{"id":"https://openalex.org/W7084124141","doi":"https://doi.org/10.1109/metroautomotive64646.2025.11119274","title":"Operating Condition Prognosis of Multi-Phase Electric Drives with Machine Learning Models","display_name":"Operating Condition Prognosis of Multi-Phase Electric Drives with Machine Learning Models","publication_year":2025,"publication_date":"2025-06-25","ids":{"openalex":"https://openalex.org/W7084124141","doi":"https://doi.org/10.1109/metroautomotive64646.2025.11119274"},"language":"en","primary_location":{"id":"doi:10.1109/metroautomotive64646.2025.11119274","is_oa":false,"landing_page_url":"https://doi.org/10.1109/metroautomotive64646.2025.11119274","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Workshop on Metrology for Automotive (MetroAutomotive)","raw_type":"proceedings-article"},"type":"article","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":null,"display_name":"Stefano Breda","orcid":null},"institutions":[{"id":"https://openalex.org/I4210123126","display_name":"Silicon Austria Labs (Austria)","ror":"https://ror.org/03b1qgn79","country_code":"AT","type":"company","lineage":["https://openalex.org/I4210123126"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Stefano Breda","raw_affiliation_strings":["System-Level Integration Technologies,Power Electronics Division, Silicon Austria Labs GmbH,Villach,Austria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"System-Level Integration Technologies,Power Electronics Division, Silicon Austria Labs GmbH,Villach,Austria","institution_ids":["https://openalex.org/I4210123126"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Monika Stipsitz","orcid":null},"institutions":[{"id":"https://openalex.org/I4210123126","display_name":"Silicon Austria Labs (Austria)","ror":"https://ror.org/03b1qgn79","country_code":"AT","type":"company","lineage":["https://openalex.org/I4210123126"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Monika Stipsitz","raw_affiliation_strings":["Collaborative Perception and Learning, Embedded Systems Division, Silicon Austria Labs GmbH,Graz,Austria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Collaborative Perception and Learning, Embedded Systems Division, Silicon Austria Labs GmbH,Graz,Austria","institution_ids":["https://openalex.org/I4210123126"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Tyson Alexander Dagorne","orcid":null},"institutions":[{"id":"https://openalex.org/I4210123126","display_name":"Silicon Austria Labs (Austria)","ror":"https://ror.org/03b1qgn79","country_code":"AT","type":"company","lineage":["https://openalex.org/I4210123126"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Tyson Alexander Dagorne","raw_affiliation_strings":["System-Level Integration Technologies,Power Electronics Division, Silicon Austria Labs GmbH,Villach,Austria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"System-Level Integration Technologies,Power Electronics Division, Silicon Austria Labs GmbH,Villach,Austria","institution_ids":["https://openalex.org/I4210123126"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Varaha Satya Bharath Kurukuru","orcid":null},"institutions":[{"id":"https://openalex.org/I4210123126","display_name":"Silicon Austria Labs (Austria)","ror":"https://ror.org/03b1qgn79","country_code":"AT","type":"company","lineage":["https://openalex.org/I4210123126"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Varaha Satya Bharath Kurukuru","raw_affiliation_strings":["System-Level Integration Technologies,Power Electronics Division, Silicon Austria Labs GmbH,Villach,Austria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"System-Level Integration Technologies,Power Electronics Division, Silicon Austria Labs GmbH,Villach,Austria","institution_ids":["https://openalex.org/I4210123126"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Ulrich Gaier","orcid":null},"institutions":[{"id":"https://openalex.org/I4210123126","display_name":"Silicon Austria Labs (Austria)","ror":"https://ror.org/03b1qgn79","country_code":"AT","type":"company","lineage":["https://openalex.org/I4210123126"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Ulrich Gaier","raw_affiliation_strings":["System-Level Integration Technologies,Power Electronics Division, Silicon Austria Labs GmbH,Villach,Austria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"System-Level Integration Technologies,Power Electronics Division, Silicon Austria Labs GmbH,Villach,Austria","institution_ids":["https://openalex.org/I4210123126"]}]},{"author_position":"last","author":{"id":null,"display_name":"Roberto Petrella","orcid":null},"institutions":[{"id":"https://openalex.org/I4210123126","display_name":"Silicon Austria Labs (Austria)","ror":"https://ror.org/03b1qgn79","country_code":"AT","type":"company","lineage":["https://openalex.org/I4210123126"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Roberto Petrella","raw_affiliation_strings":["System-Level Integration Technologies,Power Electronics Division, Silicon Austria Labs GmbH,Villach,Austria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"System-Level Integration Technologies,Power Electronics Division, Silicon Austria Labs GmbH,Villach,Austria","institution_ids":["https://openalex.org/I4210123126"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.58497365,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"173","last_page":"179"},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T10482","display_name":"Mathematical and Theoretical Epidemiology and Ecology Models","score":0.7049999833106995,"subfield":{"id":"https://openalex.org/subfields/2739","display_name":"Public Health, Environmental and Occupational Health"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10482","display_name":"Mathematical and Theoretical Epidemiology and Ecology Models","score":0.7049999833106995,"subfield":{"id":"https://openalex.org/subfields/2739","display_name":"Public Health, Environmental and Occupational Health"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11829","display_name":"Mathematical Biology Tumor Growth","score":0.06350000202655792,"subfield":{"id":"https://openalex.org/subfields/2611","display_name":"Modeling and Simulation"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11513","display_name":"stochastic dynamics and bifurcation","score":0.03530000150203705,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"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":0.5702999830245972},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.5126000046730042},{"id":"https://openalex.org/keywords/fault-detection-and-isolation","display_name":"Fault detection and isolation","score":0.4388999938964844},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.41600000858306885},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.4156999886035919},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.39079999923706055},{"id":"https://openalex.org/keywords/condition-monitoring","display_name":"Condition monitoring","score":0.3668000102043152},{"id":"https://openalex.org/keywords/mean-squared-prediction-error","display_name":"Mean squared prediction error","score":0.31679999828338623}],"concepts":[{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.597000002861023},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5702999830245972},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5637999773025513},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5480999946594238},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.5126000046730042},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.4388999938964844},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.41600000858306885},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.4156999886035919},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.39079999923706055},{"id":"https://openalex.org/C2775846686","wikidata":"https://www.wikidata.org/wiki/Q643012","display_name":"Condition monitoring","level":2,"score":0.3668000102043152},{"id":"https://openalex.org/C133731056","wikidata":"https://www.wikidata.org/wiki/Q4917288","display_name":"Control engineering","level":1,"score":0.3619999885559082},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3546000123023987},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.31679999828338623},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.3075999915599823},{"id":"https://openalex.org/C70452415","wikidata":"https://www.wikidata.org/wiki/Q3182448","display_name":"Predictive maintenance","level":2,"score":0.30660000443458557},{"id":"https://openalex.org/C103088060","wikidata":"https://www.wikidata.org/wiki/Q1062839","display_name":"Error detection and correction","level":2,"score":0.29440000653266907},{"id":"https://openalex.org/C22762622","wikidata":"https://www.wikidata.org/wiki/Q628904","display_name":"Operating point","level":2,"score":0.290800005197525},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.28519999980926514},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.28439998626708984},{"id":"https://openalex.org/C47446073","wikidata":"https://www.wikidata.org/wiki/Q5165890","display_name":"Control theory (sociology)","level":3,"score":0.2786000072956085},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.27730000019073486},{"id":"https://openalex.org/C5941749","wikidata":"https://www.wikidata.org/wiki/Q19768","display_name":"Machine tool","level":2,"score":0.275299996137619},{"id":"https://openalex.org/C2780799671","wikidata":"https://www.wikidata.org/wiki/Q17087362","display_name":"Transient (computer programming)","level":2,"score":0.26030001044273376}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/metroautomotive64646.2025.11119274","is_oa":false,"landing_page_url":"https://doi.org/10.1109/metroautomotive64646.2025.11119274","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Workshop on Metrology for Automotive (MetroAutomotive)","raw_type":"proceedings-article"},{"id":"pmh:oai:air.uniud.it:11390/1331606","is_oa":false,"landing_page_url":"https://hdl.handle.net/11390/1331606","pdf_url":null,"source":{"id":"https://openalex.org/S4306401163","display_name":"Institutional Research Information System (University of Udine)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I129043915","host_organization_name":"University of Udine","host_organization_lineage":["https://openalex.org/I129043915"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8401350975036621,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W2099616590","https://openalex.org/W2101551717","https://openalex.org/W2137725386","https://openalex.org/W2142139608","https://openalex.org/W2568312564","https://openalex.org/W2952947910","https://openalex.org/W3088283491","https://openalex.org/W3170591781","https://openalex.org/W3191550419","https://openalex.org/W3198069228","https://openalex.org/W3200435238","https://openalex.org/W3201468009","https://openalex.org/W3205769504","https://openalex.org/W4221055214","https://openalex.org/W4288062298","https://openalex.org/W4310450262","https://openalex.org/W4312851330","https://openalex.org/W4321781319","https://openalex.org/W4385834436"],"related_works":[],"abstract_inverted_index":{"Multi-phase":[0],"electric":[1],"drives":[2],"are":[3],"increasingly":[4],"adopted":[5],"in":[6,71,138],"industrial":[7],"applications":[8],"due":[9],"to":[10,34,110],"their":[11,55],"fault":[12,112],"tolerance,":[13],"efficiency,":[14],"and":[15,30,40,80,92,124,133,144],"high":[16],"performance":[17],"under":[18],"different":[19],"operating":[20,68],"conditions.":[21],"Reliable":[22],"operation":[23],"requires":[24],"accurate":[25],"classification":[26,70],"into":[27],"healthy,":[28],"degraded,":[29],"faulty":[31],"(H/D/F)":[32],"states":[33],"support":[35],"condition":[36,69],"monitoring,":[37],"reduce":[38],"downtime,":[39],"enable":[41],"predictive":[42],"maintenance.":[43],"Traditional":[44],"model-based":[45],"prognosis":[46],"methods":[47],"often":[48],"struggle":[49],"with":[50],"nonlinear":[51],"system":[52],"behavior,":[53],"limiting":[54],"effectiveness.":[56],"To":[57],"address":[58],"this,":[59],"a":[60,88,103,118,125],"machine":[61],"learning":[62],"(ML)-based":[63],"framework":[64,76,107],"is":[65,100,108],"proposed":[66],"for":[67],"multi-phase":[72],"drive":[73],"systems.":[74],"The":[75,114],"involves":[77],"simulating":[78],"normal":[79],"degraded":[81],"sensorlevel":[82],"scenarios,":[83],"generating":[84],"training":[85,87],"data,":[86],"regression":[89],"neural":[90],"network,":[91],"validating":[93],"the":[94,106],"trained":[95,115],"model.":[96],"Although":[97],"sensor":[98],"degradation":[99],"used":[101],"as":[102],"representative":[104],"case,":[105],"generalizable":[109],"other":[111],"types.":[113],"model":[116],"achieves":[117],"maximum":[119,126],"average":[120],"error":[121,128,137],"of":[122,129],"0.29%":[123],"absolute":[127],"1.5%":[130],"during":[131],"training,":[132],"less":[134],"than":[135],"3%":[136],"offline":[139],"validation,":[140],"confirming":[141],"its":[142],"accuracy":[143],"real-time":[145],"applicability.":[146]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
