{"id":"https://openalex.org/W4403675163","doi":"https://doi.org/10.1109/case59546.2024.10711317","title":"Synthetic Data Generation with Reinforcement Learning for Fault Diagnosis of Rolling Bearings","display_name":"Synthetic Data Generation with Reinforcement Learning for Fault Diagnosis of Rolling Bearings","publication_year":2024,"publication_date":"2024-08-28","ids":{"openalex":"https://openalex.org/W4403675163","doi":"https://doi.org/10.1109/case59546.2024.10711317"},"language":"en","primary_location":{"id":"doi:10.1109/case59546.2024.10711317","is_oa":false,"landing_page_url":"https://doi.org/10.1109/case59546.2024.10711317","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 20th International Conference on Automation Science and Engineering (CASE)","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/A5062806794","display_name":"Tenta Komatsu","orcid":"https://orcid.org/0009-0000-1910-3972"},"institutions":[{"id":"https://openalex.org/I1283155146","display_name":"Panasonic (Japan)","ror":"https://ror.org/011tm7n37","country_code":"JP","type":"company","lineage":["https://openalex.org/I1283155146"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Tenta Komatsu","raw_affiliation_strings":["Panasonic Industry Co., Ltd.,Kadoma City, Osaka,Japan,571-8506"],"affiliations":[{"raw_affiliation_string":"Panasonic Industry Co., Ltd.,Kadoma City, Osaka,Japan,571-8506","institution_ids":["https://openalex.org/I1283155146"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019988198","display_name":"Kyaw Htun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kyaw Myo Htun","raw_affiliation_strings":["Panasonic Industrial Devices Singapore Pte. Ltd.,Singapore,Singapore,469269"],"affiliations":[{"raw_affiliation_string":"Panasonic Industrial Devices Singapore Pte. Ltd.,Singapore,Singapore,469269","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072055231","display_name":"Zhiqi Liu","orcid":"https://orcid.org/0000-0003-4735-3092"},"institutions":[{"id":"https://openalex.org/I1283155146","display_name":"Panasonic (Japan)","ror":"https://ror.org/011tm7n37","country_code":"JP","type":"company","lineage":["https://openalex.org/I1283155146"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Liu Zhiqi","raw_affiliation_strings":["Panasonic Industry Co., Ltd.,Kadoma City, Osaka,Japan,571-8506"],"affiliations":[{"raw_affiliation_string":"Panasonic Industry Co., Ltd.,Kadoma City, Osaka,Japan,571-8506","institution_ids":["https://openalex.org/I1283155146"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011835586","display_name":"Muhammad Usman","orcid":"https://orcid.org/0000-0002-1811-5207"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Muhammad Usman","raw_affiliation_strings":["Panasonic Industrial Devices Singapore Pte. Ltd.,Singapore,Singapore,469269"],"affiliations":[{"raw_affiliation_string":"Panasonic Industrial Devices Singapore Pte. Ltd.,Singapore,Singapore,469269","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5114371794","display_name":"Faye Juliano","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Faye Juliano","raw_affiliation_strings":["Panasonic Industrial Devices Singapore Pte. Ltd.,Singapore,Singapore,469269"],"affiliations":[{"raw_affiliation_string":"Panasonic Industrial Devices Singapore Pte. Ltd.,Singapore,Singapore,469269","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5062806794"],"corresponding_institution_ids":["https://openalex.org/I1283155146"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.18852379,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3195","last_page":"3200"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11062","display_name":"Gear and Bearing Dynamics Analysis","score":0.9984999895095825,"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"}},"topics":[{"id":"https://openalex.org/T11062","display_name":"Gear and Bearing Dynamics Analysis","score":0.9984999895095825,"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/T11583","display_name":"Advanced Measurement and Metrology Techniques","score":0.9847999811172485,"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/T11138","display_name":"Tribology and Lubrication Engineering","score":0.9843000173568726,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.7210305333137512},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6121980547904968},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.5951108932495117},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5610132813453674},{"id":"https://openalex.org/keywords/reinforcement","display_name":"Reinforcement","score":0.4268917441368103},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.42545977234840393},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.28393280506134033},{"id":"https://openalex.org/keywords/structural-engineering","display_name":"Structural engineering","score":0.18295466899871826},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.1000971794128418},{"id":"https://openalex.org/keywords/seismology","display_name":"Seismology","score":0.07408425211906433}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7210305333137512},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6121980547904968},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.5951108932495117},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5610132813453674},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.4268917441368103},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42545977234840393},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.28393280506134033},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.18295466899871826},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.1000971794128418},{"id":"https://openalex.org/C165205528","wikidata":"https://www.wikidata.org/wiki/Q83371","display_name":"Seismology","level":1,"score":0.07408425211906433}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/case59546.2024.10711317","is_oa":false,"landing_page_url":"https://doi.org/10.1109/case59546.2024.10711317","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 20th International Conference on Automation Science and Engineering (CASE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W243674440","https://openalex.org/W1964511482","https://openalex.org/W1970784519","https://openalex.org/W2548223595","https://openalex.org/W2605102758","https://openalex.org/W2732499510","https://openalex.org/W2767050701","https://openalex.org/W2791694051","https://openalex.org/W2907864265","https://openalex.org/W3001741353","https://openalex.org/W3179976630","https://openalex.org/W3196282989","https://openalex.org/W4285021310","https://openalex.org/W4387445262","https://openalex.org/W6747473740","https://openalex.org/W6754779071","https://openalex.org/W6755353450","https://openalex.org/W6757592117","https://openalex.org/W6804601995"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W3107602296","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"Training":[0],"deep":[1,150],"neural":[2,151],"networks":[3],"on":[4,37,154],"synthetic":[5,38,85,156],"data":[6,16,23,39,86],"is":[7,64,92,118,179],"a":[8,84,101,149,155],"promising":[9],"method":[10],"for":[11],"solving":[12],"the":[13,26,32,49,53,70,96,104,107,111,115,123,128,134,162,180],"problems":[14],"of":[15,22,28,34,52,73,100,110,133],"scarcity,":[17],"label":[18],"noise,":[19],"and":[20,106,142],"lack":[21],"diversity.":[24],"In":[25,81],"field":[27],"equipment":[29,54,75,78],"maintenance":[30,79],"automation,":[31],"training":[33],"diagnostic":[35,167],"models":[36],"has":[40],"also":[41],"been":[42],"studied.":[43],"These":[44],"conventional":[45],"studies":[46],"assume":[47],"that":[48,148],"simulation":[50,71,112,124,175],"parameters":[51,72,113,125],"to":[55,67,120,182,186],"be":[56,59],"diagnosed":[57],"can":[58],"accurately":[60,68],"determined.":[61],"However,":[62],"it":[63,117],"not":[65],"easy":[66],"determine":[69],"all":[74],"at":[76],"actual":[77],"sites.":[80],"this":[82],"study,":[83],"generation":[87],"framework":[88,136],"using":[89,139,161],"reinforcement":[90,184],"learning":[91,185],"proposed.":[93],"By":[94],"defining":[95],"fault":[97],"characteristic":[98],"frequencies":[99],"bearing":[102,188],"as":[103,114],"state":[105],"update":[108],"amount":[109],"action,":[116],"possible":[119],"efficiently":[121],"sample":[122],"without":[126],"knowing":[127],"accurate":[129],"values.":[130],"The":[131,145],"effectiveness":[132],"proposed":[135,163],"was":[137,159],"verified":[138],"both":[140],"open-access":[141],"in-house":[143],"datasets.":[144],"results":[146],"show":[147],"network":[152],"trained":[153,171],"dataset,":[157],"which":[158],"generated":[160],"framework,":[164],"achieved":[165],"higher":[166],"accuracy":[168],"than":[169],"one":[170],"with":[172],"randomly":[173],"sampled":[174],"parameters.":[176],"This":[177],"research":[178],"first":[181],"use":[183],"generate":[187],"anomaly":[189],"data.":[190]},"counts_by_year":[],"updated_date":"2025-12-21T01:58:51.020947","created_date":"2025-10-10T00:00:00"}
