{"id":"https://openalex.org/W3084085903","doi":"https://doi.org/10.1109/icphm49022.2020.9187063","title":"Estimating and Leveraging Uncertainties in Deep Learning for Remaining Useful Life Prediction in Mechanical Systems","display_name":"Estimating and Leveraging Uncertainties in Deep Learning for Remaining Useful Life Prediction in Mechanical Systems","publication_year":2020,"publication_date":"2020-06-01","ids":{"openalex":"https://openalex.org/W3084085903","doi":"https://doi.org/10.1109/icphm49022.2020.9187063","mag":"3084085903"},"language":"en","primary_location":{"id":"doi:10.1109/icphm49022.2020.9187063","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icphm49022.2020.9187063","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Prognostics and Health Management (ICPHM)","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/A5067179798","display_name":"Jackson Cornelius","orcid":null},"institutions":[{"id":"https://openalex.org/I4210153148","display_name":"CFD Research Corporation (United States)","ror":"https://ror.org/04wd31514","country_code":"US","type":"company","lineage":["https://openalex.org/I4210153148"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jackson Cornelius","raw_affiliation_strings":["CFD Research Corporation, Huntsville, AL"],"affiliations":[{"raw_affiliation_string":"CFD Research Corporation, Huntsville, AL","institution_ids":["https://openalex.org/I4210153148"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051812239","display_name":"Blake Brockner","orcid":null},"institutions":[{"id":"https://openalex.org/I4210153148","display_name":"CFD Research Corporation (United States)","ror":"https://ror.org/04wd31514","country_code":"US","type":"company","lineage":["https://openalex.org/I4210153148"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Blake Brockner","raw_affiliation_strings":["CFD Research Corporation, Huntsville, AL"],"affiliations":[{"raw_affiliation_string":"CFD Research Corporation, Huntsville, AL","institution_ids":["https://openalex.org/I4210153148"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031195567","display_name":"Seong Hyeon Hong","orcid":"https://orcid.org/0000-0003-4747-7669"},"institutions":[{"id":"https://openalex.org/I155781252","display_name":"University of South Carolina","ror":"https://ror.org/02b6qw903","country_code":"US","type":"education","lineage":["https://openalex.org/I155781252"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Seong Hyeon Hong","raw_affiliation_strings":["University of South Carolina, Columbia, SC"],"affiliations":[{"raw_affiliation_string":"University of South Carolina, Columbia, SC","institution_ids":["https://openalex.org/I155781252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100364960","display_name":"Yi Wang","orcid":"https://orcid.org/0000-0002-5750-3181"},"institutions":[{"id":"https://openalex.org/I155781252","display_name":"University of South Carolina","ror":"https://ror.org/02b6qw903","country_code":"US","type":"education","lineage":["https://openalex.org/I155781252"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yi Wang","raw_affiliation_strings":["University of South Carolina, Columbia, SC"],"affiliations":[{"raw_affiliation_string":"University of South Carolina, Columbia, SC","institution_ids":["https://openalex.org/I155781252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108419301","display_name":"Kapil Pant","orcid":null},"institutions":[{"id":"https://openalex.org/I4210153148","display_name":"CFD Research Corporation (United States)","ror":"https://ror.org/04wd31514","country_code":"US","type":"company","lineage":["https://openalex.org/I4210153148"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kapil Pant","raw_affiliation_strings":["CFD Research Corporation, Huntsville, AL"],"affiliations":[{"raw_affiliation_string":"CFD Research Corporation, Huntsville, AL","institution_ids":["https://openalex.org/I4210153148"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081134905","display_name":"John E. Ball","orcid":"https://orcid.org/0000-0002-6774-4851"},"institutions":[{"id":"https://openalex.org/I99041443","display_name":"Mississippi State University","ror":"https://ror.org/0432jq872","country_code":"US","type":"education","lineage":["https://openalex.org/I4210141039","https://openalex.org/I99041443"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"John Ball","raw_affiliation_strings":["Mississippi State University, Starkville, MS"],"affiliations":[{"raw_affiliation_string":"Mississippi State University, Starkville, MS","institution_ids":["https://openalex.org/I99041443"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5067179798"],"corresponding_institution_ids":["https://openalex.org/I4210153148"],"apc_list":null,"apc_paid":null,"fwci":1.1769,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.78643208,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9962999820709229,"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":0.9962999820709229,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9940000176429749,"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/T10780","display_name":"Reliability and Maintenance Optimization","score":0.9926000237464905,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/prognostics","display_name":"Prognostics","score":0.885134756565094},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.6140339970588684},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5987340807914734},{"id":"https://openalex.org/keywords/heteroscedasticity","display_name":"Heteroscedasticity","score":0.5652373433113098},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.563714325428009},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5620447993278503},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5185413956642151},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4958058297634125},{"id":"https://openalex.org/keywords/uncertainty-quantification","display_name":"Uncertainty quantification","score":0.47482165694236755},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.41589587926864624}],"concepts":[{"id":"https://openalex.org/C129364497","wikidata":"https://www.wikidata.org/wiki/Q3042561","display_name":"Prognostics","level":2,"score":0.885134756565094},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.6140339970588684},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5987340807914734},{"id":"https://openalex.org/C101104100","wikidata":"https://www.wikidata.org/wiki/Q1063540","display_name":"Heteroscedasticity","level":2,"score":0.5652373433113098},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.563714325428009},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5620447993278503},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5185413956642151},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4958058297634125},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.47482165694236755},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41589587926864624}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icphm49022.2020.9187063","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icphm49022.2020.9187063","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Prognostics and Health Management (ICPHM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1560021816","https://openalex.org/W1921523184","https://openalex.org/W2013821261","https://openalex.org/W2055873761","https://openalex.org/W2062613656","https://openalex.org/W2067707835","https://openalex.org/W2110787940","https://openalex.org/W2120841219","https://openalex.org/W2124181495","https://openalex.org/W2150606224","https://openalex.org/W2280163991","https://openalex.org/W2344741583","https://openalex.org/W2415594836","https://openalex.org/W2471161958","https://openalex.org/W2516566105","https://openalex.org/W2519091744","https://openalex.org/W2744067593","https://openalex.org/W2768148617","https://openalex.org/W2772084711","https://openalex.org/W2792764867","https://openalex.org/W2889347686","https://openalex.org/W2902700103","https://openalex.org/W2947133609","https://openalex.org/W2949382160","https://openalex.org/W2950517871","https://openalex.org/W6631190155","https://openalex.org/W6640185926","https://openalex.org/W6695011786","https://openalex.org/W6749825310"],"related_works":["https://openalex.org/W2310476526","https://openalex.org/W3213192587","https://openalex.org/W2144291498","https://openalex.org/W2535730979","https://openalex.org/W3193475673","https://openalex.org/W2370073012","https://openalex.org/W4386567722","https://openalex.org/W2168646784","https://openalex.org/W2965730886","https://openalex.org/W2030958945"],"abstract_inverted_index":{"Many":[0],"researchers":[1],"in":[2,34,64,82,106,151,178,190],"the":[3,12,32,43,56,132,152,164,168,181,187,191,196,204,219],"prognostics":[4],"and":[5,76,93,136,147,215,224],"health":[6],"management":[7],"community":[8],"have":[9,29,185],"begun":[10],"exploring":[11],"use":[13],"of":[14,24,59,180],"deep":[15,83,121],"neural":[16,84,122],"networks":[17],"for":[18,72,90],"predicting":[19],"remaining":[20],"useful":[21],"life":[22],"(RUL)":[23],"mechanical":[25],"systems.":[26],"These":[27],"models":[28,86],"consistently":[30],"reestablished":[31],"state-of-the-art":[33],"RUL":[35,91,153,169,206],"prediction":[36,92,154],"performance":[37,130],"on":[38,100,131,186,195],"common":[39],"benchmarks,":[40],"such":[41],"as":[42],"NASA":[44,133],"C-MAPSS":[45,134,197],"Aircraft":[46],"Engine":[47],"dataset.":[48],"However,":[49],"they":[50],"do":[51],"not":[52],"attempt":[53],"to":[54,144,162],"capture":[55],"multiple":[57],"sources":[58],"uncertainty":[60,213],"that":[61,80,87,95,127,166,202],"are":[62,88,111],"inherent":[63],"their":[65,97],"predictions.":[66],"This":[67],"paper":[68],"presents":[69],"an":[70,225],"approach":[71],"estimating":[73],"both":[74],"epistemic":[75,146],"heteroscedastic":[77,148],"aleatoric":[78,149],"uncertainties":[79,150,189],"emerge":[81],"network":[85,123,141],"trained":[89],"demonstrates":[94,128],"quantifying":[96],"overall":[98],"impact":[99],"predictions":[101],"can":[102,209],"be":[103],"extremely":[104],"valuable":[105],"real-world":[107],"systems,":[108],"where":[109],"decisions":[110],"sometimes":[112],"made":[113],"during":[114],"uncertain":[115],"operating":[116],"conditions.":[117],"First,":[118],"a":[119,157],"novel":[120],"architecture":[124],"is":[125,142,159],"proposed":[126],"competitive":[129],"FD001":[135,198],"FD003":[137],"datasets.":[138],"Then,":[139],"this":[140],"adapted":[143],"estimate":[145],"problem.":[155],"Finally,":[156],"study":[158],"carried":[160],"out":[161],"observe":[163],"effects":[165],"augmenting":[167],"truth":[170,176,183,207],"data,":[171,184],"i.e.":[172],"utilizing":[173,203],"piecewise":[174],"linear":[175],"curves":[177],"place":[179],"actual":[182,205],"perceived":[188],"system.":[192],"Case":[193],"studies":[194],"dataset":[199],"will":[200],"show":[201],"data":[208,223],"yield":[210],"more":[211,216],"meaningful":[212],"estimates":[214],"insight":[217],"into":[218],"relationship":[220],"between":[221],"sensor":[222],"engine's":[226],"time-to-failure.":[227]},"counts_by_year":[{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}