{"id":"https://openalex.org/W4385477968","doi":"https://doi.org/10.1109/icphm57936.2023.10193965","title":"Convolutional Neural Networks for Gas Turbine Exhaust Gas Temperature and Power Predictions","display_name":"Convolutional Neural Networks for Gas Turbine Exhaust Gas Temperature and Power Predictions","publication_year":2023,"publication_date":"2023-06-05","ids":{"openalex":"https://openalex.org/W4385477968","doi":"https://doi.org/10.1109/icphm57936.2023.10193965"},"language":"en","primary_location":{"id":"doi:10.1109/icphm57936.2023.10193965","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icphm57936.2023.10193965","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 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/A5071196859","display_name":"T. Ravichandran","orcid":null},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Thambirajah Ravichandran","raw_affiliation_strings":["University of Waterloo,Waterloo,ON,Canada","University of Waterloo, Waterloo, ON, Canada"],"affiliations":[{"raw_affiliation_string":"University of Waterloo,Waterloo,ON,Canada","institution_ids":["https://openalex.org/I151746483"]},{"raw_affiliation_string":"University of Waterloo, Waterloo, ON, Canada","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108736758","display_name":"Yuan Liu","orcid":"https://orcid.org/0009-0009-7270-2929"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuan Liu","raw_affiliation_strings":["Tecsis Corporation,Ottawa,ON,Canada","Tecsis Corporation, Ottawa, ON, Canada"],"affiliations":[{"raw_affiliation_string":"Tecsis Corporation,Ottawa,ON,Canada","institution_ids":[]},{"raw_affiliation_string":"Tecsis Corporation, Ottawa, ON, Canada","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101682408","display_name":"Amar Kumar","orcid":"https://orcid.org/0000-0003-0382-9095"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Amar Kumar","raw_affiliation_strings":["Tecsis Corporation,Ottawa,ON,Canada","Tecsis Corporation, Ottawa, ON, Canada"],"affiliations":[{"raw_affiliation_string":"Tecsis Corporation,Ottawa,ON,Canada","institution_ids":[]},{"raw_affiliation_string":"Tecsis Corporation, Ottawa, ON, Canada","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102772748","display_name":"Alka Srivastava","orcid":"https://orcid.org/0000-0002-0997-3576"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alka Srivastava","raw_affiliation_strings":["Tecsis Corporation,Ottawa,ON,Canada","Tecsis Corporation, Ottawa, ON, Canada"],"affiliations":[{"raw_affiliation_string":"Tecsis Corporation,Ottawa,ON,Canada","institution_ids":[]},{"raw_affiliation_string":"Tecsis Corporation, Ottawa, ON, Canada","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5071196859"],"corresponding_institution_ids":["https://openalex.org/I151746483"],"apc_list":null,"apc_paid":null,"fwci":0.6488,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.62122572,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"119","last_page":"127"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12304","display_name":"Radiative Heat Transfer Studies","score":0.993399977684021,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T12304","display_name":"Radiative Heat Transfer Studies","score":0.993399977684021,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T13928","display_name":"Advanced Sensor Technologies Research","score":0.9872000217437744,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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":0.9764000177383423,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7574546337127686},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6583001613616943},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5149562954902649},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.5145920515060425},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4887514114379883},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.459073543548584},{"id":"https://openalex.org/keywords/gas-turbines","display_name":"Gas turbines","score":0.45127809047698975},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.4340890049934387},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.43207743763923645},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3764362037181854},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.17162570357322693}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7574546337127686},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6583001613616943},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5149562954902649},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.5145920515060425},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4887514114379883},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.459073543548584},{"id":"https://openalex.org/C2987684023","wikidata":"https://www.wikidata.org/wiki/Q193470","display_name":"Gas turbines","level":2,"score":0.45127809047698975},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.4340890049934387},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.43207743763923645},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3764362037181854},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.17162570357322693},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"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/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icphm57936.2023.10193965","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icphm57936.2023.10193965","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Prognostics and Health Management (ICPHM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.8500000238418579}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W2038445170","https://openalex.org/W2042274746","https://openalex.org/W2064482408","https://openalex.org/W2075118551","https://openalex.org/W2137543489","https://openalex.org/W2782812883","https://openalex.org/W2797862164","https://openalex.org/W2825800704","https://openalex.org/W2885839277","https://openalex.org/W2964962196","https://openalex.org/W2996752936","https://openalex.org/W3010180770","https://openalex.org/W3098100992","https://openalex.org/W3098819468","https://openalex.org/W3108673000","https://openalex.org/W3165390445","https://openalex.org/W3172264170","https://openalex.org/W4200400443","https://openalex.org/W4300819821"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W2611989081","https://openalex.org/W4321487865","https://openalex.org/W4313906399","https://openalex.org/W4239306820","https://openalex.org/W4391266461","https://openalex.org/W4226493464","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983"],"abstract_inverted_index":{"In":[0],"this":[1,154],"work,":[2],"a":[3,116,140],"data-driven":[4],"and":[5,51,81,98,107,137,170],"deep":[6],"learning-based":[7],"predictive":[8,83,92,132],"modeling":[9,93],"framework":[10,94],"has":[11,102],"been":[12,103],"developed":[13],"for":[14,20,71,105,134,145,164,168],"generating":[15],"accurate":[16],"prediction":[17,109],"models":[18,123,167],"intended":[19],"gas":[21,48],"turbine":[22],"engine":[23],"performance":[24],"analysis.":[25],"This":[26],"paper":[27],"focuses":[28],"on":[29],"the":[30,90,99,149,156],"application":[31],"of":[32,54,89,118,151],"Convolutional":[33],"Neural":[34],"Networks":[35],"(CNNs)":[36],"along":[37],"with":[38,139],"tabular":[39,62,128],"data":[40,63,113,129],"to":[41,45,64,159],"image":[42,65],"conversion":[43,66],"techniques":[44],"predict":[46],"exhaust":[47],"temperature":[49],"(EGT)":[50],"power":[52,108,171],"outputs":[53],"Gas":[55],"Turbine":[56],"Engines":[57],"(GTE).":[58],"Using":[59],"one":[60],"such":[61],"method":[67],"called":[68],"Image":[69],"Generator":[70],"Tabular":[72],"Data":[73],"(IGTD),":[74],"several":[75],"CNN":[76,122],"model":[77],"architectures":[78],"were":[79,85],"explored,":[80],"their":[82],"capabilities":[84,133],"compared.":[86],"The":[87,121],"effectiveness":[88],"proposed":[91],"which":[95],"combines":[96],"CNNs":[97,163],"IGTD":[100,161],"algorithm":[101],"demonstrated":[104],"EGT":[106,136,146,169],"using":[110,124],"GTE":[111,166],"operational":[112],"collected":[114],"over":[115],"period":[117],"three":[119],"years.":[120],"images":[125],"converted":[126],"from":[127],"exhibit":[130],"superior":[131],"both":[135],"power,":[138],"more":[141],"significant":[142],"improvement":[143],"observed":[144],"prediction.":[147,172],"To":[148],"best":[150],"our":[152],"knowledge,":[153],"is":[155],"first":[157],"attempt":[158],"apply":[160],"based":[162],"developing":[165]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}