{"id":"https://openalex.org/W2956342231","doi":"https://doi.org/10.1109/tie.2019.2924605","title":"Remaining Useful Life Prediction Based on a Double-Convolutional Neural Network Architecture","display_name":"Remaining Useful Life Prediction Based on a Double-Convolutional Neural Network Architecture","publication_year":2019,"publication_date":"2019-07-12","ids":{"openalex":"https://openalex.org/W2956342231","doi":"https://doi.org/10.1109/tie.2019.2924605","mag":"2956342231"},"language":"en","primary_location":{"id":"doi:10.1109/tie.2019.2924605","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tie.2019.2924605","pdf_url":null,"source":{"id":"https://openalex.org/S58031724","display_name":"IEEE Transactions on Industrial Electronics","issn_l":"0278-0046","issn":["0278-0046","1557-9948"],"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 Industrial Electronics","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/A5009872730","display_name":"Boyuan Yang","orcid":"https://orcid.org/0000-0002-5248-0929"},"institutions":[{"id":"https://openalex.org/I28407311","display_name":"University of Manchester","ror":"https://ror.org/027m9bs27","country_code":"GB","type":"education","lineage":["https://openalex.org/I28407311"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Boyuan Yang","raw_affiliation_strings":["School of Electrical and Electronic Engineering, University of Manchester, Manchester, U.K","University of Manchester [Manchester] (Oxford Rd, Manchester M13 9PL - Royaume-Uni)"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Engineering, University of Manchester, Manchester, U.K","institution_ids":["https://openalex.org/I28407311"]},{"raw_affiliation_string":"University of Manchester [Manchester] (Oxford Rd, Manchester M13 9PL - Royaume-Uni)","institution_ids":["https://openalex.org/I28407311"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100728390","display_name":"Ruonan Liu","orcid":"https://orcid.org/0000-0001-9963-7092"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ruonan Liu","raw_affiliation_strings":["School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA","CMU - Carnegie Mellon University [Pittsburgh] (5000 Forbes Ave,  Pittsburgh, PA 15213 - \u00c9tats-Unis)"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]},{"raw_affiliation_string":"CMU - Carnegie Mellon University [Pittsburgh] (5000 Forbes Ave,  Pittsburgh, PA 15213 - \u00c9tats-Unis)","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012431211","display_name":"Enrico Zio","orcid":"https://orcid.org/0000-0002-7108-637X"},"institutions":[{"id":"https://openalex.org/I4403386560","display_name":"Centre de Recherche sur les Risques et les Crises","ror":"https://ror.org/05p7zpp88","country_code":"FR","type":"facility","lineage":["https://openalex.org/I190752583","https://openalex.org/I2746051580","https://openalex.org/I4403386560","https://openalex.org/I70768539"]},{"id":"https://openalex.org/I93860229","display_name":"Politecnico di Milano","ror":"https://ror.org/01nffqt88","country_code":"IT","type":"education","lineage":["https://openalex.org/I93860229"]}],"countries":["FR","IT"],"is_corresponding":false,"raw_author_name":"Enrico Zio","raw_affiliation_strings":["Department of Energy, Politecnico di Milano, Milano, Italy","CRC - Centre de recherche sur les Risques et les Crises (Rue Claude Daunesse CS 10207 06904 Sophia-Antipolis Cedex - France)"],"affiliations":[{"raw_affiliation_string":"Department of Energy, Politecnico di Milano, Milano, Italy","institution_ids":["https://openalex.org/I93860229"]},{"raw_affiliation_string":"CRC - Centre de recherche sur les Risques et les Crises (Rue Claude Daunesse CS 10207 06904 Sophia-Antipolis Cedex - France)","institution_ids":["https://openalex.org/I4403386560"]}]}],"institutions":[],"countries_distinct_count":4,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5009872730"],"corresponding_institution_ids":["https://openalex.org/I28407311"],"apc_list":null,"apc_paid":null,"fwci":31.8462,"has_fulltext":false,"cited_by_count":404,"citation_normalized_percentile":{"value":0.99907311,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"66","issue":"12","first_page":"9521","last_page":"9530"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9984999895095825,"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":0.9984999895095825,"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":0.993399977684021,"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/T12169","display_name":"Non-Destructive Testing Techniques","score":0.9909999966621399,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7410300970077515},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6895908117294312},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.6188708543777466},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5892233848571777},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5696483254432678},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.507047176361084},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4966924786567688},{"id":"https://openalex.org/keywords/extractor","display_name":"Extractor","score":0.48111265897750854},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4791273772716522},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.44437849521636963},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.42293331027030945},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3865257501602173},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.3011135458946228}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7410300970077515},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6895908117294312},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.6188708543777466},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5892233848571777},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5696483254432678},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.507047176361084},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4966924786567688},{"id":"https://openalex.org/C117978034","wikidata":"https://www.wikidata.org/wiki/Q5422192","display_name":"Extractor","level":2,"score":0.48111265897750854},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4791273772716522},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.44437849521636963},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.42293331027030945},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3865257501602173},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3011135458946228},{"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","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},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C21880701","wikidata":"https://www.wikidata.org/wiki/Q2144042","display_name":"Process engineering","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/tie.2019.2924605","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tie.2019.2924605","pdf_url":null,"source":{"id":"https://openalex.org/S58031724","display_name":"IEEE Transactions on Industrial Electronics","issn_l":"0278-0046","issn":["0278-0046","1557-9948"],"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 Industrial Electronics","raw_type":"journal-article"},{"id":"pmh:oai:HAL:hal-02432604v1","is_oa":false,"landing_page_url":"https://minesparis-psl.hal.science/hal-02432604","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Transactions on Industrial Electronics, 2019, 66 (12), pp.9521-9530. &#x27E8;10.1109/TIE.2019.2924605&#x27E9;","raw_type":"Journal articles"},{"id":"pmh:oai:re.public.polimi.it:11311/1122841","is_oa":false,"landing_page_url":"http://hdl.handle.net/11311/1122841","pdf_url":null,"source":{"id":"https://openalex.org/S4306400312","display_name":"Virtual Community of Pathological Anatomy (University of Castilla La Mancha)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I79189158","host_organization_name":"University of Castilla-La Mancha","host_organization_lineage":["https://openalex.org/I79189158"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/12","score":0.5199999809265137,"display_name":"Responsible consumption and production"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W2152564","https://openalex.org/W1507439363","https://openalex.org/W1522301498","https://openalex.org/W1556459174","https://openalex.org/W1578870273","https://openalex.org/W1983364832","https://openalex.org/W1988655998","https://openalex.org/W2005523062","https://openalex.org/W2022141820","https://openalex.org/W2033336377","https://openalex.org/W2042641006","https://openalex.org/W2069262928","https://openalex.org/W2112796928","https://openalex.org/W2114106396","https://openalex.org/W2133832971","https://openalex.org/W2142724780","https://openalex.org/W2163605009","https://openalex.org/W2170393096","https://openalex.org/W2294172420","https://openalex.org/W2323403147","https://openalex.org/W2463813940","https://openalex.org/W2487340486","https://openalex.org/W2495649841","https://openalex.org/W2544905596","https://openalex.org/W2562762876","https://openalex.org/W2583356199","https://openalex.org/W2591055632","https://openalex.org/W2593519057","https://openalex.org/W2594845301","https://openalex.org/W2603225712","https://openalex.org/W2618530766","https://openalex.org/W2765854388","https://openalex.org/W2768753204","https://openalex.org/W2791694051","https://openalex.org/W2794869810","https://openalex.org/W2919115771","https://openalex.org/W2964121744","https://openalex.org/W4392481888","https://openalex.org/W6630231556","https://openalex.org/W6631190155","https://openalex.org/W6633355566","https://openalex.org/W6861872020"],"related_works":["https://openalex.org/W1979583797","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W3167935049","https://openalex.org/W3029198973","https://openalex.org/W3141979996","https://openalex.org/W1996690921","https://openalex.org/W1941834444"],"abstract_inverted_index":{"Remaining":[0],"useful":[1,85],"life":[2],"(RUL)":[3],"prediction":[4,46,90,200,205],"has":[5],"been":[6],"increasingly":[7],"considered":[8],"in":[9,87,136,150],"many":[10],"industrial":[11],"fields":[12],"for":[13,37,117,188],"the":[14,55,62,84,101,111,157,173,176,195,211,216],"reliability":[15,145,168],"and":[16,105,202,207,213],"safety":[17],"of":[18,26,60,123,154,175,180,183,215],"their":[19],"systems.":[20],"As":[21],"a":[22,50,106,133,161],"data":[23,179],"analysis":[24],"tool":[25],"deep":[27,29],"learning,":[28],"convolutional":[30],"neural":[31],"network":[32],"(CNN)":[33],"shows":[34,198],"great":[35],"potential":[36],"RUL":[38,45,118,137,158,189],"prediction.":[39,119,138,190],"This":[40],"paper":[41],"proposes":[42],"an":[43,143],"intelligent":[44],"method":[47,64,197],"based":[48],"on":[49],"double-CNN":[51],"model":[52,104,114],"architecture.":[53],"Given":[54],"powerful":[56],"feature":[57,78],"extraction":[58],"capability":[59],"CNN,":[61],"proposed":[63,107,165,177,196,217],"is":[65,98,115,147,164],"fed":[66],"with":[67,71,192],"original":[68],"vibration":[69],"signals":[70],"no":[72],"need":[73],"to":[74,76,166,169],"resort":[75],"any":[77],"extractor,":[79],"which":[80,131],"can":[81],"also":[82],"retain":[83],"information":[86],"maximum.":[88],"The":[89,204],"includes":[91],"two":[92],"stages:":[93],"first,":[94],"incipient":[95],"fault":[96],"point":[97],"identified":[99],"by":[100],"first":[102,148],"CNN":[103,113],"\u201c3/5\u201d":[108],"principle;":[109],"then,":[110],"second":[112],"constructed":[116],"In":[120],"practice,":[121],"RULs":[122],"identical":[124],"components":[125],"are":[126,186],"different":[127],"from":[128],"each":[129],"other,":[130],"poses":[132],"major":[134],"challenge":[135],"To":[139,171],"overcome":[140],"this":[141,151],"problem,":[142],"intermediate":[144],"variable":[146],"calculated":[149],"paper,":[152],"instead":[153],"directly":[155],"predicting":[156],"value.":[159],"Then,":[160],"mapping":[162],"algorithm":[163],"map":[167],"RUL.":[170],"demonstrate":[172],"effectiveness":[174,212],"method,":[178],"four":[181],"tests":[182],"bearing":[184],"degradation":[185],"utilized":[187],"Compared":[191],"state-of-the-art":[193],"methods,":[194],"higher":[199],"accuracy":[201],"robustness.":[203],"results":[206],"evaluation":[208],"indexes":[209],"demonstrated":[210],"superiority":[214],"method.":[218]},"counts_by_year":[{"year":2026,"cited_by_count":14},{"year":2025,"cited_by_count":56},{"year":2024,"cited_by_count":75},{"year":2023,"cited_by_count":70},{"year":2022,"cited_by_count":72},{"year":2021,"cited_by_count":74},{"year":2020,"cited_by_count":38},{"year":2019,"cited_by_count":5}],"updated_date":"2026-04-01T17:29:45.350535","created_date":"2025-10-10T00:00:00"}
