{"id":"https://openalex.org/W4415974242","doi":"https://doi.org/10.1016/j.procs.2025.09.290","title":"Comparison of Conditional and Non-Conditional Data Augmentation Approaches with Generative Adversarial Networks: A Case Study on Bearing Fault Diagnosis","display_name":"Comparison of Conditional and Non-Conditional Data Augmentation Approaches with Generative Adversarial Networks: A Case Study on Bearing Fault Diagnosis","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4415974242","doi":"https://doi.org/10.1016/j.procs.2025.09.290"},"language":"en","primary_location":{"id":"doi:10.1016/j.procs.2025.09.290","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.procs.2025.09.290","pdf_url":null,"source":{"id":"https://openalex.org/S120348307","display_name":"Procedia Computer Science","issn_l":"1877-0509","issn":["1877-0509"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Procedia Computer Science","raw_type":"journal-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1016/j.procs.2025.09.290","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Timo K\u00f6nig","orcid":null},"institutions":[{"id":"https://openalex.org/I4210136950","display_name":"Hochschule Aalen","ror":"https://ror.org/04gg60e72","country_code":"DE","type":"education","lineage":["https://openalex.org/I4210136950"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Timo K\u00f6nig","raw_affiliation_strings":["Aalen University of Applied Sciences, Institute for Drive Technology Aalen, 73430 Aalen, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Aalen University of Applied Sciences, Institute for Drive Technology Aalen, 73430 Aalen, Germany","institution_ids":["https://openalex.org/I4210136950"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054848067","display_name":"Akash Mangaluru Ramananda","orcid":"https://orcid.org/0000-0002-1443-9343"},"institutions":[{"id":"https://openalex.org/I4210136950","display_name":"Hochschule Aalen","ror":"https://ror.org/04gg60e72","country_code":"DE","type":"education","lineage":["https://openalex.org/I4210136950"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Akash Mangaluru Ramananda","raw_affiliation_strings":["Aalen University of Applied Sciences, Institute for Drive Technology Aalen, 73430 Aalen, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Aalen University of Applied Sciences, Institute for Drive Technology Aalen, 73430 Aalen, Germany","institution_ids":["https://openalex.org/I4210136950"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041657497","display_name":"Fabian Wagner","orcid":"https://orcid.org/0000-0002-5964-6485"},"institutions":[{"id":"https://openalex.org/I4210136950","display_name":"Hochschule Aalen","ror":"https://ror.org/04gg60e72","country_code":"DE","type":"education","lineage":["https://openalex.org/I4210136950"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Fabian Wagner","raw_affiliation_strings":["Aalen University of Applied Sciences, Faculty of Electronics and Computer Science, 73430 Aalen, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Aalen University of Applied Sciences, Faculty of Electronics and Computer Science, 73430 Aalen, Germany","institution_ids":["https://openalex.org/I4210136950"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077752598","display_name":"Markus Kley","orcid":"https://orcid.org/0000-0003-4061-0797"},"institutions":[{"id":"https://openalex.org/I4210136950","display_name":"Hochschule Aalen","ror":"https://ror.org/04gg60e72","country_code":"DE","type":"education","lineage":["https://openalex.org/I4210136950"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Markus Kley","raw_affiliation_strings":["Aalen University of Applied Sciences, Institute for Drive Technology Aalen, 73430 Aalen, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Aalen University of Applied Sciences, Institute for Drive Technology Aalen, 73430 Aalen, Germany","institution_ids":["https://openalex.org/I4210136950"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053445565","display_name":"Marcus Liebschner","orcid":"https://orcid.org/0000-0002-1387-1214"},"institutions":[{"id":"https://openalex.org/I4210136950","display_name":"Hochschule Aalen","ror":"https://ror.org/04gg60e72","country_code":"DE","type":"education","lineage":["https://openalex.org/I4210136950"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Marcus Liebschner","raw_affiliation_strings":["Aalen University of Applied Sciences, Faculty of Electronics and Computer Science, 73430 Aalen, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Aalen University of Applied Sciences, Faculty of Electronics and Computer Science, 73430 Aalen, Germany","institution_ids":["https://openalex.org/I4210136950"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210136950"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"270","issue":null,"first_page":"1696","last_page":"1705"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9545999765396118,"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":0.9545999765396118,"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":0.0052999998442828655,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.003000000026077032,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/generative-adversarial-network","display_name":"Generative adversarial network","score":0.5860000252723694},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.5325999855995178},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5302000045776367},{"id":"https://openalex.org/keywords/scope","display_name":"Scope (computer science)","score":0.5113999843597412},{"id":"https://openalex.org/keywords/test-data","display_name":"Test data","score":0.4447000026702881},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.4255000054836273},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.4000000059604645},{"id":"https://openalex.org/keywords/bearing","display_name":"Bearing (navigation)","score":0.3736000061035156},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35910001397132874}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8615000247955322},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.5860000252723694},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.5325999855995178},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5302000045776367},{"id":"https://openalex.org/C2778012447","wikidata":"https://www.wikidata.org/wiki/Q1034415","display_name":"Scope (computer science)","level":2,"score":0.5113999843597412},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4984000027179718},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47189998626708984},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4596000015735626},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.4447000026702881},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.4255000054836273},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.4000000059604645},{"id":"https://openalex.org/C199978012","wikidata":"https://www.wikidata.org/wiki/Q1273815","display_name":"Bearing (navigation)","level":2,"score":0.3736000061035156},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35910001397132874},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.35659998655319214},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32010000944137573},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3059000074863434},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.30149999260902405},{"id":"https://openalex.org/C97385483","wikidata":"https://www.wikidata.org/wiki/Q16954980","display_name":"Deep belief network","level":3,"score":0.30140000581741333},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.2913999855518341},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.28630000352859497},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2840999960899353},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.27970001101493835},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.27950000762939453},{"id":"https://openalex.org/C43555835","wikidata":"https://www.wikidata.org/wiki/Q2300258","display_name":"Conditional probability distribution","level":2,"score":0.27399998903274536},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.2599000036716461},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.25859999656677246},{"id":"https://openalex.org/C63540848","wikidata":"https://www.wikidata.org/wiki/Q3140932","display_name":"Fault tolerance","level":2,"score":0.257099986076355}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1016/j.procs.2025.09.290","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.procs.2025.09.290","pdf_url":null,"source":{"id":"https://openalex.org/S120348307","display_name":"Procedia Computer Science","issn_l":"1877-0509","issn":["1877-0509"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Procedia Computer Science","raw_type":"journal-article"},{"id":"pmh:oai:opus-htw-aalen.bsz-bw.de:8925","is_oa":true,"landing_page_url":"https://opus-htw-aalen.bsz-bw.de/frontdoor/index/index/docId/8925","pdf_url":null,"source":{"id":"https://openalex.org/S4306401044","display_name":"OPUS (Aalen University)","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-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.1016/j.procs.2025.09.290","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.procs.2025.09.290","pdf_url":null,"source":{"id":"https://openalex.org/S120348307","display_name":"Procedia Computer Science","issn_l":"1877-0509","issn":["1877-0509"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Procedia Computer Science","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4303701741","display_name":"Bundesministerium f\u00fcr Wirtschaft und Klimaschutz","ror":"https://ror.org/02vgg2808"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1978119245","https://openalex.org/W2753935149","https://openalex.org/W2979389376","https://openalex.org/W3012450219","https://openalex.org/W3025967384","https://openalex.org/W3159415473","https://openalex.org/W3174788865","https://openalex.org/W4212992602","https://openalex.org/W4225380710","https://openalex.org/W4235594362","https://openalex.org/W4286433454","https://openalex.org/W4293496870","https://openalex.org/W4293732289","https://openalex.org/W4313061249","https://openalex.org/W4320491464","https://openalex.org/W4376865666","https://openalex.org/W4380323854","https://openalex.org/W4389483557","https://openalex.org/W4389483833","https://openalex.org/W4392975299","https://openalex.org/W4404838148"],"related_works":[],"abstract_inverted_index":{"In":[0,89],"order":[1],"to":[2,31,155,192,227],"perform":[3],"fault":[4,44],"classification":[5],"using":[6,110],"Machine":[7,39],"Learning":[8,40],"algorithms,":[9],"sufficient":[10,29,198],"and":[11,35,73,103,108,135],"balanced":[12],"data":[13,23,70,75,87,112,190,195,206,211],"is":[14,24,61,106,207],"required.":[15],"Nevertheless,":[16,200],"in":[17,27,139,172,240],"a":[18,28,33,56,99,114,119,136,147,159,173,184,214,218],"lot":[19],"of":[20,38,63,82,92,98,113,146,178,203,221],"use":[21],"cases":[22],"not":[25],"available":[26,79],"manner":[30],"allow":[32],"stable":[34],"valid":[36],"training":[37,177],"based":[41],"algorithms":[42,84],"for":[43,50,68,80,85,162,231],"classification.":[45],"There":[46],"are":[47,78,126,225],"various":[48],"methods":[49,224],"augmenting":[51],"real":[52,194],"measured":[53,117],"data,":[54,230],"whereby":[55],"Generative":[57],"Adversarial":[58],"Network":[59],"(GAN)":[60],"one":[62],"the":[64,90,93,96,132,140,153,179,193,201,204,210,229,238,241],"most":[65],"suitable":[66],"approaches":[67,77,125],"synthetic":[69,86],"generation.":[71,88],"Conditional":[72],"non-conditional":[74,215],"augmentation":[76],"implementation":[81],"GAN":[83,101,105,124,181,216],"scope":[91],"proposed":[94],"paper,":[95],"performance":[97],"conditional":[100],"(cGAN)":[102],"traditional":[104,174],"evaluated":[107],"compared":[109,191],"vibration":[111],"rolling":[115],"bearing":[116,120],"on":[118],"test":[121],"rig.":[122],"Both":[123],"used":[127,226],"with":[128,213],"optimized":[129],"losses,":[130],"considering":[131],"Wasserstein":[133],"Distance":[134],"gradient":[137],"penalty":[138],"loss":[141,144],"function.":[142],"The":[143,176,187],"function":[145],"cGAN":[148],"contains":[149],"label":[150,170],"information,":[151],"enabling":[152],"model":[154],"be":[156],"trained":[157,168],"as":[158,171,209],"holistic":[160],"system":[161],"all":[163],"labels,":[164],"rather":[165],"than":[166],"being":[167],"per":[169],"GAN.":[175],"both":[180],"networks":[182],"show":[183],"high":[185],"performance.":[186],"synthetically":[188],"generated":[189,205,212,242],"also":[196],"shows":[197],"similarity.":[199],"distribution":[202],"different,":[208],"has":[217],"better":[219],"quality":[220],"results.":[222],"Different":[223],"evaluate":[228],"example":[232],"statistical":[233],"or":[234],"cluster":[235],"analysis,":[236],"highlighting":[237],"differences":[239],"data.":[243]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-11-06T00:00:00"}