{"id":"https://openalex.org/W4285291819","doi":"https://doi.org/10.1109/tsmc.2022.3185102","title":"Bayesian Deep-Learning-Based Prognostic Model for Equipment Without Label Data Related to Lifetime","display_name":"Bayesian Deep-Learning-Based Prognostic Model for Equipment Without Label Data Related to Lifetime","publication_year":2022,"publication_date":"2022-07-07","ids":{"openalex":"https://openalex.org/W4285291819","doi":"https://doi.org/10.1109/tsmc.2022.3185102"},"language":"en","primary_location":{"id":"doi:10.1109/tsmc.2022.3185102","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsmc.2022.3185102","pdf_url":null,"source":{"id":"https://openalex.org/S4210209078","display_name":"IEEE Transactions on Systems Man and Cybernetics Systems","issn_l":"2168-2216","issn":["2168-2216","2168-2232"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Systems, Man, and Cybernetics: Systems","raw_type":"journal-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/A5008031342","display_name":"Hong Pei","orcid":"https://orcid.org/0000-0002-9105-0120"},"institutions":[{"id":"https://openalex.org/I2801618472","display_name":"PLA Rocket Force University of Engineering","ror":"https://ror.org/00gg5zj35","country_code":"CN","type":"education","lineage":["https://openalex.org/I2801618472"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hong Pei","raw_affiliation_strings":["Department of Automation, Rocket Force University of Engineering, Xi&#x2019;an, China"],"affiliations":[{"raw_affiliation_string":"Department of Automation, Rocket Force University of Engineering, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I2801618472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067994505","display_name":"Xiaosheng Si","orcid":"https://orcid.org/0000-0001-5226-9923"},"institutions":[{"id":"https://openalex.org/I2801618472","display_name":"PLA Rocket Force University of Engineering","ror":"https://ror.org/00gg5zj35","country_code":"CN","type":"education","lineage":["https://openalex.org/I2801618472"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiao-Sheng Si","raw_affiliation_strings":["Department of Automation, Rocket Force University of Engineering, Xi&#x2019;an, China"],"affiliations":[{"raw_affiliation_string":"Department of Automation, Rocket Force University of Engineering, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I2801618472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057912398","display_name":"Changhua Hu","orcid":"https://orcid.org/0000-0002-1545-9100"},"institutions":[{"id":"https://openalex.org/I2801618472","display_name":"PLA Rocket Force University of Engineering","ror":"https://ror.org/00gg5zj35","country_code":"CN","type":"education","lineage":["https://openalex.org/I2801618472"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Changhua Hu","raw_affiliation_strings":["Department of Automation, Rocket Force University of Engineering, Xi&#x2019;an, China"],"affiliations":[{"raw_affiliation_string":"Department of Automation, Rocket Force University of Engineering, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I2801618472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083714859","display_name":"Tianmei Li","orcid":"https://orcid.org/0000-0002-4181-265X"},"institutions":[{"id":"https://openalex.org/I2801618472","display_name":"PLA Rocket Force University of Engineering","ror":"https://ror.org/00gg5zj35","country_code":"CN","type":"education","lineage":["https://openalex.org/I2801618472"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianmei Li","raw_affiliation_strings":["Department of Automation, Rocket Force University of Engineering, Xi&#x2019;an, China"],"affiliations":[{"raw_affiliation_string":"Department of Automation, Rocket Force University of Engineering, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I2801618472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100749864","display_name":"Chuan He","orcid":"https://orcid.org/0000-0003-1314-2377"},"institutions":[{"id":"https://openalex.org/I2801618472","display_name":"PLA Rocket Force University of Engineering","ror":"https://ror.org/00gg5zj35","country_code":"CN","type":"education","lineage":["https://openalex.org/I2801618472"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chuan He","raw_affiliation_strings":["Department of Automation, Rocket Force University of Engineering, Xi&#x2019;an, China"],"affiliations":[{"raw_affiliation_string":"Department of Automation, Rocket Force University of Engineering, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I2801618472"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081959415","display_name":"Zhenan Pang","orcid":"https://orcid.org/0000-0003-4309-4003"},"institutions":[{"id":"https://openalex.org/I2801618472","display_name":"PLA Rocket Force University of Engineering","ror":"https://ror.org/00gg5zj35","country_code":"CN","type":"education","lineage":["https://openalex.org/I2801618472"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenan Pang","raw_affiliation_strings":["Department of Automation, Rocket Force University of Engineering, Xi&#x2019;an, China"],"affiliations":[{"raw_affiliation_string":"Department of Automation, Rocket Force University of Engineering, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I2801618472"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5008031342"],"corresponding_institution_ids":["https://openalex.org/I2801618472"],"apc_list":null,"apc_paid":null,"fwci":5.2463,"has_fulltext":false,"cited_by_count":34,"citation_normalized_percentile":{"value":0.96249306,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"53","issue":"1","first_page":"504","last_page":"517"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10780","display_name":"Reliability and Maintenance Optimization","score":0.9980999827384949,"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"}},"topics":[{"id":"https://openalex.org/T10780","display_name":"Reliability and Maintenance Optimization","score":0.9980999827384949,"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"}},{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9945999979972839,"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":0.9927999973297119,"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/deep-learning","display_name":"Deep learning","score":0.7444731593132019},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.742295503616333},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.734189510345459},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.649489164352417},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.639159083366394},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.5949521064758301},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.5763731598854065},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5652727484703064},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.536419153213501},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.5309284329414368},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5002498626708984},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5001230239868164},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.4953116476535797},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4292655885219574},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.4223247766494751}],"concepts":[{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7444731593132019},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.742295503616333},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.734189510345459},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.649489164352417},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.639159083366394},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5949521064758301},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.5763731598854065},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5652727484703064},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.536419153213501},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.5309284329414368},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5002498626708984},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5001230239868164},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.4953116476535797},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4292655885219574},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.4223247766494751},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tsmc.2022.3185102","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsmc.2022.3185102","pdf_url":null,"source":{"id":"https://openalex.org/S4210209078","display_name":"IEEE Transactions on Systems Man and Cybernetics Systems","issn_l":"2168-2216","issn":["2168-2216","2168-2232"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Systems, Man, and Cybernetics: Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3007177163","display_name":null,"funder_award_id":"62103433","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4747377716","display_name":null,"funder_award_id":"62073336","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5852167102","display_name":null,"funder_award_id":"61922089","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7343347525","display_name":null,"funder_award_id":"61903376","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W582134693","https://openalex.org/W1977002251","https://openalex.org/W2047152377","https://openalex.org/W2144352195","https://openalex.org/W2225156818","https://openalex.org/W2375891406","https://openalex.org/W2511340798","https://openalex.org/W2564947831","https://openalex.org/W2606080248","https://openalex.org/W2617137613","https://openalex.org/W2773549135","https://openalex.org/W2927135077","https://openalex.org/W2944647284","https://openalex.org/W2944676531","https://openalex.org/W2953498979","https://openalex.org/W2981973403","https://openalex.org/W2984413472","https://openalex.org/W2985380938","https://openalex.org/W3011593554","https://openalex.org/W3072054496","https://openalex.org/W3095770430","https://openalex.org/W3159184215","https://openalex.org/W3177862163","https://openalex.org/W4220842087","https://openalex.org/W4220911024","https://openalex.org/W6617145748"],"related_works":["https://openalex.org/W2117545158","https://openalex.org/W3013612038","https://openalex.org/W2407375987","https://openalex.org/W2505726097","https://openalex.org/W2950975704","https://openalex.org/W3049691116","https://openalex.org/W2010643158","https://openalex.org/W2106867672","https://openalex.org/W4310268968","https://openalex.org/W3081214562"],"abstract_inverted_index":{"Deep":[0],"learning":[1,97,113,185],"has":[2],"become":[3],"a":[4,90,104,151],"promising":[5],"tool":[6],"for":[7,93,114,166,222],"processing":[8],"the":[9,18,37,43,46,52,57,62,72,79,83,95,108,117,124,128,132,139,145,154,163,167,171,176,180,189,196,199,209,214,219,223,234,241,245,248,254,263],"massive":[10],"data":[11,85,119,126,134],"and":[12,23,131],"attracted":[13],"an":[14],"increasing":[15],"attention":[16],"in":[17,65,138,169,213],"fields":[19],"of":[20,40,56,78,110,127,135,178,182,198,247],"degradation":[21,148,201,235],"modeling":[22],"remaining":[24],"useful":[25],"life":[26],"(RUL)":[27],"prediction.":[28],"The":[29],"existing":[30],"deep-learning-based":[31],"methods":[32],"are":[33,49,141],"generally":[34],"faced":[35],"with":[36,258],"two":[38],"aspects":[39],"problems.":[41],"On":[42,71,175],"one":[44],"hand,":[45,74],"prediction":[47,67],"results":[48],"represented":[50],"by":[51,232],"point":[53,246],"estimates":[54],"instead":[55],"probabilistic":[58],"distribution":[59],"and,":[60],"thus,":[61,192],"prognostic":[63,105,265],"uncertainty":[64,197,236,243],"RUL":[66,242],"cannot":[68],"be":[69,230,238],"characterized.":[70],"other":[73],"there":[75],"exist":[76],"plenty":[77],"engineering":[80],"assets":[81],"without":[82],"label":[84,118],"related":[86,120],"to":[87,121,143,261],"lifetime,":[88],"posing":[89],"great":[91],"challenge":[92],"training":[94],"deep":[96,112,184],"network.":[98],"Toward":[99],"this":[100],"end,":[101],"we":[102,193,252],"propose":[103],"model":[106],"under":[107],"framework":[109],"Bayesian":[111,183,215],"equipment":[115,130,137,225],"lacking":[116],"lifetime.":[122],"First,":[123],"monitoring":[125,173],"historical":[129,133],"field":[136],"database":[140],"preprocessed":[142],"generate":[144],"samples":[146],"regarding":[147],"information":[149],"as":[150,162],"label.":[152],"Second,":[153],"bidirectional":[155,190],"recurrent":[156],"neural":[157,216],"network":[158,165],"(RNN)":[159],"is":[160,186],"employed":[161],"candidate":[164],"advantages":[168],"handling":[170],"sequential":[172],"data.":[174],"basis":[177],"this,":[179],"idea":[181],"incorporated":[187],"into":[188,240],"RNN;":[191],"can":[194,229,237],"characterize":[195],"predicted":[200],"level":[202],"at":[203,226],"any":[204,227],"future":[205],"time":[206,228],"via":[207],"utilizing":[208],"variational":[210],"inference":[211],"technique":[212],"networks.":[217],"Furthermore,":[218],"failure":[220],"probability":[221],"concerned":[224],"determined,":[231],"which":[233],"converted":[239],"from":[244],"reliability":[249],"theory.":[250],"Finally,":[251],"provide":[253],"case":[255],"study":[256],"associated":[257],"lithium-ion":[259],"batteries":[260],"verify":[262],"proposed":[264],"model.":[266]},"counts_by_year":[{"year":2025,"cited_by_count":14},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":4}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
