{"id":"https://openalex.org/W4399526582","doi":"https://doi.org/10.1109/access.2024.3412050","title":"An Improved Lightweight Variant of EfficientNetV2 Coupled With Sensor Fusion and Transfer Learning Techniques for Motor Fault Diagnosis","display_name":"An Improved Lightweight Variant of EfficientNetV2 Coupled With Sensor Fusion and Transfer Learning Techniques for Motor Fault Diagnosis","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4399526582","doi":"https://doi.org/10.1109/access.2024.3412050"},"language":"en","primary_location":{"id":"doi:10.1109/access.2024.3412050","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3412050","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2024.3412050","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5114858897","display_name":"Liang Jiang","orcid":"https://orcid.org/0000-0001-7314-3991"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Liang Jiang","raw_affiliation_strings":["School of Automation, Wuxi University, Wuxi, Jiangsu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation, Wuxi University, Wuxi, Jiangsu, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015373684","display_name":"S. H. Zhu","orcid":"https://orcid.org/0009-0006-4010-202X"},"institutions":[{"id":"https://openalex.org/I200845125","display_name":"Nanjing University of Information Science and Technology","ror":"https://ror.org/02y0rxk19","country_code":"CN","type":"education","lineage":["https://openalex.org/I200845125"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sicheng Zhu","raw_affiliation_strings":["School of Automation, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China"],"raw_orcid":"https://orcid.org/0009-0006-4010-202X","affiliations":[{"raw_affiliation_string":"School of Automation, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China","institution_ids":["https://openalex.org/I200845125"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031505558","display_name":"Ning Sun","orcid":"https://orcid.org/0000-0002-0280-5983"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ning Sun","raw_affiliation_strings":["School of Automation, Wuxi University, Wuxi, Jiangsu, China"],"raw_orcid":"https://orcid.org/0000-0002-0280-5983","affiliations":[{"raw_affiliation_string":"School of Automation, Wuxi University, Wuxi, Jiangsu, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5114858897"],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":4.3048,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.94821775,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"12","issue":null,"first_page":"84470","last_page":"84487"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9939000010490417,"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"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9939000010490417,"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"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9907000064849854,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9900000095367432,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6290505528450012},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.5169641375541687},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.48331332206726074},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.45158064365386963},{"id":"https://openalex.org/keywords/transfer","display_name":"Transfer (computing)","score":0.41353705525398254},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.34492987394332886}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6290505528450012},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.5169641375541687},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.48331332206726074},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.45158064365386963},{"id":"https://openalex.org/C2776175482","wikidata":"https://www.wikidata.org/wiki/Q1195816","display_name":"Transfer (computing)","level":2,"score":0.41353705525398254},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34492987394332886},{"id":"https://openalex.org/C165205528","wikidata":"https://www.wikidata.org/wiki/Q83371","display_name":"Seismology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2024.3412050","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3412050","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:141983859e1d44c985f7261ee66c8bb3","is_oa":true,"landing_page_url":"https://doaj.org/article/141983859e1d44c985f7261ee66c8bb3","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 12, Pp 84470-84487 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2024.3412050","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3412050","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.5899999737739563,"id":"https://metadata.un.org/sdg/7"}],"awards":[{"id":"https://openalex.org/G6183462949","display_name":null,"funder_award_id":"42275156","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W2095705004","https://openalex.org/W2117539524","https://openalex.org/W2132984323","https://openalex.org/W2152254169","https://openalex.org/W2194775991","https://openalex.org/W2268875920","https://openalex.org/W2395579298","https://openalex.org/W2518937691","https://openalex.org/W2906256948","https://openalex.org/W2955425717","https://openalex.org/W2963125010","https://openalex.org/W2963163009","https://openalex.org/W2981609437","https://openalex.org/W2982083293","https://openalex.org/W2990122260","https://openalex.org/W3048221011","https://openalex.org/W3092711945","https://openalex.org/W3122103149","https://openalex.org/W3126471189","https://openalex.org/W3136919081","https://openalex.org/W3148949211","https://openalex.org/W3154236835","https://openalex.org/W3167976421","https://openalex.org/W3171038842","https://openalex.org/W3181367324","https://openalex.org/W3181638476","https://openalex.org/W3193243551","https://openalex.org/W3207384086","https://openalex.org/W4210605464","https://openalex.org/W4229000217","https://openalex.org/W4285504903","https://openalex.org/W4296118515","https://openalex.org/W4386527666","https://openalex.org/W6637373629","https://openalex.org/W6674330103","https://openalex.org/W6684191040","https://openalex.org/W6762718338","https://openalex.org/W6793164127"],"related_works":["https://openalex.org/W3201126466","https://openalex.org/W4282827391","https://openalex.org/W4405541655","https://openalex.org/W4386828785","https://openalex.org/W2099421762","https://openalex.org/W2530546662","https://openalex.org/W3165580226","https://openalex.org/W2967030268","https://openalex.org/W2185253430","https://openalex.org/W3135401135"],"abstract_inverted_index":{"Although":[0],"deep":[1],"learning":[2,46,207],"methods":[3],"based":[4],"on":[5],"single":[6],"sensors":[7,74],"are":[8],"widely":[9],"applied":[10],"in":[11,248],"fault":[12,59,221,226],"diagnosis,":[13],"leveraging":[14],"multi-sensor":[15,27,116,212],"data":[16],"to":[17,48,68,84,110,126,168,190],"learn":[18],"useful":[19],"information":[20,213],"remains":[21],"a":[22,32,95,225,232],"challenge.":[23],"To":[24],"fully":[25],"utilize":[26],"information,":[28],"this":[29,156],"paper":[30],"proposes":[31],"lightweight":[33,137,218],"improvement":[34],"of":[35,115,250,258,268,275],"the":[36,62,70,79,112,128,143,176,193,216,241,245],"EfficientNetV2":[37,123],"architecture,":[38],"combined":[39],"with":[40,255,272],"sensor":[41],"fusion":[42,96,108,214],"technology":[43],"and":[44,52,78,105,135,146,152,163,175,187,215,253,260],"transfer":[45,206],"techniques,":[47],"develop":[49],"an":[50,256,273],"efficient":[51],"reliable":[53],"new":[54],"method":[55,98,243,247],"specifically":[56],"for":[57,196,209,220],"motor":[58,233],"diagnosis.":[60],"First,":[61],"continuous":[63],"wavelet":[64],"transform":[65],"is":[66,82,99,124,181,229],"utilized":[67],"convert":[69],"signals":[71],"from":[72],"various":[73],"into":[75,88],"time-frequency":[76,117],"images,":[77],"Mallat":[80],"algorithm":[81],"employed":[83],"decompose":[85],"each":[86],"image":[87],"sub-band":[89,113],"coefficients":[90,114],"at":[91,119],"different":[92,120],"levels.":[93,121],"Secondly,":[94],"reconstruction":[97],"constructed":[100],"using":[101,184,231],"coefficient":[102],"absolute":[103],"maximum":[104,177],"weighted":[106],"average":[107],"rules":[109],"integrate":[111],"images":[118],"Subsequently,":[122],"improved":[125,182,197,217],"enhance":[127,169],"model\u2019s":[129,144],"feature":[130,170],"extraction":[131,171],"capabilities,":[132],"computational":[133,153],"efficiency,":[134],"achieve":[136],"effects.":[138],"The":[139,199,236],"EfficientNetV2-M0":[140],"network":[141,157,200],"modifies":[142],"depth":[145],"width":[147],"multiplicity":[148],"factors,":[149],"reducing":[150],"parameters":[151],"complexity.":[154],"Furthermore,":[155],"incorporates":[158],"Diverse":[159],"Branch":[160],"Block":[161],"(DBB)":[162],"Multidimensional":[164],"Collaborative":[165],"Attention":[166],"(MCA)":[167],"under":[172,266],"complex":[173],"backgrounds,":[174],"cross-entropy":[178],"loss":[179,189],"function":[180],"by":[183],"label":[185],"smoothing":[186],"focal":[188],"dynamically":[191],"adjust":[192],"classification":[194],"weights":[195],"accuracy.":[198],"leverages":[201],"pre-trained":[202],"models":[203],"obtained":[204],"through":[205],"techniques":[208],"deployment,":[210],"combining":[211],"model":[219],"diagnosis":[222,227],"applications.":[223],"Finally,":[224],"experiment":[228],"conducted":[230],"state":[234],"dataset.":[235],"experimental":[237],"results":[238],"demonstrate":[239],"that":[240],"proposed":[242],"outperforms":[244],"control":[246],"terms":[249],"diagnostic":[251],"performance":[252,264],"robustness,":[254],"accuracy":[257,274],"100%,":[259],"it":[261],"exhibits":[262],"excellent":[263],"even":[265],"conditions":[267],"small":[269],"sample":[270],"data,":[271],"98.81%.":[276]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":12}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
