7179999947547913},{"id":"https://openalex.org/keywords/deep-neural-network","display_name":"deep neural network","score":0.7760000228881836}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.750210165977478},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7185546159744263},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6029180884361267},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5850532054901123},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5610743761062622},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.5160355567932129},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.43828457593917847},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.41312751173973083},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40422606468200684},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33669835329055786},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","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},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ccdc.2018.8407419","doi":"https://doi.org/10.1109/ccdc.2018.8407419","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ccdc.2018.8407419","pdf_url":null,"source":{"id":"https://openalex.org/S4363607715","display_name":"Chinese Control and Decision Conference (CCDC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 Chinese Control And Decision Conference (CCDC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1600547571","https://openalex.org/W2000961175","https://openalex.org/W2004355430","https://openalex.org/W2083022762","https://openalex.org/W2100495367","https://openalex.org/W2102017903","https://openalex.org/W2126894374","https://openalex.org/W2145094598","https://openalex.org/W2184192902","https://openalex.org/W2253401090","https://openalex.org/W2356671626","https://openalex.org/W2738563279","https://openalex.org/W2768753204","https://openalex.org/W2886200610","https://openalex.org/W2887076107"],"related_works":["https://openalex.org/W2808162871","https://openalex.org/W4387197099","https://openalex.org/W3011793324","https://openalex.org/W3112316981","https://openalex.org/W2897882464","https://openalex.org/W2898597600","https://openalex.org/W2915377492","https://openalex.org/W3114578509","https://openalex.org/W2972463160","https://openalex.org/W2485614840"],"abstract_inverted_index":{"Deep":[0],"neural":[1,26],"network":[2],"(DNN)":[3],"with":[4],"powerful":[5],"feature":[6],"learning":[7,32,44,50],"ability":[8],"is":[9,18,52],"widely":[10],"used":[11],"in":[12,54],"fault":[13,87,98,107],"diagnosis.":[14],"Stacked":[15],"de-noising":[16],"auto-encoder(DAE)":[17],"a":[19,24],"common":[20],"way":[21],"to":[22],"achieve":[23],"deep":[25],"network.":[27],"In":[28],"this":[29],"paper,":[30],"the":[31,41,46,55,60,63,69,74,80,93,104],"rate":[33,51],"of":[34,48,62,66,103],"DAE":[35,67],"has":[36],"been":[37],"improved.":[38],"Different":[39],"from":[40],"traditional":[42],"fixed":[43],"rate,":[45,77],"idea":[47],"time-varying":[49],"adopted":[53],"paper":[56],"which":[57],"effectively":[58],"reduces":[59],"oscillation":[61],"cost":[64],"function":[65],"during":[68],"iterative":[70],"process":[71],"based":[72,106],"on":[73],"guaranteed":[75],"iteration":[76],"so":[78],"that":[79,92],"extracted":[81],"features":[82],"are":[83],"more":[84],"favorable":[85],"for":[86],"classification.":[88],"Experimental":[89],"results":[90],"show":[91],"proposed":[94],"method":[95],"can":[96],"improve":[97],"classification":[99],"accuracy":[100],"and":[101],"robustness":[102],"DNN":[105],"diagnosis":[108],"model.":[109]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-10-05T09:50:58.205359","created_date":"2018-07-19T00:00:00"}