{"id":"https://openalex.org/W4410853144","doi":"https://doi.org/10.1109/tim.2025.3574908","title":"M<sup>2</sup>-STGN: A Missing Data-Tolerant Learning Model for Condition Monitoring of Wind Turbines","display_name":"M<sup>2</sup>-STGN: A Missing Data-Tolerant Learning Model for Condition Monitoring of Wind Turbines","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4410853144","doi":"https://doi.org/10.1109/tim.2025.3574908"},"language":"en","primary_location":{"id":"doi:10.1109/tim.2025.3574908","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2025.3574908","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"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 Instrumentation and Measurement","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/A5100844783","display_name":"Jian Xu","orcid":"https://orcid.org/0000-0002-4269-1319"},"institutions":[{"id":"https://openalex.org/I153473198","display_name":"North China Electric Power University","ror":"https://ror.org/04qr5t414","country_code":"CN","type":"education","lineage":["https://openalex.org/I153473198"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian Xu","raw_affiliation_strings":["School of Control and Computer Engineering, North China Electric Power University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-4269-1319","affiliations":[{"raw_affiliation_string":"School of Control and Computer Engineering, North China Electric Power University, Beijing, China","institution_ids":["https://openalex.org/I153473198"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102732976","display_name":"Yuguang Niu","orcid":"https://orcid.org/0000-0002-0124-8548"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuguang Niu","raw_affiliation_strings":["Beijing Huairou Laboratory, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-0124-8548","affiliations":[{"raw_affiliation_string":"Beijing Huairou Laboratory, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100694110","display_name":"Ming Du","orcid":"https://orcid.org/0000-0002-5570-1846"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ming Du","raw_affiliation_strings":["Beijing Huairou Laboratory, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-5570-1846","affiliations":[{"raw_affiliation_string":"Beijing Huairou Laboratory, Beijing, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059030906","display_name":"Yao Jun","orcid":null},"institutions":[{"id":"https://openalex.org/I153473198","display_name":"North China Electric Power University","ror":"https://ror.org/04qr5t414","country_code":"CN","type":"education","lineage":["https://openalex.org/I153473198"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Yao","raw_affiliation_strings":["School of Control and Computer Engineering, North China Electric Power University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0007-8448-2148","affiliations":[{"raw_affiliation_string":"School of Control and Computer Engineering, North China Electric Power University, Beijing, China","institution_ids":["https://openalex.org/I153473198"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.9042,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.7375559,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"74","issue":null,"first_page":"1","last_page":"16"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.998199999332428,"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":0.998199999332428,"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/T11372","display_name":"Hydraulic and Pneumatic Systems","score":0.9819999933242798,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9807000160217285,"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/wind-power","display_name":"Wind power","score":0.6573083996772766},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.5366876125335693},{"id":"https://openalex.org/keywords/condition-monitoring","display_name":"Condition monitoring","score":0.4751558005809784},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.45000672340393066},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3921060860157013},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.33083194494247437},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.32533276081085205},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.324127197265625},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.32015877962112427},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.2727295756340027},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.17810803651809692},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.1288871467113495}],"concepts":[{"id":"https://openalex.org/C78600449","wikidata":"https://www.wikidata.org/wiki/Q43302","display_name":"Wind power","level":2,"score":0.6573083996772766},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.5366876125335693},{"id":"https://openalex.org/C2775846686","wikidata":"https://www.wikidata.org/wiki/Q643012","display_name":"Condition monitoring","level":2,"score":0.4751558005809784},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.45000672340393066},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3921060860157013},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.33083194494247437},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.32533276081085205},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.324127197265625},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.32015877962112427},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.2727295756340027},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.17810803651809692},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.1288871467113495},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tim.2025.3574908","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2025.3574908","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"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 Instrumentation and Measurement","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.8700000047683716}],"awards":[{"id":"https://openalex.org/G8111628943","display_name":null,"funder_award_id":"52206009","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W1781481323","https://openalex.org/W2521465978","https://openalex.org/W2788592841","https://openalex.org/W3082388813","https://openalex.org/W3113272947","https://openalex.org/W3128016615","https://openalex.org/W3129225410","https://openalex.org/W3138430000","https://openalex.org/W3140699518","https://openalex.org/W3158668220","https://openalex.org/W3159013107","https://openalex.org/W3159913937","https://openalex.org/W3185649576","https://openalex.org/W3201693442","https://openalex.org/W3202494336","https://openalex.org/W4200468119","https://openalex.org/W4205323810","https://openalex.org/W4206652641","https://openalex.org/W4206713150","https://openalex.org/W4285115901","https://openalex.org/W4285215557","https://openalex.org/W4285306189","https://openalex.org/W4297494228","https://openalex.org/W4310664802","https://openalex.org/W4311807066","https://openalex.org/W4321781319","https://openalex.org/W4362690417","https://openalex.org/W4379984756","https://openalex.org/W4386276192","https://openalex.org/W4387966578","https://openalex.org/W4390604456","https://openalex.org/W4391228727","https://openalex.org/W4394744485","https://openalex.org/W4399486975","https://openalex.org/W4401568016","https://openalex.org/W4401990972","https://openalex.org/W4402125024","https://openalex.org/W4402436767","https://openalex.org/W4402934243","https://openalex.org/W4403674689","https://openalex.org/W4403904881","https://openalex.org/W6749825310"],"related_works":["https://openalex.org/W4380150146","https://openalex.org/W3024870410","https://openalex.org/W2410652950","https://openalex.org/W4283773154","https://openalex.org/W3139174110","https://openalex.org/W4289597203","https://openalex.org/W2085630472","https://openalex.org/W2799162308","https://openalex.org/W2741863531","https://openalex.org/W1969365005"],"abstract_inverted_index":{"The":[0],"field":[1],"data":[2,7,48,99],"of":[3,18,26,35,65,84,98,103,176],"supervisory":[4],"control":[5],"and":[6,21,33,68,100,142,146,190],"acquisition":[8],"(SCADA)":[9],"systems":[10],"are":[11,87,183],"commonly":[12],"missing,":[13],"which":[14],"increases":[15],"the":[16,23,31,43,96,101,136,161,174,187],"difficulty":[17],"feature":[19,50,71],"extraction":[20],"reduces":[22],"effective":[24],"utilization":[25],"data.":[27,134],"This":[28],"negatively":[29],"affects":[30],"reliability":[32],"integrality":[34],"wind":[36],"turbine":[37],"condition":[38],"monitoring":[39],"(WTCM).":[40],"To":[41],"address":[42,156],"interference":[44],"caused":[45,159],"by":[46,160],"missing":[47,144],"during":[49],"extraction,":[51],"this":[52],"paper":[53],"proposes":[54],"a":[55,106,165],"multiscale":[56,66,90],"masked":[57,69,108,126],"spatial-temporal":[58,149],"graph":[59,75],"networks":[60,77,124],"(M2-STGN)":[61],"model,":[62],"mainly":[63],"composed":[64],"spatial":[67,82],"temporal":[70,104,107,122,127],"learning":[72],"modules.":[73],"Employing":[74],"attention":[76,109,117],"(GAT)":[78],"with":[79,121],"different":[80],"heads,":[81],"features":[83,128,150],"diverse":[85],"granularity":[86],"extracted":[88,131],"from":[89,132],"in":[91],"parallel.":[92],"Then,":[93],"based":[94],"on":[95],"absence":[97],"degree":[102],"correlation,":[105],"module":[110],"is":[111,170],"specially":[112],"designed":[113],"to":[114,140,155,172,185],"calculate":[115],"differentiated":[116],"scores.":[118],"By":[119],"collaborating":[120],"convolutional":[123],"(TCN),":[125],"can":[129],"be":[130],"non-missing":[133],"Therefore,":[135],"proposed":[137,171,188],"model":[138,189],"adapts":[139],"complex":[141],"changeable":[143],"proportions":[145],"provides":[147],"robust":[148],"for":[151],"WTCM":[152,163],"tasks.":[153],"Furthermore,":[154],"missed":[157],"alarms":[158],"discontinuous":[162],"process,":[164],"fault":[166,177],"soft":[167],"judgment":[168],"strategy":[169],"enhance":[173],"sensitivity":[175],"detection.":[178],"Two":[179],"real-world":[180],"SCADA":[181],"datasets":[182],"utilized":[184],"validate":[186],"strategy.":[191]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}