{"id":"https://openalex.org/W3174576536","doi":"https://doi.org/10.3390/e23060751","title":"Sensor and Actuator Fault Diagnosis for Robot Joint Based on Deep CNN","display_name":"Sensor and Actuator Fault Diagnosis for Robot Joint Based on Deep CNN","publication_year":2021,"publication_date":"2021-06-15","ids":{"openalex":"https://openalex.org/W3174576536","doi":"https://doi.org/10.3390/e23060751","mag":"3174576536","pmid":"https://pubmed.ncbi.nlm.nih.gov/34203708"},"language":"en","primary_location":{"id":"doi:10.3390/e23060751","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e23060751","pdf_url":"https://www.mdpi.com/1099-4300/23/6/751/pdf?version=1623831043","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"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":"Entropy","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/1099-4300/23/6/751/pdf?version=1623831043","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5051219204","display_name":"Jinghui Pan","orcid":"https://orcid.org/0000-0003-2004-584X"},"institutions":[{"id":"https://openalex.org/I92403157","display_name":"University of Science and Technology Beijing","ror":"https://ror.org/02egmk993","country_code":"CN","type":"education","lineage":["https://openalex.org/I92403157"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jinghui Pan","raw_affiliation_strings":["School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China","institution_ids":["https://openalex.org/I92403157"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037323203","display_name":"Lili Qu","orcid":"https://orcid.org/0000-0001-9058-6313"},"institutions":[{"id":"https://openalex.org/I14894300","display_name":"Foshan University","ror":"https://ror.org/02xvvvp28","country_code":"CN","type":"education","lineage":["https://openalex.org/I14894300"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lili Qu","raw_affiliation_strings":["School of Mechatronic Engineering and Automation, Foshan University, Foshan 528231, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mechatronic Engineering and Automation, Foshan University, Foshan 528231, China","institution_ids":["https://openalex.org/I14894300"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016153320","display_name":"Kaixiang Peng","orcid":"https://orcid.org/0000-0001-8314-3047"},"institutions":[{"id":"https://openalex.org/I92403157","display_name":"University of Science and Technology Beijing","ror":"https://ror.org/02egmk993","country_code":"CN","type":"education","lineage":["https://openalex.org/I92403157"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kaixiang Peng","raw_affiliation_strings":["School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China","institution_ids":["https://openalex.org/I92403157"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5051219204"],"corresponding_institution_ids":["https://openalex.org/I92403157"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2227},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2227},"fwci":3.5852,"has_fulltext":true,"cited_by_count":53,"citation_normalized_percentile":{"value":0.93428893,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":93,"max":100},"biblio":{"volume":"23","issue":"6","first_page":"751","last_page":"751"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9987999796867371,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9987999796867371,"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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9937000274658203,"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":0.9677000045776367,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7936185598373413},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7550208568572998},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6911538243293762},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6158322095870972},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6006123423576355},{"id":"https://openalex.org/keywords/actuator","display_name":"Actuator","score":0.5804128646850586},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.5369093418121338},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5053982734680176},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4858834147453308},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4774911105632782},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.42470741271972656}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7936185598373413},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7550208568572998},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6911538243293762},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6158322095870972},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6006123423576355},{"id":"https://openalex.org/C172707124","wikidata":"https://www.wikidata.org/wiki/Q423488","display_name":"Actuator","level":2,"score":0.5804128646850586},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.5369093418121338},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5053982734680176},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4858834147453308},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4774911105632782},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.42470741271972656},{"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}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.3390/e23060751","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e23060751","pdf_url":"https://www.mdpi.com/1099-4300/23/6/751/pdf?version=1623831043","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"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":"Entropy","raw_type":"journal-article"},{"id":"pmid:34203708","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/34203708","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":"Entropy (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:doaj.org/article:994beddd54f34aba8662a1c92628c422","is_oa":true,"landing_page_url":"https://doaj.org/article/994beddd54f34aba8662a1c92628c422","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy, Vol 23, Iss 6, p 751 (2021)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1099-4300/23/6/751/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/e23060751","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":"Entropy; Volume 23; Issue 6; Pages: 751","raw_type":"Text"},{"id":"pmh:oai:pubmedcentral.nih.gov:8232324","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8232324","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy (Basel)","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/e23060751","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e23060751","pdf_url":"https://www.mdpi.com/1099-4300/23/6/751/pdf?version=1623831043","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"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":"Entropy","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.6899999976158142,"id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G3519992340","display_name":null,"funder_award_id":"2019YFB1309900","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3174576536.pdf","grobid_xml":"https://content.openalex.org/works/W3174576536.grobid-xml"},"referenced_works_count":40,"referenced_works":["https://openalex.org/W1806891645","https://openalex.org/W1953185517","https://openalex.org/W1983782700","https://openalex.org/W2025768430","https://openalex.org/W2071534836","https://openalex.org/W2082323100","https://openalex.org/W2117675571","https://openalex.org/W2132520336","https://openalex.org/W2136922672","https://openalex.org/W2230652337","https://openalex.org/W2322650702","https://openalex.org/W2324044936","https://openalex.org/W2366161520","https://openalex.org/W2371629303","https://openalex.org/W2461729787","https://openalex.org/W2595657631","https://openalex.org/W2603304445","https://openalex.org/W2608571722","https://openalex.org/W2622826443","https://openalex.org/W2767113858","https://openalex.org/W2768174095","https://openalex.org/W2768753204","https://openalex.org/W2769634371","https://openalex.org/W2790702084","https://openalex.org/W2810292802","https://openalex.org/W2885208219","https://openalex.org/W2911725628","https://openalex.org/W2916642359","https://openalex.org/W2926228387","https://openalex.org/W2976197991","https://openalex.org/W2977142280","https://openalex.org/W2977501260","https://openalex.org/W2980686936","https://openalex.org/W2981095233","https://openalex.org/W2997249751","https://openalex.org/W3029911800","https://openalex.org/W3082600888","https://openalex.org/W3089167803","https://openalex.org/W6631190155","https://openalex.org/W6998470532"],"related_works":["https://openalex.org/W2965546495","https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W259157601","https://openalex.org/W4205463238","https://openalex.org/W4226493464","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"a":[3],"data-driven":[4],"method-based":[5],"fault":[6,45,74,135,173,179],"diagnosis":[7,136,174],"method":[8,175],"using":[9,49,151],"the":[10,50,58,61,81,93,109,124,127,152,171],"deep":[11,82,129],"convolutional":[12,83,130],"neural":[13,52,84,131,144,148],"network":[14,85,132,145,149,158],"(DCNN).":[15],"The":[16,167],"DCNN":[17,165,172],"is":[18,86],"used":[19],"to":[20,56,88,96,98,122],"deal":[21],"with":[22,164],"sensor":[23],"and":[24,36,41,43,66,155,162],"actuator":[25],"faults":[26],"of":[27,64,73,105,126],"robot":[28],"joints,":[29],"such":[30],"as":[31],"gain":[32],"error,":[33,35],"offset":[34],"malfunction":[37],"for":[38,102],"both":[39,71],"sensors":[40,65],"actuators,":[42],"different":[44,134],"types":[46,72],"are":[47,68,75,160],"diagnosed":[48],"trained":[51],"network.":[53],"In":[54,120],"order":[55,121],"achieve":[57],"above":[59],"goal,":[60],"fused":[62],"data":[63,95],"actuators":[67],"used,":[69],"where":[70],"described":[76],"in":[77],"one":[78],"formulation.":[79],"Then,":[80],"applied":[87],"learn":[89],"characteristic":[90],"features":[91],"from":[92],"merged":[94],"try":[97],"find":[99],"discriminative":[100],"information":[101],"each":[103],"kind":[104],"fault.":[106],"After":[107],"that,":[108],"fully":[110],"connected":[111],"layer":[112],"does":[113],"prediction":[114],"work":[115],"based":[116],"on":[117],"learned":[118],"features.":[119],"verify":[123],"effectiveness":[125],"proposed":[128],"model,":[133],"methods":[137],"including":[138],"support":[139],"vector":[140],"machine":[141],"(SVM),":[142],"artificial":[143],"(ANN),":[146],"conventional":[147],"(CNN)":[150],"LeNet-5":[153],"method,":[154],"long-term":[156],"memory":[157],"(LTMN)":[159],"investigated":[161],"compared":[163],"method.":[166],"results":[168],"show":[169],"that":[170],"can":[176],"realize":[177],"high":[178],"recognition":[180],"accuracy":[181],"while":[182],"needing":[183],"less":[184],"model":[185],"training":[186],"time.":[187]},"counts_by_year":[{"year":2026,"cited_by_count":10},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":13},{"year":2023,"cited_by_count":11},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":2}],"updated_date":"2026-07-30T17:31:21.811387","created_date":"2025-10-10T00:00:00"}
