{"id":"https://openalex.org/W4396682681","doi":"https://doi.org/10.1145/3645279.3645291","title":"Fault Diagnosis Model of GIS Equipment Based on Information Fusion Technology","display_name":"Fault Diagnosis Model of GIS Equipment Based on Information Fusion Technology","publication_year":2023,"publication_date":"2023-11-17","ids":{"openalex":"https://openalex.org/W4396682681","doi":"https://doi.org/10.1145/3645279.3645291"},"language":"en","primary_location":{"id":"doi:10.1145/3645279.3645291","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3645279.3645291","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 International Conference on Big Data Mining and Information Processing","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/A5009302595","display_name":"Shengli Wu","orcid":"https://orcid.org/0009-0006-4640-3456"},"institutions":[{"id":"https://openalex.org/I4210088511","display_name":"Inner Mongolia Electric Power (China)","ror":"https://ror.org/0041szh84","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210088511"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shengli Wu","raw_affiliation_strings":["State Grid Inner Mongolia Eastern Power Co., Ltd., China"],"raw_orcid":"https://orcid.org/0009-0006-4640-3456","affiliations":[{"raw_affiliation_string":"State Grid Inner Mongolia Eastern Power Co., Ltd., China","institution_ids":["https://openalex.org/I4210088511"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079703983","display_name":"Yong Mei Zhang","orcid":"https://orcid.org/0009-0001-6102-2874"},"institutions":[{"id":"https://openalex.org/I4210088511","display_name":"Inner Mongolia Electric Power (China)","ror":"https://ror.org/0041szh84","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210088511"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yong Zhang","raw_affiliation_strings":["State Grid Inner Mongolia Eastern Power Co., Ltd., China"],"raw_orcid":"https://orcid.org/0009-0001-6102-2874","affiliations":[{"raw_affiliation_string":"State Grid Inner Mongolia Eastern Power Co., Ltd., China","institution_ids":["https://openalex.org/I4210088511"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007792998","display_name":"Chunhui Gao","orcid":"https://orcid.org/0009-0008-3135-1133"},"institutions":[{"id":"https://openalex.org/I4210088511","display_name":"Inner Mongolia Electric Power (China)","ror":"https://ror.org/0041szh84","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210088511"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunhui Gao","raw_affiliation_strings":["State Grid Inner Mongolia Eastern Power Co., Ltd., China"],"raw_orcid":"https://orcid.org/0009-0008-3135-1133","affiliations":[{"raw_affiliation_string":"State Grid Inner Mongolia Eastern Power Co., Ltd., China","institution_ids":["https://openalex.org/I4210088511"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038976674","display_name":"Xinwen Feng","orcid":"https://orcid.org/0009-0002-5592-2152"},"institutions":[{"id":"https://openalex.org/I4210088511","display_name":"Inner Mongolia Electric Power (China)","ror":"https://ror.org/0041szh84","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210088511"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinwen Feng","raw_affiliation_strings":["State Grid Inner Mongolia Eastern Power Co., Ltd., China"],"raw_orcid":"https://orcid.org/0009-0002-5592-2152","affiliations":[{"raw_affiliation_string":"State Grid Inner Mongolia Eastern Power Co., Ltd., China","institution_ids":["https://openalex.org/I4210088511"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037419105","display_name":"Xi Hong Chen","orcid":"https://orcid.org/0009-0008-7752-0406"},"institutions":[{"id":"https://openalex.org/I4210088511","display_name":"Inner Mongolia Electric Power (China)","ror":"https://ror.org/0041szh84","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210088511"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xi Chen","raw_affiliation_strings":["State Grid Inner Mongolia Eastern Power Co., Ltd., China"],"raw_orcid":"https://orcid.org/0009-0008-7752-0406","affiliations":[{"raw_affiliation_string":"State Grid Inner Mongolia Eastern Power Co., Ltd., China","institution_ids":["https://openalex.org/I4210088511"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5009302595"],"corresponding_institution_ids":["https://openalex.org/I4210088511"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.22364596,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"65","last_page":"71"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.8690000176429749,"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"}},"topics":[{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.8690000176429749,"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"}},{"id":"https://openalex.org/T10809","display_name":"Occupational Health and Safety Research","score":0.8245999813079834,"subfield":{"id":"https://openalex.org/subfields/3614","display_name":"Radiological and Ultrasound Technology"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.7390999794006348,"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/data-mining","display_name":"Data mining","score":0.6233730316162109},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6139228343963623},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.591230571269989},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.5702894926071167},{"id":"https://openalex.org/keywords/reliability-engineering","display_name":"Reliability engineering","score":0.5552802681922913},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5458171963691711},{"id":"https://openalex.org/keywords/information-fusion","display_name":"Information fusion","score":0.5343061685562134},{"id":"https://openalex.org/keywords/rough-set","display_name":"Rough set","score":0.486287921667099},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.4655535817146301},{"id":"https://openalex.org/keywords/fuse","display_name":"Fuse (electrical)","score":0.4653165340423584},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.4635787010192871},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.46131631731987},{"id":"https://openalex.org/keywords/geographic-information-system","display_name":"Geographic information system","score":0.429108589887619},{"id":"https://openalex.org/keywords/fault-model","display_name":"Fault model","score":0.41535383462905884},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.30282077193260193},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.2579338848590851}],"concepts":[{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6233730316162109},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6139228343963623},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.591230571269989},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.5702894926071167},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.5552802681922913},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5458171963691711},{"id":"https://openalex.org/C2982962833","wikidata":"https://www.wikidata.org/wiki/Q17092450","display_name":"Information fusion","level":2,"score":0.5343061685562134},{"id":"https://openalex.org/C111012933","wikidata":"https://www.wikidata.org/wiki/Q3137210","display_name":"Rough set","level":2,"score":0.486287921667099},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.4655535817146301},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.4653165340423584},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.4635787010192871},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.46131631731987},{"id":"https://openalex.org/C41856607","wikidata":"https://www.wikidata.org/wiki/Q483130","display_name":"Geographic information system","level":2,"score":0.429108589887619},{"id":"https://openalex.org/C167391956","wikidata":"https://www.wikidata.org/wiki/Q1401211","display_name":"Fault model","level":3,"score":0.41535383462905884},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.30282077193260193},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2579338848590851},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"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/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.0},{"id":"https://openalex.org/C134146338","wikidata":"https://www.wikidata.org/wiki/Q1815901","display_name":"Electronic circuit","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C165205528","wikidata":"https://www.wikidata.org/wiki/Q83371","display_name":"Seismology","level":1,"score":0.0},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","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/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3645279.3645291","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3645279.3645291","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 International Conference on Big Data Mining and Information Processing","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":0.4300000071525574}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":1,"referenced_works":["https://openalex.org/W3185972919"],"related_works":["https://openalex.org/W3000097931","https://openalex.org/W2354322770","https://openalex.org/W4237547500","https://openalex.org/W1570848052","https://openalex.org/W1992380313","https://openalex.org/W2364976037","https://openalex.org/W2372770459","https://openalex.org/W2790862507","https://openalex.org/W3036482021","https://openalex.org/W4385351700"],"abstract_inverted_index":{"Traditional":[0],"fault":[1,43,163,229],"diagnosis":[2,44,55,115,164,230],"methods":[3],"are":[4],"often":[5],"difficult":[6],"to":[7,93,97,132],"diagnose":[8],"accurately":[9],"and":[10,23,51,62,87,104,111,145,191,216,226],"efficiently":[11],"because":[12],"of":[13,32,56,73,78,116,147,152,160,181,196,206,218,231],"the":[14,30,53,67,76,99,113,124,128,135,138,143,148,158,172,184,194,197,213],"complex":[15,190,233],"relationships":[16],"such":[17,81],"as":[18,82],"multiple":[19,21,24],"faults,":[20],"representations":[22],"correlations":[25],"that":[26],"may":[27],"occur":[28],"during":[29,157],"operation":[31,214],"GIS":[33,41,57,161,219],"equipment.":[34,234],"Therefore,":[35],"this":[36,95,207],"paper":[37],"puts":[38],"forward":[39],"a":[40],"equipment":[42,58,79,117,162],"model":[45,68,101,125,139,176,198,208],"based":[46,166],"on":[47,167],"information":[48,64,74,96,168],"fusion":[49,169],"technology,":[50],"realizes":[52],"accurate":[54,114],"faults":[59,103,118],"through":[60],"multi-level":[61],"multi-source":[63],"fusion.":[65],"Specifically,":[66],"firstly":[69],"collects":[70],"all":[71],"kinds":[72],"in":[75,137],"process":[77],"operation,":[80],"temperature,":[83],"pressure,":[84],"vibration,":[85],"etc.,":[86],"then":[88],"uses":[89],"Bayesian":[90],"network":[91,109],"technology":[92],"fuse":[94],"establish":[98],"relationship":[100],"between":[102],"representative":[105],"state":[106],"variables.":[107],"Through":[108],"learning":[110],"reasoning,":[112],"can":[119,209],"be":[120],"realized.":[121],"In":[122,189],"addition,":[123],"also":[126,222],"adopts":[127],"uncertainty":[129,136],"processing":[130],"method":[131],"deal":[133],"with":[134,183],"reasonably,":[140],"which":[141],"improves":[142],"robustness":[144],"reliability":[146],"model.":[149],"A":[150],"series":[151],"quantitative":[153],"data":[154],"were":[155],"collected":[156],"evaluation":[159],"models":[165],"technology.":[170],"On":[171],"test":[173],"set,":[174],"my":[175],"achieved":[177],"an":[178],"average":[179],"accuracy":[180,186,195],"92%,":[182],"highest":[185],"reaching":[187],"95%.":[188],"multi-fault":[192],"scenarios,":[193],"remains":[199],"stable":[200],"at":[201],"over":[202],"88%.":[203],"The":[204],"application":[205],"not":[210],"only":[211],"improve":[212],"efficiency":[215],"safety":[217],"equipment,":[220],"but":[221],"provide":[223],"useful":[224],"reference":[225],"enlightenment":[227],"for":[228],"other":[232]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}