{"id":"https://openalex.org/W4385484642","doi":"https://doi.org/10.1109/ijcnn54540.2023.10191827","title":"Uncertainty Aware Deep Learning for Fault Prediction Using Multivariate Time Series Signals","display_name":"Uncertainty Aware Deep Learning for Fault Prediction Using Multivariate Time Series Signals","publication_year":2023,"publication_date":"2023-06-18","ids":{"openalex":"https://openalex.org/W4385484642","doi":"https://doi.org/10.1109/ijcnn54540.2023.10191827"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn54540.2023.10191827","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn54540.2023.10191827","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.osti.gov/biblio/2283148","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5108729529","display_name":"Md Monibor Rahman","orcid":null},"institutions":[{"id":"https://openalex.org/I81365321","display_name":"Old Dominion University","ror":"https://ror.org/04zjtrb98","country_code":"US","type":"education","lineage":["https://openalex.org/I81365321"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Md Monibor Rahman","raw_affiliation_strings":["Old Dominion University,Vision Lab,Department of Electrical and Computer Engineering,Norfolk,VA,USA","Department of Electrical and Computer Engineering, Vision Lab, Old Dominion University, Norfolk, VA, USA"],"affiliations":[{"raw_affiliation_string":"Old Dominion University,Vision Lab,Department of Electrical and Computer Engineering,Norfolk,VA,USA","institution_ids":["https://openalex.org/I81365321"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Vision Lab, Old Dominion University, Norfolk, VA, USA","institution_ids":["https://openalex.org/I81365321"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080203059","display_name":"Lasitha Vidyaratne","orcid":"https://orcid.org/0000-0003-4053-7948"},"institutions":[{"id":"https://openalex.org/I29801172","display_name":"Thomas Jefferson National Accelerator Facility","ror":"https://ror.org/02vwzrd76","country_code":"US","type":"facility","lineage":["https://openalex.org/I1330989302","https://openalex.org/I29801172","https://openalex.org/I39565521"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"L. Vidyaratne","raw_affiliation_strings":["Jefferson Laboratory,Newport News,VA,USA","Jefferson Laboratory, Newport News, VA, USA"],"affiliations":[{"raw_affiliation_string":"Jefferson Laboratory,Newport News,VA,USA","institution_ids":["https://openalex.org/I29801172"]},{"raw_affiliation_string":"Jefferson Laboratory, Newport News, VA, USA","institution_ids":["https://openalex.org/I29801172"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023115758","display_name":"Adam Carpenter","orcid":"https://orcid.org/0000-0002-5559-2213"},"institutions":[{"id":"https://openalex.org/I29801172","display_name":"Thomas Jefferson National Accelerator Facility","ror":"https://ror.org/02vwzrd76","country_code":"US","type":"facility","lineage":["https://openalex.org/I1330989302","https://openalex.org/I29801172","https://openalex.org/I39565521"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"A. Carpenter","raw_affiliation_strings":["Jefferson Laboratory,Newport News,VA,USA","Jefferson Laboratory, Newport News, VA, USA"],"affiliations":[{"raw_affiliation_string":"Jefferson Laboratory,Newport News,VA,USA","institution_ids":["https://openalex.org/I29801172"]},{"raw_affiliation_string":"Jefferson Laboratory, Newport News, VA, USA","institution_ids":["https://openalex.org/I29801172"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053531437","display_name":"Chris Tennant","orcid":"https://orcid.org/0000-0003-3814-8417"},"institutions":[{"id":"https://openalex.org/I29801172","display_name":"Thomas Jefferson National Accelerator Facility","ror":"https://ror.org/02vwzrd76","country_code":"US","type":"facility","lineage":["https://openalex.org/I1330989302","https://openalex.org/I29801172","https://openalex.org/I39565521"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"C. Tennant","raw_affiliation_strings":["Jefferson Laboratory,Newport News,VA,USA","Jefferson Laboratory, Newport News, VA, USA"],"affiliations":[{"raw_affiliation_string":"Jefferson Laboratory,Newport News,VA,USA","institution_ids":["https://openalex.org/I29801172"]},{"raw_affiliation_string":"Jefferson Laboratory, Newport News, VA, USA","institution_ids":["https://openalex.org/I29801172"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081863591","display_name":"Khan M. Iftekharuddin","orcid":"https://orcid.org/0000-0001-8316-4163"},"institutions":[{"id":"https://openalex.org/I81365321","display_name":"Old Dominion University","ror":"https://ror.org/04zjtrb98","country_code":"US","type":"education","lineage":["https://openalex.org/I81365321"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"K. Iftekharuddin","raw_affiliation_strings":["Old Dominion University,Vision Lab,Department of Electrical and Computer Engineering,Norfolk,VA,USA","Department of Electrical and Computer Engineering, Vision Lab, Old Dominion University, Norfolk, VA, USA"],"affiliations":[{"raw_affiliation_string":"Old Dominion University,Vision Lab,Department of Electrical and Computer Engineering,Norfolk,VA,USA","institution_ids":["https://openalex.org/I81365321"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Vision Lab, Old Dominion University, Norfolk, VA, USA","institution_ids":["https://openalex.org/I81365321"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5108729529"],"corresponding_institution_ids":["https://openalex.org/I81365321"],"apc_list":null,"apc_paid":null,"fwci":1.5228,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.88363622,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11367","display_name":"Particle accelerators and beam dynamics","score":0.993399977684021,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T11367","display_name":"Particle accelerators and beam dynamics","score":0.993399977684021,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T13050","display_name":"Oil and Gas Production Techniques","score":0.9478999972343445,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/T11220","display_name":"Water Systems and Optimization","score":0.9398999810218811,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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":0.7416345477104187},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.727830171585083},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6595445871353149},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6029662489891052},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5874109268188477},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.5777842998504639},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.525540292263031},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.5061606764793396},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4766022562980652},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4765501618385315},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.46228504180908203},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4472358524799347},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.34599339962005615},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.12004789710044861}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7416345477104187},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.727830171585083},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6595445871353149},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6029662489891052},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5874109268188477},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.5777842998504639},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.525540292263031},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.5061606764793396},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4766022562980652},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4765501618385315},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.46228504180908203},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4472358524799347},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34599339962005615},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.12004789710044861},{"id":"https://openalex.org/C165205528","wikidata":"https://www.wikidata.org/wiki/Q83371","display_name":"Seismology","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/ijcnn54540.2023.10191827","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn54540.2023.10191827","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:osti.gov:2283148","is_oa":true,"landing_page_url":"https://www.osti.gov/biblio/2283148","pdf_url":null,"source":{"id":"https://openalex.org/S4306402487","display_name":"OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I139351228","host_organization_name":"Office of Scientific and Technical Information","host_organization_lineage":["https://openalex.org/I139351228"],"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":null}],"best_oa_location":{"id":"pmh:oai:osti.gov:2283148","is_oa":true,"landing_page_url":"https://www.osti.gov/biblio/2283148","pdf_url":null,"source":{"id":"https://openalex.org/S4306402487","display_name":"OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I139351228","host_organization_name":"Office of Scientific and Technical Information","host_organization_lineage":["https://openalex.org/I139351228"],"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":null},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306084","display_name":"U.S. Department of Energy","ror":"https://ror.org/01bj3aw27"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W582134693","https://openalex.org/W1499864241","https://openalex.org/W1981433115","https://openalex.org/W2035115911","https://openalex.org/W2606101940","https://openalex.org/W2780628352","https://openalex.org/W2944342107","https://openalex.org/W2966938419","https://openalex.org/W2972137370","https://openalex.org/W2978157082","https://openalex.org/W2994992425","https://openalex.org/W3034538648","https://openalex.org/W3035667192","https://openalex.org/W3046605870","https://openalex.org/W3048221011","https://openalex.org/W3093573961","https://openalex.org/W3107874572","https://openalex.org/W3127350863","https://openalex.org/W3138452546","https://openalex.org/W3177169361","https://openalex.org/W4205223274","https://openalex.org/W4205702820","https://openalex.org/W4206070770","https://openalex.org/W4286542829","https://openalex.org/W4287184087","https://openalex.org/W4296974348","https://openalex.org/W4320351089","https://openalex.org/W4383336414","https://openalex.org/W6841025777","https://openalex.org/W6962919036"],"related_works":["https://openalex.org/W3082178636","https://openalex.org/W2782041652","https://openalex.org/W2612657834","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3167935049","https://openalex.org/W3029198973"],"abstract_inverted_index":{"The":[0,92],"superconducting":[1],"radio-frequency":[2],"cavities":[3],"are":[4],"a":[5,20,49,135,141],"crucial":[6],"component":[7],"of":[8,32,132,138,171],"the":[9,83,90,113,116,150,155,161,169,172,191],"Continuous":[10],"Electron":[11],"Beam":[12],"Accelerator":[13],"Facility":[14],"(CEBAF)":[15],"at":[16],"Jefferson":[17],"Lab.":[18],"When":[19],"cavity":[21,33,103],"faults,":[22],"beam":[23],"delivery":[24],"to":[25,36,40,64,86,160],"experimental":[26],"end":[27],"users":[28],"is":[29,38,62,146],"disrupted.":[30],"Prediction":[31],"faults":[34,67,121],"prior":[35],"onset":[37,124],"essential":[39],"reduce":[41],"operation":[42],"and":[43,105,134],"maintenance":[44],"costs.":[45],"In":[46],"this":[47],"work,":[48],"parallel":[50,183],"long":[51],"short-term":[52],"memory":[53],"(LSTM)-convolutional":[54],"neural":[55],"network":[56],"(CNN)-based":[57],"deep":[58],"learning":[59],"(DL)":[60],"model":[61,85,93,117,151,173],"proposed":[63,182],"predict":[65],"impending":[66,120],"using":[68,78,96,140],"pre-fault":[69],"signals.":[70],"Further,":[71],"we":[72,167],"introduce":[73],"an":[74,126],"uncertainty":[75],"quantification":[76],"approach":[77],"Monte":[79],"Carlo":[80],"dropout":[81],"with":[82,125,174],"LSTM-CNN":[84],"ascertain":[87],"confidence":[88],"in":[89],"prediction.":[91],"was":[94],"tested":[95],"multivariate":[97],"time":[98,143,157],"series":[99],"signals":[100],"from":[101],"stable":[102],"operations":[104],"before":[106,122],"faults.":[107],"Initial":[108],"results":[109],"show":[110],"that":[111,149],"on":[112],"test":[114],"dataset,":[115],"can":[118],"identify":[119],"their":[123],"average":[127],"10-fold":[128],"cross":[129],"validation":[130],"accuracy":[131],"97.39%":[133],"standard":[136],"deviation":[137],"0.12%":[139],"100-ms":[142],"window.":[144],"It":[145],"also":[147],"observed":[148],"performs":[152],"better":[153,188],"as":[154],"prediction":[156,179],"moves":[158],"closer":[159],"fault":[162,178],"onset.":[163],"For":[164],"additional":[165],"context,":[166],"compare":[168],"performance":[170,189],"three":[175],"machine-learning":[176],"(ML)-based":[177],"models.":[180],"Our":[181],"LSTM-CNN-based":[184],"DL":[185],"method":[186],"shows":[187],"than":[190],"ML-based":[192],"methods.":[193]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}