{"id":"https://openalex.org/W7140237769","doi":"https://doi.org/10.1016/j.neucom.2026.133352","title":"Hybrid graph neural network with FFT and genetic optimization for fault detection in wind turbines","display_name":"Hybrid graph neural network with FFT and genetic optimization for fault detection in wind turbines","publication_year":2026,"publication_date":"2026-03-24","ids":{"openalex":"https://openalex.org/W7140237769","doi":"https://doi.org/10.1016/j.neucom.2026.133352"},"language":"en","primary_location":{"id":"doi:10.1016/j.neucom.2026.133352","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.neucom.2026.133352","pdf_url":null,"source":{"id":"https://openalex.org/S45693802","display_name":"Neurocomputing","issn_l":"0925-2312","issn":["0925-2312","1872-8286"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neurocomputing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1016/j.neucom.2026.133352","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Dennys Coronel","orcid":null},"institutions":[{"id":"https://openalex.org/I121748325","display_name":"Universidad Complutense de Madrid","ror":"https://ror.org/02p0gd045","country_code":"ES","type":"education","lineage":["https://openalex.org/I121748325"]},{"id":"https://openalex.org/I39398033","display_name":"Pontificia Universidad Cat\u00f3lica del Ecuador","ror":"https://ror.org/02qztda51","country_code":"EC","type":"education","lineage":["https://openalex.org/I39398033"]}],"countries":["EC","ES"],"is_corresponding":false,"raw_author_name":"Dennys Coronel","raw_affiliation_strings":["Complutense University of Madrid, Computer Science Faculty, C. del Prof. Jos\u00e9 Garc\u00eda Santesmases, 9, 28040, Madrid, Spain","Pontificia Universidad Cat\u00f3lica del Ecuador, H\u00e1bitat, Infraestructura y Creatividad, Av. Manuelita S\u00e1enz., 180207, Ambato, Ecuador"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Complutense University of Madrid, Computer Science Faculty, C. del Prof. Jos\u00e9 Garc\u00eda Santesmases, 9, 28040, Madrid, Spain","institution_ids":["https://openalex.org/I121748325"]},{"raw_affiliation_string":"Pontificia Universidad Cat\u00f3lica del Ecuador, H\u00e1bitat, Infraestructura y Creatividad, Av. Manuelita S\u00e1enz., 180207, Ambato, Ecuador","institution_ids":["https://openalex.org/I39398033"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Cesar Guevara","orcid":"https://orcid.org/0000-0003-1571-5829"},"institutions":[{"id":"https://openalex.org/I4210087408","display_name":"Instituto de Tecnolog\u00edas F\u00edsicas y de la Informaci\u00f3n \u201cLeonardo Torres Quevedo\u201d","ror":"https://ror.org/00zsy6110","country_code":"ES","type":"facility","lineage":["https://openalex.org/I134820265","https://openalex.org/I4210087408"]}],"countries":["ES"],"is_corresponding":true,"raw_author_name":"Cesar Guevara","raw_affiliation_strings":["CUNEF Universidad, Quantitative Methods Department, C. de Leonardo Prieto Castro, 2, 28040, Madrid, Spain"],"raw_orcid":"https://orcid.org/0000-0003-1571-5829","affiliations":[{"raw_affiliation_string":"CUNEF Universidad, Quantitative Methods Department, C. de Leonardo Prieto Castro, 2, 28040, Madrid, Spain","institution_ids":["https://openalex.org/I4210087408"]}]},{"author_position":"last","author":{"id":null,"display_name":"Matilde Santos","orcid":"https://orcid.org/0000-0003-1993-8368"},"institutions":[{"id":"https://openalex.org/I121748325","display_name":"Universidad Complutense de Madrid","ror":"https://ror.org/02p0gd045","country_code":"ES","type":"education","lineage":["https://openalex.org/I121748325"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Matilde Santos","raw_affiliation_strings":["Complutense University of Madrid, Institute of Knowledge Technology, C. del Prof. Jos\u00e9 Garc\u00eda Santesmases, 9, 28040, Madrid, Spain"],"raw_orcid":"https://orcid.org/0000-0003-1993-8368","affiliations":[{"raw_affiliation_string":"Complutense University of Madrid, Institute of Knowledge Technology, C. del Prof. Jos\u00e9 Garc\u00eda Santesmases, 9, 28040, Madrid, Spain","institution_ids":["https://openalex.org/I121748325"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210087408"],"apc_list":{"value":2470,"currency":"USD","value_usd":2470},"apc_paid":{"value":2470,"currency":"USD","value_usd":2470},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.39013697,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"684","issue":null,"first_page":"133352","last_page":"133352"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9951000213623047,"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":0.9951000213623047,"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/T11062","display_name":"Gear and Bearing Dynamics Analysis","score":0.0010000000474974513,"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"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.0003000000142492354,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/fast-fourier-transform","display_name":"Fast Fourier transform","score":0.5741000175476074},{"id":"https://openalex.org/keywords/test-bench","display_name":"Test bench","score":0.5662000179290771},{"id":"https://openalex.org/keywords/genetic-algorithm","display_name":"Genetic algorithm","score":0.5611000061035156},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.5461000204086304},{"id":"https://openalex.org/keywords/fault-detection-and-isolation","display_name":"Fault detection and isolation","score":0.5188000202178955},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.4666000008583069},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4489000141620636},{"id":"https://openalex.org/keywords/extreme-learning-machine","display_name":"Extreme learning machine","score":0.4307999908924103},{"id":"https://openalex.org/keywords/turbine","display_name":"Turbine","score":0.42399999499320984},{"id":"https://openalex.org/keywords/wind-power","display_name":"Wind power","score":0.4122999906539917}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7590000033378601},{"id":"https://openalex.org/C75172450","wikidata":"https://www.wikidata.org/wiki/Q623950","display_name":"Fast Fourier transform","level":2,"score":0.5741000175476074},{"id":"https://openalex.org/C2776266606","wikidata":"https://www.wikidata.org/wiki/Q476482","display_name":"Test bench","level":2,"score":0.5662000179290771},{"id":"https://openalex.org/C8880873","wikidata":"https://www.wikidata.org/wiki/Q187787","display_name":"Genetic algorithm","level":2,"score":0.5611000061035156},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.5461000204086304},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.5188000202178955},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.4666000008583069},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4489000141620636},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4339999854564667},{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.4307999908924103},{"id":"https://openalex.org/C2778449969","wikidata":"https://www.wikidata.org/wiki/Q130760","display_name":"Turbine","level":2,"score":0.42399999499320984},{"id":"https://openalex.org/C78600449","wikidata":"https://www.wikidata.org/wiki/Q43302","display_name":"Wind power","level":2,"score":0.4122999906539917},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40290001034736633},{"id":"https://openalex.org/C2775846686","wikidata":"https://www.wikidata.org/wiki/Q643012","display_name":"Condition monitoring","level":2,"score":0.398499995470047},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.39419999718666077},{"id":"https://openalex.org/C89805583","wikidata":"https://www.wikidata.org/wiki/Q192940","display_name":"Accelerometer","level":2,"score":0.36340001225471497},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.35690000653266907},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3287999927997589},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.326200008392334},{"id":"https://openalex.org/C2780365114","wikidata":"https://www.wikidata.org/wiki/Q169478","display_name":"MATLAB","level":2,"score":0.32440000772476196},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.32429999113082886},{"id":"https://openalex.org/C10485038","wikidata":"https://www.wikidata.org/wiki/Q48996162","display_name":"Hyperparameter optimization","level":3,"score":0.31279999017715454},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.3125999867916107},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.2994000017642975},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.29660001397132874},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2840000092983246},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.2808000147342682},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.2802000045776367},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.26969999074935913},{"id":"https://openalex.org/C198394728","wikidata":"https://www.wikidata.org/wiki/Q3695508","display_name":"Vibration","level":2,"score":0.26829999685287476},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.2540000081062317}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1016/j.neucom.2026.133352","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.neucom.2026.133352","pdf_url":null,"source":{"id":"https://openalex.org/S45693802","display_name":"Neurocomputing","issn_l":"0925-2312","issn":["0925-2312","1872-8286"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neurocomputing","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1016/j.neucom.2026.133352","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.neucom.2026.133352","pdf_url":null,"source":{"id":"https://openalex.org/S45693802","display_name":"Neurocomputing","issn_l":"0925-2312","issn":["0925-2312","1872-8286"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neurocomputing","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.8715128898620605}],"awards":[{"id":"https://openalex.org/G2702539199","display_name":null,"funder_award_id":"PID2024-1556530B-C21","funder_id":"https://openalex.org/F4320321595","funder_display_name":"Federaci\u00f3n Espa\u00f1ola de Enfermedades Raras"},{"id":"https://openalex.org/G8047616637","display_name":null,"funder_award_id":"PID2021-123543OB-C21","funder_id":"https://openalex.org/F4320315062","funder_display_name":"Ministerio de Ciencia, Innovaci\u00f3n y Universidades"}],"funders":[{"id":"https://openalex.org/F4320315062","display_name":"Ministerio de Ciencia, Innovaci\u00f3n y Universidades","ror":null},{"id":"https://openalex.org/F4320321595","display_name":"Federaci\u00f3n Espa\u00f1ola de Enfermedades Raras","ror":"https://ror.org/0348bpk17"},{"id":"https://openalex.org/F4320335598","display_name":"Agencia Estatal de Investigaci\u00f3n","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W2343835906","https://openalex.org/W2973001054","https://openalex.org/W3039958185","https://openalex.org/W3110251189","https://openalex.org/W3121022549","https://openalex.org/W3127403190","https://openalex.org/W3134325399","https://openalex.org/W3176843380","https://openalex.org/W3202494336","https://openalex.org/W4200179031","https://openalex.org/W4211172221","https://openalex.org/W4212866171","https://openalex.org/W4221111410","https://openalex.org/W4229001178","https://openalex.org/W4309332682","https://openalex.org/W4312702896","https://openalex.org/W4317618818","https://openalex.org/W4320897356","https://openalex.org/W4362663176","https://openalex.org/W4392370087","https://openalex.org/W4393346496","https://openalex.org/W4396533243","https://openalex.org/W4401009106","https://openalex.org/W4404473025","https://openalex.org/W4405747157","https://openalex.org/W4412835088","https://openalex.org/W4415300307"],"related_works":[],"abstract_inverted_index":{"The":[0],"rapid":[1],"expansion":[2],"of":[3,50,129,132,136,167],"wind":[4],"energy":[5],"has":[6],"increased":[7],"the":[8,76,85,110,120,140,161,165,168],"need":[9],"for":[10,68,172],"reliable,":[11],"data-driven":[12],"condition":[13,142],"monitoring":[14],"to":[15,41],"reduce":[16],"operation":[17],"and":[18,21,63,116,134,145,154],"maintenance":[19],"costs":[20],"improve":[22],"turbine":[23],"availability.":[24],"This":[25],"paper":[26],"presents":[27],"an":[28],"anomaly":[29,176],"detection":[30],"framework":[31],"that":[32],"combines":[33],"(i)":[34],"Fast":[35],"Fourier":[36],"Transform":[37],"(FFT)":[38],"feature":[39],"extraction":[40],"capture":[42],"discriminative":[43],"frequency-domain":[44],"signatures,":[45],"(ii)":[46],"a":[47,55,65,101,105,126,150],"graph-based":[48],"formulation":[49],"multi-sensor":[51],"dependencies":[52],"learned":[53],"with":[54,104,143],"hybrid":[56],"Graph":[57],"Neural":[58],"Network":[59],"(GraphSAGE":[60],"+":[61],"GATConv),":[62],"(iii)":[64],"genetic":[66],"algorithm":[67],"automated":[69],"hyperparameter":[70],"optimization.":[71],"Experiments":[72],"are":[73],"conducted":[74],"on":[75,160],"publicly":[77],"available":[78],"Gearbox":[79],"Reliability":[80],"Collaborative":[81],"(GRC)":[82],"dataset":[83],"from":[84,95],"National":[86],"Renewable":[87],"Energy":[88],"Laboratory":[89],"(NREL),":[90],"using":[91],"gearbox":[92,175],"vibration":[93],"signals":[94],"five":[96],"selected":[97],"accelerometer":[98],"channels.":[99],"Under":[100],"window-level":[102],"protocol":[103],"class-balanced":[106],"70/30":[107],"train\u2013test":[108],"split,":[109],"proposed":[111,169],"model":[112],"achieves":[113],"training":[114],"accuracy":[115],"test":[117,122],"accuracy.":[118],"On":[119],"held-out":[121],"set,":[123],"it":[124],"attains":[125],"macro-averaged":[127],"precision":[128,144],",":[130,133,137],"recall":[131],"F1-score":[135],"while":[138],"detecting":[139],"Damaged":[141],"recall.":[146],"These":[147],"results":[148],"indicate":[149],"small":[151],"generalization":[152],"gap":[153],"outperform":[155],"previously":[156],"reported":[157],"AI-based":[158],"methods":[159],"same":[162],"dataset,":[163],"supporting":[164],"effectiveness":[166],"GNN-driven":[170],"approach":[171],"robust":[173],"wind-turbine":[174],"detection.":[177]},"counts_by_year":[],"updated_date":"2026-06-14T06:11:07.267592","created_date":"2026-03-25T00:00:00"}