{"id":"https://openalex.org/W2970575923","doi":"https://doi.org/10.1145/3352411.3352434","title":"Transformer Fault Diagnosis Model Based on Iterative Nearest Neighbor Interpolation and Ensemble Learning","display_name":"Transformer Fault Diagnosis Model Based on Iterative Nearest Neighbor Interpolation and Ensemble Learning","publication_year":2019,"publication_date":"2019-07-19","ids":{"openalex":"https://openalex.org/W2970575923","doi":"https://doi.org/10.1145/3352411.3352434","mag":"2970575923"},"language":"en","primary_location":{"id":"doi:10.1145/3352411.3352434","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3352411.3352434","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 2nd International Conference on Data Science and Information Technology","raw_type":"proceedings-article"},"type":"conference-paper","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/A5100704151","display_name":"Yunfei Liu","orcid":"https://orcid.org/0000-0003-0725-7258"},"institutions":[{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunfei Liu","raw_affiliation_strings":["College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China","institution_ids":["https://openalex.org/I9842412"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100336778","display_name":"Jing Li","orcid":"https://orcid.org/0000-0001-6815-9729"},"institutions":[{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Li","raw_affiliation_strings":["College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China","institution_ids":["https://openalex.org/I9842412"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100730692","display_name":"Li Zhao","orcid":"https://orcid.org/0000-0001-5787-2705"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao Li","raw_affiliation_strings":["Grid Liaoning Electric Power Supply Co., Ltd, Shenyang, Liaoning, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Grid Liaoning Electric Power Supply Co., Ltd, Shenyang, Liaoning, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071363838","display_name":"Lin Qiao","orcid":"https://orcid.org/0000-0002-3923-220X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lin Qiao","raw_affiliation_strings":["Grid Liaoning Electric Power Supply Co., Ltd, Shenyang, Liaoning, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Grid Liaoning Electric Power Supply Co., Ltd, Shenyang, Liaoning, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100366854","display_name":"Shuo Chen","orcid":"https://orcid.org/0000-0001-9398-0009"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shuo Chen","raw_affiliation_strings":["Grid Liaoning Electric Power Supply Co., Ltd, Shenyang, Liaoning, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Grid Liaoning Electric Power Supply Co., Ltd, Shenyang, Liaoning, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005533926","display_name":"Xuming Lv","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xuming Lv","raw_affiliation_strings":["Grid Liaoning Electric Power Supply Co., Ltd, Shenyang, Liaoning, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Grid Liaoning Electric Power Supply Co., Ltd, Shenyang, Liaoning, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.7965,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.64597079,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"140","last_page":"146"},"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/T14319","display_name":"Currency Recognition and Detection","score":0.9771999716758728,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9753999710083008,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/missing-data","display_name":"Missing data","score":0.8096614480018616},{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.673221230506897},{"id":"https://openalex.org/keywords/interpolation","display_name":"Interpolation (computer graphics)","score":0.604738175868988},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5977222919464111},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5786164999008179},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.5480738282203674},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.5248425006866455},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4839513599872589},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.44693219661712646},{"id":"https://openalex.org/keywords/iterative-method","display_name":"Iterative method","score":0.43746018409729004},{"id":"https://openalex.org/keywords/dissolved-gas-analysis","display_name":"Dissolved gas analysis","score":0.4354400932788849},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4136022925376892},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4042770564556122},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.38105154037475586},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2565532326698303},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1287609338760376}],"concepts":[{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.8096614480018616},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.673221230506897},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.604738175868988},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5977222919464111},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5786164999008179},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.5480738282203674},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.5248425006866455},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4839513599872589},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.44693219661712646},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.43746018409729004},{"id":"https://openalex.org/C81818771","wikidata":"https://www.wikidata.org/wiki/Q787515","display_name":"Dissolved gas analysis","level":5,"score":0.4354400932788849},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4136022925376892},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4042770564556122},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.38105154037475586},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2565532326698303},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1287609338760376},{"id":"https://openalex.org/C181335627","wikidata":"https://www.wikidata.org/wiki/Q901418","display_name":"Transformer oil","level":4,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3352411.3352434","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3352411.3352434","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 2nd International Conference on Data Science and Information Technology","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W2009184503","https://openalex.org/W2014698734","https://openalex.org/W2034814312","https://openalex.org/W2050767557","https://openalex.org/W2080850431","https://openalex.org/W2295598076","https://openalex.org/W2415033110","https://openalex.org/W2739312105","https://openalex.org/W2743084590","https://openalex.org/W2886620460","https://openalex.org/W2910145865","https://openalex.org/W2921773775","https://openalex.org/W3102476541","https://openalex.org/W6638752421"],"related_works":["https://openalex.org/W2181530120","https://openalex.org/W4211215373","https://openalex.org/W2024529227","https://openalex.org/W2055961818","https://openalex.org/W1574575415","https://openalex.org/W3144172081","https://openalex.org/W3179858851","https://openalex.org/W3028371478","https://openalex.org/W2081476516","https://openalex.org/W2581984549"],"abstract_inverted_index":{"In":[0],"order":[1,58,66],"to":[2,36,43,67,117],"improve":[3],"the":[4,13,38,53,78,90,106,127],"accuracy":[5,108,129],"of":[6,9,15,45,48,59,70,99,109],"fault":[7,97,111],"diagnosis":[8,98,112],"power":[10,39],"transformer":[11,110],"in":[12,52,65,83],"presence":[14],"missing":[16,21,60,76,79],"values,":[17,77],"we":[18],"propose":[19],"a":[20],"value":[22],"imputation":[23],"method":[24,116],"by":[25,87,124],"iterative":[26,85],"k":[27],"nearest":[28],"neighbor":[29],"(KNN),":[30],"and":[31,126],"ensemble":[32,54,91],"learning":[33,92],"is":[34,62,94,122],"used":[35,95],"diagnose":[37],"transformer.":[40],"First,":[41],"according":[42],"importance":[44],"different":[46],"attributes":[47],"dissolved":[49],"gas":[50],"data":[51,61,74,120],"classification":[55],"method,":[56],"interpolation":[57],"determined.":[63],"Then,":[64],"take":[68],"advantage":[69],"valid":[71],"information":[72],"from":[73],"with":[75],"values":[80],"are":[81],"interpolated":[82],"an":[84],"manner":[86],"KNN.":[88],"Finally,":[89],"model":[93],"for":[96],"transformers.":[100],"The":[101],"experimental":[102],"results":[103],"show":[104],"that":[105],"average":[107,128],"after":[113],"using":[114],"this":[115],"interpolate":[118],"DGA":[119],"sets":[121],"increased":[123],"15.4%,":[125],"can":[130],"reach":[131],"82%.":[132]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}