{"id":"https://openalex.org/W3164266284","doi":"https://doi.org/10.3390/s21113608","title":"Fault Diagnosis and Fault Frequency Determination of Permanent Magnet Synchronous Motor Based on Deep Learning","display_name":"Fault Diagnosis and Fault Frequency Determination of Permanent Magnet Synchronous Motor Based on Deep Learning","publication_year":2021,"publication_date":"2021-05-22","ids":{"openalex":"https://openalex.org/W3164266284","doi":"https://doi.org/10.3390/s21113608","mag":"3164266284","pmid":"https://pubmed.ncbi.nlm.nih.gov/34067249"},"language":"en","primary_location":{"id":"doi:10.3390/s21113608","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s21113608","pdf_url":"https://www.mdpi.com/1424-8220/21/11/3608/pdf?version=1621845327","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/1424-8220/21/11/3608/pdf?version=1621845327","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5052061569","display_name":"Chiao-Sheng Wang","orcid":"https://orcid.org/0000-0002-6203-4973"},"institutions":[{"id":"https://openalex.org/I142974352","display_name":"National Sun Yat-sen University","ror":"https://ror.org/00mjawt10","country_code":"TW","type":"education","lineage":["https://openalex.org/I142974352"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chiao-Sheng Wang","raw_affiliation_strings":["Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Kaohsiung 804, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Kaohsiung 804, Taiwan","institution_ids":["https://openalex.org/I142974352"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073679391","display_name":"I-Hsi Kao","orcid":"https://orcid.org/0000-0003-4462-5515"},"institutions":[{"id":"https://openalex.org/I142974352","display_name":"National Sun Yat-sen University","ror":"https://ror.org/00mjawt10","country_code":"TW","type":"education","lineage":["https://openalex.org/I142974352"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"I-Hsi Kao","raw_affiliation_strings":["Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Kaohsiung 804, Taiwan"],"raw_orcid":"https://orcid.org/0000-0003-4462-5515","affiliations":[{"raw_affiliation_string":"Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Kaohsiung 804, Taiwan","institution_ids":["https://openalex.org/I142974352"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010616886","display_name":"Jau\u2010Woei Perng","orcid":"https://orcid.org/0000-0002-4703-8845"},"institutions":[{"id":"https://openalex.org/I142974352","display_name":"National Sun Yat-sen University","ror":"https://ror.org/00mjawt10","country_code":"TW","type":"education","lineage":["https://openalex.org/I142974352"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Jau-Woei Perng","raw_affiliation_strings":["Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Kaohsiung 804, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Kaohsiung 804, Taiwan","institution_ids":["https://openalex.org/I142974352"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5010616886"],"corresponding_institution_ids":["https://openalex.org/I142974352"],"apc_list":{"value":2400,"currency":"CHF","value_usd":2598},"apc_paid":{"value":2400,"currency":"CHF","value_usd":2598},"fwci":6.1092,"has_fulltext":true,"cited_by_count":62,"citation_normalized_percentile":{"value":0.9690463,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":100},"biblio":{"volume":"21","issue":"11","first_page":"3608","last_page":"3608"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9980999827384949,"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":0.9980999827384949,"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/T12169","display_name":"Non-Destructive Testing Techniques","score":0.9631999731063843,"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/T11343","display_name":"Power Transformer Diagnostics and Insulation","score":0.9577999711036682,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/overfitting","display_name":"Overfitting","score":0.7235296368598938},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6849073171615601},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6218380928039551},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5703411102294922},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5414891242980957},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.5293613076210022},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4441807270050049},{"id":"https://openalex.org/keywords/synchronous-motor","display_name":"Synchronous motor","score":0.436509907245636},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3464052677154541},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3431263267993927},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.29515987634658813}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.7235296368598938},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6849073171615601},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6218380928039551},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5703411102294922},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5414891242980957},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.5293613076210022},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4441807270050049},{"id":"https://openalex.org/C71376005","wikidata":"https://www.wikidata.org/wiki/Q845675","display_name":"Synchronous motor","level":2,"score":0.436509907245636},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3464052677154541},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3431263267993927},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.29515987634658813},{"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/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.3390/s21113608","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s21113608","pdf_url":"https://www.mdpi.com/1424-8220/21/11/3608/pdf?version=1621845327","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:34067249","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/34067249","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:doaj.org/article:34841eed446442fea0f0be21a938a88e","is_oa":true,"landing_page_url":"https://doaj.org/article/34841eed446442fea0f0be21a938a88e","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":"Sensors, Vol 21, Iss 11, p 3608 (2021)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1424-8220/21/11/3608/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/s21113608","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; Volume 21; Issue 11; Pages: 3608","raw_type":"Text"},{"id":"pmh:oai:pubmedcentral.nih.gov:8196902","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8196902","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":"Sensors (Basel)","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/s21113608","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s21113608","pdf_url":"https://www.mdpi.com/1424-8220/21/11/3608/pdf?version=1621845327","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":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3164266284.pdf","grobid_xml":"https://content.openalex.org/works/W3164266284.grobid-xml"},"referenced_works_count":50,"referenced_works":["https://openalex.org/W1992707230","https://openalex.org/W1996056654","https://openalex.org/W2097117768","https://openalex.org/W2154437080","https://openalex.org/W2163605009","https://openalex.org/W2163984882","https://openalex.org/W2183341477","https://openalex.org/W2187089797","https://openalex.org/W2194775991","https://openalex.org/W2274287116","https://openalex.org/W2295107390","https://openalex.org/W2317595875","https://openalex.org/W2560181314","https://openalex.org/W2594088219","https://openalex.org/W2618530766","https://openalex.org/W2741768983","https://openalex.org/W2748511798","https://openalex.org/W2766819698","https://openalex.org/W2789504340","https://openalex.org/W2789904726","https://openalex.org/W2801912834","https://openalex.org/W2809538946","https://openalex.org/W2810057162","https://openalex.org/W2886794804","https://openalex.org/W2890981503","https://openalex.org/W2911418581","https://openalex.org/W2913289332","https://openalex.org/W2944516260","https://openalex.org/W2949605076","https://openalex.org/W2950179405","https://openalex.org/W2956467153","https://openalex.org/W2963573361","https://openalex.org/W2964350391","https://openalex.org/W2968906743","https://openalex.org/W2991597181","https://openalex.org/W2995144806","https://openalex.org/W3011171540","https://openalex.org/W3015362700","https://openalex.org/W3016548657","https://openalex.org/W3018957240","https://openalex.org/W3043801889","https://openalex.org/W3049198170","https://openalex.org/W3082600888","https://openalex.org/W3119634645","https://openalex.org/W3120930604","https://openalex.org/W3128871194","https://openalex.org/W3214396588","https://openalex.org/W6684191040","https://openalex.org/W6758964607","https://openalex.org/W6790849442"],"related_works":["https://openalex.org/W4362597605","https://openalex.org/W1574414179","https://openalex.org/W4297676672","https://openalex.org/W3009056573","https://openalex.org/W2922073769","https://openalex.org/W4281702477","https://openalex.org/W2490526372","https://openalex.org/W4376166922","https://openalex.org/W4378510483","https://openalex.org/W4221142204"],"abstract_inverted_index":{"The":[0,34,78,96,133],"early":[1],"diagnosis":[2,28],"of":[3,29,45,53,59,71,80,98,174,187],"a":[4,21,68],"motor":[5,16,146],"is":[6,40,84,178,183],"important.":[7],"Many":[8],"researchers":[9],"have":[10],"used":[11],"deep":[12],"learning":[13,131],"to":[14,105,117,125,180],"diagnose":[15,143],"applications.":[17],"This":[18],"paper":[19],"proposes":[20],"one-dimensional":[22,35],"convolutional":[23,36,47],"neural":[24,37],"network":[25,38],"for":[26],"the":[27,51,54,60,62,81,86,107,111,120,126,129,138,158,166,171,175,193],"permanent":[30],"magnet":[31],"synchronous":[32],"motors.":[33],"model":[39,140,159,177],"weakly":[41],"supervised":[42],"and":[43,56,75,152,168,196],"consists":[44],"multiple":[46],"feature-extraction":[48,87],"modules.":[49],"Through":[50],"analysis":[52],"torque":[55,169],"current":[57,167],"signals":[58],"motors,":[61],"motors":[63],"can":[64,89,141,160],"be":[65],"diagnosed":[66],"under":[67],"wide":[69],"range":[70],"speeds,":[72],"variable":[73],"loads,":[74],"eccentricity":[76],"effects.":[77,163],"advantage":[79],"proposed":[82,116,139,176],"method":[83],"that":[85,123,137,186],"modules":[88],"extract":[90],"multiscale":[91],"features":[92],"from":[93],"complex":[94],"conditions.":[95],"number":[97],"training":[99],"parameters":[100],"was":[101,115],"reduced":[102],"so":[103],"as":[104,192],"solve":[106],"overfitting":[108],"problem.":[109],"Furthermore,":[110],"class":[112],"feature":[113],"map":[114],"automatically":[118],"determine":[119],"frequency":[121],"component":[122],"contributes":[124],"classification":[127,172],"using":[128],"weak":[130],"method.":[132],"experimental":[134],"results":[135],"reveal":[136],"effectively":[142],"three":[144],"different":[145],"states-healthy":[147],"state,":[148,151],"demagnetization":[149],"fault":[150,154],"bearing":[153],"state.":[155],"In":[156],"addition,":[157],"detect":[161],"eccentric":[162],"By":[164],"combining":[165],"features,":[170],"accuracy":[173],"up":[179],"98.85%,":[181],"which":[182],"higher":[184],"than":[185],"classical":[188],"machine-learning":[189],"methods":[190],"such":[191],"k-nearest":[194],"neighbor":[195],"support":[197],"vector":[198],"machine.":[199]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":14},{"year":2024,"cited_by_count":18},{"year":2023,"cited_by_count":12},{"year":2022,"cited_by_count":11},{"year":2021,"cited_by_count":3}],"updated_date":"2026-06-09T15:46:55.921056","created_date":"2025-10-10T00:00:00"}