{"id":"https://openalex.org/W3128426308","doi":"https://doi.org/10.1109/ickii50300.2020.9318912","title":"Explainable Artificial Intelligence for the Remaining Useful Life Prognosis of the Turbofan Engines","display_name":"Explainable Artificial Intelligence for the Remaining Useful Life Prognosis of the Turbofan Engines","publication_year":2020,"publication_date":"2020-08-21","ids":{"openalex":"https://openalex.org/W3128426308","doi":"https://doi.org/10.1109/ickii50300.2020.9318912","mag":"3128426308"},"language":"en","primary_location":{"id":"doi:10.1109/ickii50300.2020.9318912","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ickii50300.2020.9318912","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 3rd IEEE International Conference on Knowledge Innovation and Invention (ICKII)","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/A5101097617","display_name":"Chang Woo Hong","orcid":null},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Chang Woo Hong","raw_affiliation_strings":["School of Electrical & Electronic Engineering, Yonsei University,Seoul,Korea","School of Electrical & Electronic Engineering, Yonsei University, Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"School of Electrical & Electronic Engineering, Yonsei University,Seoul,Korea","institution_ids":["https://openalex.org/I193775966"]},{"raw_affiliation_string":"School of Electrical & Electronic Engineering, Yonsei University, Seoul, Korea","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100609703","display_name":"Changmin Lee","orcid":"https://orcid.org/0000-0002-9870-3956"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Changmin Lee","raw_affiliation_strings":["School of Electrical & Electronic Engineering, Yonsei University,Seoul,Korea","School of Electrical & Electronic Engineering, Yonsei University, Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"School of Electrical & Electronic Engineering, Yonsei University,Seoul,Korea","institution_ids":["https://openalex.org/I193775966"]},{"raw_affiliation_string":"School of Electrical & Electronic Engineering, Yonsei University, Seoul, Korea","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101519040","display_name":"Kwangsuk Lee","orcid":"https://orcid.org/0000-0003-4315-1258"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Kwangsuk Lee","raw_affiliation_strings":["School of Electrical & Electronic Engineering, Yonsei University,Seoul,Korea","School of Electrical & Electronic Engineering, Yonsei University, Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"School of Electrical & Electronic Engineering, Yonsei University,Seoul,Korea","institution_ids":["https://openalex.org/I193775966"]},{"raw_affiliation_string":"School of Electrical & Electronic Engineering, Yonsei University, Seoul, Korea","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022079685","display_name":"Min-Seung Ko","orcid":"https://orcid.org/0000-0003-0147-5676"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Min-Seung Ko","raw_affiliation_strings":["School of Electrical & Electronic Engineering, Yonsei University,Seoul,Korea","School of Electrical & Electronic Engineering, Yonsei University, Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"School of Electrical & Electronic Engineering, Yonsei University,Seoul,Korea","institution_ids":["https://openalex.org/I193775966"]},{"raw_affiliation_string":"School of Electrical & Electronic Engineering, Yonsei University, Seoul, Korea","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056817921","display_name":"Kyeon Hur","orcid":"https://orcid.org/0000-0003-3726-7545"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Kyeon Hur","raw_affiliation_strings":["School of Electrical & Electronic Engineering, Yonsei University,Seoul,Korea","School of Electrical & Electronic Engineering, Yonsei University, Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"School of Electrical & Electronic Engineering, Yonsei University,Seoul,Korea","institution_ids":["https://openalex.org/I193775966"]},{"raw_affiliation_string":"School of Electrical & Electronic Engineering, Yonsei University, Seoul, Korea","institution_ids":["https://openalex.org/I193775966"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101097617"],"corresponding_institution_ids":["https://openalex.org/I193775966"],"apc_list":null,"apc_paid":null,"fwci":2.0596,"has_fulltext":false,"cited_by_count":26,"citation_normalized_percentile":{"value":0.87240585,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"144","last_page":"147"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.991100013256073,"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":0.991100013256073,"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/T10944","display_name":"Turbomachinery Performance and Optimization","score":0.9650999903678894,"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/T10117","display_name":"Advanced Combustion Engine Technologies","score":0.9587000012397766,"subfield":{"id":"https://openalex.org/subfields/1507","display_name":"Fluid Flow and Transfer Processes"},"field":{"id":"https://openalex.org/fields/15","display_name":"Chemical Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/turbofan","display_name":"Turbofan","score":0.9848119020462036},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.7328413128852844},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6862068176269531},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.622666597366333},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.6014774441719055},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.559207558631897},{"id":"https://openalex.org/keywords/plot","display_name":"Plot (graphics)","score":0.47299203276634216},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4548453092575073},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37351536750793457},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.19566237926483154},{"id":"https://openalex.org/keywords/automotive-engineering","display_name":"Automotive engineering","score":0.13757500052452087},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.11718690395355225}],"concepts":[{"id":"https://openalex.org/C110050840","wikidata":"https://www.wikidata.org/wiki/Q654051","display_name":"Turbofan","level":2,"score":0.9848119020462036},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.7328413128852844},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6862068176269531},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.622666597366333},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.6014774441719055},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.559207558631897},{"id":"https://openalex.org/C167651023","wikidata":"https://www.wikidata.org/wiki/Q1474611","display_name":"Plot (graphics)","level":2,"score":0.47299203276634216},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4548453092575073},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37351536750793457},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.19566237926483154},{"id":"https://openalex.org/C171146098","wikidata":"https://www.wikidata.org/wiki/Q124192","display_name":"Automotive engineering","level":1,"score":0.13757500052452087},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.11718690395355225},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ickii50300.2020.9318912","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ickii50300.2020.9318912","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 3rd IEEE International Conference on Knowledge Innovation and Invention (ICKII)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.6800000071525574,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W188378287","https://openalex.org/W1585983533","https://openalex.org/W2064675550","https://openalex.org/W2112796928","https://openalex.org/W2131774270","https://openalex.org/W2772084711","https://openalex.org/W2902700103","https://openalex.org/W2910660149","https://openalex.org/W2962862931","https://openalex.org/W2974095898","https://openalex.org/W4393257588","https://openalex.org/W6737947904"],"related_works":["https://openalex.org/W4233568029","https://openalex.org/W2967774773","https://openalex.org/W3166075233","https://openalex.org/W2373556961","https://openalex.org/W2375127734","https://openalex.org/W4386141271","https://openalex.org/W2074007942","https://openalex.org/W837305724","https://openalex.org/W2380048437","https://openalex.org/W2370335777"],"abstract_inverted_index":{"This":[0,54],"paper":[1],"proposes":[2],"a":[3,40],"deep-stacked":[4],"neural":[5,43],"network":[6,44,62],"to":[7,63],"prognose":[8],"the":[9,14,28,33,65,73,105,110,114],"remaining":[10,29],"useful":[11,30],"life":[12,31],"of":[13,32,76,104,116],"turbofan":[15,34,106],"engines":[16,35],"and":[17,50,60,98],"analyze":[18,86],"results":[19,75],"using":[20],"explainable":[21],"artificial":[22,70],"intelligence.":[23],"The":[24,68,94],"proposed":[25],"model":[26,55,84],"prognoses":[27],"accurately":[36],"by":[37,112],"properly":[38,108],"stacking":[39],"one-dimensional":[41],"convolutional":[42],"(1D-CNN),":[45],"long":[46],"short-term":[47],"memory":[48],"(LSTM),":[49],"bidirectional":[51],"LSTM":[52],"algorithms.":[53],"also":[56],"uses":[57],"dropout":[58],"technique":[59],"residual":[61],"enhance":[64],"prediction":[66],"accuracy.":[67],"Explainable":[69],"intelligence":[71],"analyzes":[72],"prognostic":[74],"RUL.":[77],"Using":[78],"SHAP":[79,95],"(SHapely":[80],"Addictive":[81],"exPlanation),":[82],"this":[83],"can":[85,101],"features":[87],"that":[88],"have":[89],"significantly":[90],"influenced":[91],"RUL":[92],"prediction.":[93],"force":[96],"plot":[97,100],"decision":[99],"help":[102],"decision-makers":[103],"engine":[107],"manage":[109],"maintenance":[111],"showing":[113],"influence":[115],"sensors.":[117]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}