{"id":"https://openalex.org/W4319586924","doi":"https://doi.org/10.1109/dsaa54385.2022.10032385","title":"Generating Synthetic Time Series for Machine-Learning-Empowered Monitoring of Electric Motor Test Benches","display_name":"Generating Synthetic Time Series for Machine-Learning-Empowered Monitoring of Electric Motor Test Benches","publication_year":2022,"publication_date":"2022-10-13","ids":{"openalex":"https://openalex.org/W4319586924","doi":"https://doi.org/10.1109/dsaa54385.2022.10032385"},"language":"en","primary_location":{"id":"doi:10.1109/dsaa54385.2022.10032385","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dsaa54385.2022.10032385","pdf_url":null,"source":{"id":"https://openalex.org/S4363608340","display_name":"2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA)","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":"https://openalex.org/A5011526759","display_name":"Tobias Westmeier","orcid":"https://orcid.org/0000-0001-8483-9556"},"institutions":[{"id":"https://openalex.org/I106157433","display_name":"University of Kassel","ror":"https://ror.org/04zc7p361","country_code":"DE","type":"education","lineage":["https://openalex.org/I106157433"]},{"id":"https://openalex.org/I889804353","display_name":"Robert Bosch (Germany)","ror":"https://ror.org/01fe0jt45","country_code":"DE","type":"company","lineage":["https://openalex.org/I889804353"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Tobias Westmeier","raw_affiliation_strings":["University of Kassel,Department of Electrical Engineering and Computer Science,Kassel,Germany","Bosch Center for Artificial Intelligence, Renningen, Germany","Department of Electrical Engineering and Computer Science, University of Kassel, Kassel, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Kassel,Department of Electrical Engineering and Computer Science,Kassel,Germany","institution_ids":["https://openalex.org/I106157433"]},{"raw_affiliation_string":"Bosch Center for Artificial Intelligence, Renningen, Germany","institution_ids":["https://openalex.org/I889804353"]},{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, University of Kassel, Kassel, Germany","institution_ids":["https://openalex.org/I106157433"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068772599","display_name":"Diego Botache","orcid":"https://orcid.org/0000-0003-1694-0307"},"institutions":[{"id":"https://openalex.org/I106157433","display_name":"University of Kassel","ror":"https://ror.org/04zc7p361","country_code":"DE","type":"education","lineage":["https://openalex.org/I106157433"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Diego Botache","raw_affiliation_strings":["University of Kassel,Department of Electrical Engineering and Computer Science,Kassel,Germany","Department of Electrical Engineering and Computer Science, University of Kassel, Kassel, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Kassel,Department of Electrical Engineering and Computer Science,Kassel,Germany","institution_ids":["https://openalex.org/I106157433"]},{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, University of Kassel, Kassel, Germany","institution_ids":["https://openalex.org/I106157433"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022867597","display_name":"Maarten Bieshaar","orcid":"https://orcid.org/0000-0002-6471-6062"},"institutions":[{"id":"https://openalex.org/I106157433","display_name":"University of Kassel","ror":"https://ror.org/04zc7p361","country_code":"DE","type":"education","lineage":["https://openalex.org/I106157433"]},{"id":"https://openalex.org/I889804353","display_name":"Robert Bosch (Germany)","ror":"https://ror.org/01fe0jt45","country_code":"DE","type":"company","lineage":["https://openalex.org/I889804353"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Maarten Bieshaar","raw_affiliation_strings":["University of Kassel,Department of Electrical Engineering and Computer Science,Kassel,Germany","Bosch Center for Artificial Intelligence, Renningen, Germany","Department of Electrical Engineering and Computer Science, University of Kassel, Kassel, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Kassel,Department of Electrical Engineering and Computer Science,Kassel,Germany","institution_ids":["https://openalex.org/I106157433"]},{"raw_affiliation_string":"Bosch Center for Artificial Intelligence, Renningen, Germany","institution_ids":["https://openalex.org/I889804353"]},{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, University of Kassel, Kassel, Germany","institution_ids":["https://openalex.org/I106157433"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065340030","display_name":"Bernhard Sick","orcid":"https://orcid.org/0000-0001-9467-656X"},"institutions":[{"id":"https://openalex.org/I106157433","display_name":"University of Kassel","ror":"https://ror.org/04zc7p361","country_code":"DE","type":"education","lineage":["https://openalex.org/I106157433"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Bernhard Sick","raw_affiliation_strings":["University of Kassel,Department of Electrical Engineering and Computer Science,Kassel,Germany","Department of Electrical Engineering and Computer Science, University of Kassel, Kassel, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Kassel,Department of Electrical Engineering and Computer Science,Kassel,Germany","institution_ids":["https://openalex.org/I106157433"]},{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, University of Kassel, Kassel, Germany","institution_ids":["https://openalex.org/I106157433"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.7015,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.68685852,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9952999949455261,"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":0.9952999949455261,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9890999794006348,"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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9883999824523926,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7378676533699036},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6141481399536133},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5758395791053772},{"id":"https://openalex.org/keywords/test-data","display_name":"Test data","score":0.5467355251312256},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.5241639614105225},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5150468349456787},{"id":"https://openalex.org/keywords/test-bench","display_name":"Test bench","score":0.47420480847358704},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4558987617492676},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.43864428997039795},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.43844470381736755},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.42530521750450134},{"id":"https://openalex.org/keywords/fault-detection-and-isolation","display_name":"Fault detection and isolation","score":0.42268988490104675},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.20145735144615173},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.0834520161151886}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7378676533699036},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6141481399536133},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5758395791053772},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.5467355251312256},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.5241639614105225},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5150468349456787},{"id":"https://openalex.org/C2776266606","wikidata":"https://www.wikidata.org/wiki/Q476482","display_name":"Test bench","level":2,"score":0.47420480847358704},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4558987617492676},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.43864428997039795},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.43844470381736755},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.42530521750450134},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.42268988490104675},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.20145735144615173},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.0834520161151886},{"id":"https://openalex.org/C172707124","wikidata":"https://www.wikidata.org/wiki/Q423488","display_name":"Actuator","level":2,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dsaa54385.2022.10032385","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dsaa54385.2022.10032385","pdf_url":null,"source":{"id":"https://openalex.org/S4363608340","display_name":"2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.41999998688697815}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321408","display_name":"Ministry of Education","ror":"https://ror.org/01p262204"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":53,"referenced_works":["https://openalex.org/W571200655","https://openalex.org/W1506806321","https://openalex.org/W1982275278","https://openalex.org/W2045186954","https://openalex.org/W2055873761","https://openalex.org/W2078179989","https://openalex.org/W2104933073","https://openalex.org/W2148143831","https://openalex.org/W2164330572","https://openalex.org/W2239193640","https://openalex.org/W2510642588","https://openalex.org/W2519091744","https://openalex.org/W2559655401","https://openalex.org/W2584755547","https://openalex.org/W2612690371","https://openalex.org/W2614183994","https://openalex.org/W2617302333","https://openalex.org/W2896457183","https://openalex.org/W2896850464","https://openalex.org/W2941389059","https://openalex.org/W2944364052","https://openalex.org/W2945027741","https://openalex.org/W2948978827","https://openalex.org/W2963185411","https://openalex.org/W2963609389","https://openalex.org/W2972810968","https://openalex.org/W3012772911","https://openalex.org/W3015694082","https://openalex.org/W3083891030","https://openalex.org/W3115948762","https://openalex.org/W3123931583","https://openalex.org/W3153657341","https://openalex.org/W3156523113","https://openalex.org/W3167297489","https://openalex.org/W3173233772","https://openalex.org/W3173675787","https://openalex.org/W4206699361","https://openalex.org/W4214813689","https://openalex.org/W4243367342","https://openalex.org/W4292779060","https://openalex.org/W4297817572","https://openalex.org/W6640963894","https://openalex.org/W6687045409","https://openalex.org/W6737563499","https://openalex.org/W6755257315","https://openalex.org/W6755520906","https://openalex.org/W6756476962","https://openalex.org/W6762093281","https://openalex.org/W6762987983","https://openalex.org/W6765779288","https://openalex.org/W6775395751","https://openalex.org/W6778883912","https://openalex.org/W6798935812"],"related_works":["https://openalex.org/W2131906933","https://openalex.org/W4386259959","https://openalex.org/W2594053212","https://openalex.org/W2377169396","https://openalex.org/W2318183561","https://openalex.org/W2899447478","https://openalex.org/W4306778406","https://openalex.org/W2075571720","https://openalex.org/W3084906606","https://openalex.org/W3186268266"],"abstract_inverted_index":{"The":[0],"development":[1,33],"of":[2,45,75,186,194,239],"new":[3,140],"electric":[4],"traction":[5],"machines":[6],"is":[7],"a":[8,90,99,106,115,125,139,195],"time-consuming":[9],"process":[10,40,233],"as":[11,49,138],"it":[12],"involves":[13],"intensive":[14],"testing":[15],"on":[16,98,191,203],"motor":[17,196],"test":[18,83,197],"benches.":[19,84],"Machine-Learning-empowered":[20],"monitoring":[21,41],"offers":[22],"the":[23,50,72,112,120,133,145,166,176,200,231,235],"opportunity":[24],"to":[25,64,70,93,114,143,172,213,245],"anticipate":[26],"costly":[27],"failures":[28],"early":[29],"and":[30,56,118,199],"hence":[31],"reduce":[32],"time.":[34],"However,":[35],"machine":[36],"learning":[37],"(ML)":[38],"for":[39,217],"requires":[42],"large":[43],"amounts":[44],"training":[46,77,215,232],"data,":[47],"especially":[48],"targeted":[51],"fault":[52,187,219],"states":[53],"are":[54],"scarce":[55],"yet":[57],"diverse":[58],"in":[59,81,175,230,237],"their":[60],"appearances.Therefore,":[61],"we":[62,88,131,163,182],"propose":[63,132],"use":[65],"synthetic":[66,95,204],"time":[67,101],"series":[68,102],"data":[69,78,96,113,122,168,193,216,229],"leverage":[71],"high":[73],"cost":[74],"acquiring":[76],"from":[79],"experiments":[80],"real":[82,177,192],"In":[85,129,160,179],"this":[86,161],"article,":[87],"present":[89,174],"novel":[91],"scheme":[92],"generate":[94,214],"based":[97,190,202],"sub-dimensional":[100],"representation.":[103],"We":[104,206],"introduce":[105],"highly":[107],"flexible":[108],"model":[109],"by":[110,124,155],"mapping":[111],"latent":[116,121],"representation":[117],"approximating":[119],"distribution":[123],"Gaussian":[126],"Mixture":[127],"Model.":[128],"addition,":[130],"Fr\u00e9chet":[134],"InceptionTime":[135],"Distance":[136],"(FITD)":[137],"distance":[141],"measure":[142],"evaluate":[144],"generated":[146],"data.":[147,178,205],"It":[148],"allows":[149],"extracting":[150],"characteristics":[151,170],"at":[152],"different":[153,218],"scales":[154],"using":[156,227],"multiple":[157],"kernel":[158],"sizes.":[159],"way,":[162],"ensure":[164],"that":[165,226],"synthesized":[167,228],"contains":[169],"similar":[171],"those":[173],"our":[180],"experiment,":[181],"train":[183],"two":[184],"types":[185,220],"detectors,":[188],"one":[189],"bench":[198],"other":[201],"also":[207],"consider":[208],"employing":[209],"fault-aware":[210],"conditional":[211],"architectures":[212],"explicitly.":[221],"Our":[222],"final":[223],"results":[224],"show":[225],"increases":[234],"performance":[236],"terms":[238],"classification":[240],"accuracy":[241],"score":[242],"(CAS)":[243],"up":[244],"29%.":[246]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2024,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}