{"id":"https://openalex.org/W4409668557","doi":"https://doi.org/10.1186/s42162-025-00519-3","title":"Transformer fault diagnosis using machine learning: a method combining SHAP feature selection and intelligent optimization of LGBM","display_name":"Transformer fault diagnosis using machine learning: a method combining SHAP feature selection and intelligent optimization of LGBM","publication_year":2025,"publication_date":"2025-04-22","ids":{"openalex":"https://openalex.org/W4409668557","doi":"https://doi.org/10.1186/s42162-025-00519-3"},"language":"en","primary_location":{"id":"doi:10.1186/s42162-025-00519-3","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s42162-025-00519-3","pdf_url":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00519-3","source":{"id":"https://openalex.org/S3035173479","display_name":"Energy Informatics","issn_l":"2520-8942","issn":["2520-8942"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Energy Informatics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00519-3","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5063432412","display_name":"Cheng Liu","orcid":"https://orcid.org/0000-0003-3666-5605"},"institutions":[{"id":"https://openalex.org/I4210104733","display_name":"Jiangsu Vocational Institute of Commerce","ror":"https://ror.org/01aew1m62","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210104733"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cheng Liu","raw_affiliation_strings":["Department of Digital Business, Jiangsu Vocational Institute of Commerce, Nanjing, 211168, Jiangsu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Digital Business, Jiangsu Vocational Institute of Commerce, Nanjing, 211168, Jiangsu, China","institution_ids":["https://openalex.org/I4210104733"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100643761","display_name":"Weiming Yang","orcid":"https://orcid.org/0000-0001-7679-2559"},"institutions":[{"id":"https://openalex.org/I4210104733","display_name":"Jiangsu Vocational Institute of Commerce","ror":"https://ror.org/01aew1m62","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210104733"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiming Yang","raw_affiliation_strings":["Department of Digital Business, Jiangsu Vocational Institute of Commerce, Nanjing, 211168, Jiangsu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Digital Business, Jiangsu Vocational Institute of Commerce, Nanjing, 211168, Jiangsu, China","institution_ids":["https://openalex.org/I4210104733"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210104733"],"apc_list":{"value":1340,"currency":"USD","value_usd":1340},"apc_paid":{"value":1340,"currency":"USD","value_usd":1340},"fwci":5.4008,"has_fulltext":true,"cited_by_count":17,"citation_normalized_percentile":{"value":0.96525934,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"8","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11343","display_name":"Power Transformer Diagnostics and Insulation","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T11343","display_name":"Power Transformer Diagnostics and Insulation","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9962000250816345,"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/T12169","display_name":"Non-Destructive Testing Techniques","score":0.9835000038146973,"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/feature-selection","display_name":"Feature selection","score":0.7301529049873352},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.6201856732368469},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5196277499198914},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.48765385150909424},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44389843940734863},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4365561306476593},{"id":"https://openalex.org/keywords/reliability-engineering","display_name":"Reliability engineering","score":0.35219329595565796},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3254152536392212},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.3103668689727783},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.2209269106388092},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.10192441940307617}],"concepts":[{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.7301529049873352},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.6201856732368469},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5196277499198914},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.48765385150909424},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44389843940734863},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4365561306476593},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.35219329595565796},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3254152536392212},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3103668689727783},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.2209269106388092},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.10192441940307617}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1186/s42162-025-00519-3","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s42162-025-00519-3","pdf_url":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00519-3","source":{"id":"https://openalex.org/S3035173479","display_name":"Energy Informatics","issn_l":"2520-8942","issn":["2520-8942"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Energy Informatics","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:35e4561ca1f04d2f91e9f94343f1bcc7","is_oa":true,"landing_page_url":"https://doaj.org/article/35e4561ca1f04d2f91e9f94343f1bcc7","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Energy Informatics, Vol 8, Iss 1, Pp 1-19 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1186/s42162-025-00519-3","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s42162-025-00519-3","pdf_url":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00519-3","source":{"id":"https://openalex.org/S3035173479","display_name":"Energy Informatics","issn_l":"2520-8942","issn":["2520-8942"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Energy Informatics","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321605","display_name":"Government of Jiangsu Province","ror":"https://ror.org/004svx814"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4409668557.pdf","grobid_xml":"https://content.openalex.org/works/W4409668557.grobid-xml"},"referenced_works_count":30,"referenced_works":["https://openalex.org/W146900863","https://openalex.org/W1595159159","https://openalex.org/W2110390205","https://openalex.org/W2154053567","https://openalex.org/W2164557413","https://openalex.org/W2167101736","https://openalex.org/W2954876896","https://openalex.org/W2962862931","https://openalex.org/W2998506103","https://openalex.org/W3139484821","https://openalex.org/W3173455696","https://openalex.org/W3190956449","https://openalex.org/W3205119553","https://openalex.org/W3210666031","https://openalex.org/W3212690266","https://openalex.org/W4313304634","https://openalex.org/W4318483023","https://openalex.org/W4318833282","https://openalex.org/W4320532256","https://openalex.org/W4366833182","https://openalex.org/W4382460738","https://openalex.org/W4387713177","https://openalex.org/W4387735354","https://openalex.org/W4393190681","https://openalex.org/W4396505859","https://openalex.org/W4401226089","https://openalex.org/W4402991628","https://openalex.org/W4403562721","https://openalex.org/W4405374791","https://openalex.org/W4406778695"],"related_works":["https://openalex.org/W4205762803","https://openalex.org/W2535856026","https://openalex.org/W2265065644","https://openalex.org/W2134699697","https://openalex.org/W3017188156","https://openalex.org/W2322875716","https://openalex.org/W2961085424","https://openalex.org/W2383516975","https://openalex.org/W2374878784","https://openalex.org/W2147679489"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"a":[3,11,45],"novel":[4],"approach":[5,139],"for":[6],"transformer":[7,141],"fault":[8,142],"diagnosis.":[9,143],"Initially,":[10],"high-dimensional":[12],"feature":[13,41],"set":[14],"comprising":[15],"19":[16],"features":[17],"related":[18],"to":[19,25,39,72],"five":[20],"gas":[21],"concentrations":[22],"is":[23,37,70],"constructed":[24],"reflect":[26],"the":[27,31,54,62,74,77,98,101,108,120,134],"gas-fault":[28],"relationship.":[29],"Subsequently,":[30],"Shapley":[32],"Additive":[33],"Explanations":[34],"(SHAP)":[35],"method":[36],"employed":[38],"evaluate":[40],"importance":[42],"and":[43,56,113,132],"select":[44],"subset":[46],"that":[47],"significantly":[48],"influences":[49],"model":[50,55,106],"predictions,":[51],"thereby":[52],"simplifying":[53],"enhancing":[57],"its":[58,86],"interpretability.":[59],"Following":[60],"this,":[61],"bald":[63],"eagle":[64],"search":[65],"(BES)":[66],"intelligent":[67],"optimization":[68],"algorithm":[69],"utilized":[71],"optimize":[73],"hyperparameters":[75],"of":[76,100,111,117,136],"light":[78],"gradient":[79],"boosting":[80],"machine":[81,94],"(LGBM)":[82],"model,":[83],"further":[84],"improving":[85],"predictive":[87],"capability.":[88],"Comparative":[89],"experiments":[90],"with":[91,123],"various":[92],"traditional":[93],"learning":[95],"models":[96],"validate":[97],"effectiveness":[99],"proposed":[102],"method.":[103],"The":[104],"SHAP-BES-LGBM":[105],"achieves":[107],"highest":[109],"accuracy":[110],"0.9509":[112],"an":[114],"f1":[115],"score":[116],"0.9606":[118],"on":[119],"test":[121],"set,":[122],"only":[124],"11":[125],"samples":[126],"misclassified,":[127],"demonstrating":[128],"superior":[129],"classification":[130],"performance":[131],"underscoring":[133],"advantages":[135],"this":[137],"integrated":[138],"in":[140]},"counts_by_year":[{"year":2026,"cited_by_count":10},{"year":2025,"cited_by_count":7}],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2025-10-10T00:00:00"}