{"id":"https://openalex.org/W4407466529","doi":"https://doi.org/10.1109/iccvit63928.2024.10872511","title":"Research on Pump Condition Prediction Based on Ensemble Learning Strategies","display_name":"Research on Pump Condition Prediction Based on Ensemble Learning Strategies","publication_year":2024,"publication_date":"2024-11-24","ids":{"openalex":"https://openalex.org/W4407466529","doi":"https://doi.org/10.1109/iccvit63928.2024.10872511"},"language":"en","primary_location":{"id":"doi:10.1109/iccvit63928.2024.10872511","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccvit63928.2024.10872511","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 2nd International Conference on Computer, Vision and Intelligent Technology (ICCVIT)","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/A5101128207","display_name":"Dandan Dong","orcid":null},"institutions":[{"id":"https://openalex.org/I204553293","display_name":"China University of Petroleum, Beijing","ror":"https://ror.org/041qf4r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I204553293"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dandan Dong","raw_affiliation_strings":["China University of Petroleum-Beijing at Karamay,Department of Computer Science,Karamay,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China University of Petroleum-Beijing at Karamay,Department of Computer Science,Karamay,China","institution_ids":["https://openalex.org/I204553293"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016325459","display_name":"Zhiyang Jia","orcid":"https://orcid.org/0000-0001-8423-8785"},"institutions":[{"id":"https://openalex.org/I204553293","display_name":"China University of Petroleum, Beijing","ror":"https://ror.org/041qf4r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I204553293"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiyang Jia","raw_affiliation_strings":["China University of Petroleum-Beijing at Karamay,Department of Computer Science,Karamay,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China University of Petroleum-Beijing at Karamay,Department of Computer Science,Karamay,China","institution_ids":["https://openalex.org/I204553293"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074916212","display_name":"Yi\u2010Chang Li","orcid":"https://orcid.org/0000-0002-7558-5942"},"institutions":[{"id":"https://openalex.org/I204553293","display_name":"China University of Petroleum, Beijing","ror":"https://ror.org/041qf4r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I204553293"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yichang Li","raw_affiliation_strings":["China University of Petroleum-Beijing at Karamay,Department of Computer Science,Karamay,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China University of Petroleum-Beijing at Karamay,Department of Computer Science,Karamay,China","institution_ids":["https://openalex.org/I204553293"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101488513","display_name":"Yudong Sun","orcid":"https://orcid.org/0000-0003-0501-2038"},"institutions":[{"id":"https://openalex.org/I204553293","display_name":"China University of Petroleum, Beijing","ror":"https://ror.org/041qf4r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I204553293"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yudong Sun","raw_affiliation_strings":["China University of Petroleum-Beijing at Karamay,Department of Computer Science,Karamay,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China University of Petroleum-Beijing at Karamay,Department of Computer Science,Karamay,China","institution_ids":["https://openalex.org/I204553293"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057284460","display_name":"Kang Ji","orcid":"https://orcid.org/0000-0001-7338-9772"},"institutions":[{"id":"https://openalex.org/I4210154630","display_name":"Karamay Central Hospital of Xinjiang","ror":"https://ror.org/04cxszt91","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210154630"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kang Ji","raw_affiliation_strings":["Xinjiang Oilfield Company,Data Company,Karamay,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xinjiang Oilfield Company,Data Company,Karamay,China","institution_ids":["https://openalex.org/I4210154630"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.2858,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.83751372,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11372","display_name":"Hydraulic and Pneumatic Systems","score":0.9980999827384949,"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"}},"topics":[{"id":"https://openalex.org/T11372","display_name":"Hydraulic and Pneumatic Systems","score":0.9980999827384949,"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/T13050","display_name":"Oil and Gas Production Techniques","score":0.9955999851226807,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/T13644","display_name":"Control Systems in Engineering","score":0.9743000268936157,"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/ensemble-learning","display_name":"Ensemble learning","score":0.6874156594276428},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6475903987884521},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4843648374080658},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4653953015804291}],"concepts":[{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.6874156594276428},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6475903987884521},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4843648374080658},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4653953015804291}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccvit63928.2024.10872511","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccvit63928.2024.10872511","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 2nd International Conference on Computer, Vision and Intelligent Technology (ICCVIT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":3,"referenced_works":["https://openalex.org/W2767350431","https://openalex.org/W2810292802","https://openalex.org/W2945833139"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W3046775127","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474"],"abstract_inverted_index":{"Failures":[0],"in":[1,14],"water":[2,20,47,95],"pumps":[3,48],"can":[4],"lead":[5],"to":[6,39,127],"shutdowns,":[7],"production":[8],"disruptions,":[9],"and":[10,22,30,36,44,61,86,136],"resource":[11],"wastage,":[12],"particularly":[13],"areas":[15],"such":[16],"as":[17],"industrial":[18],"production,":[19],"supply,":[21],"flood":[23],"control,":[24],"where":[25],"the":[26,31,41,91,94,106,129,133,148],"impact":[27],"is":[28,49,125],"significant":[29],"losses":[32],"immeasurable.":[33],"Therefore,":[34],"monitoring":[35],"early":[37],"warning":[38],"enhance":[40],"operational":[42],"efficiency":[43],"stability":[45],"of":[46,93,115,143],"crucial.":[50],"Based":[51],"on":[52,123],"research":[53],"into":[54],"deep":[55],"learning":[56,71,120],"algorithms,":[57],"this":[58],"paper":[59],"proposes":[60],"implements":[62],"a":[63],"pump":[64,96],"condition":[65],"prediction":[66,130,150],"model":[67,108],"using":[68],"an":[69,112,118,140],"ensemble":[70,119],"strategy.":[72],"By":[73],"constructing":[74],"three":[75],"different":[76,98],"models:":[77],"Long":[78],"Short-Term":[79],"Memory":[80],"(LSTM),":[81],"Convolutional":[82],"Neural":[83,88],"Network":[84,89],"(CNN),":[85],"Deep":[87],"(DNN),":[90],"states":[92],"within":[97],"time":[99],"windows":[100],"are":[101],"predicted":[102],"respectively.":[103],"Among":[104],"them,":[105],"LSTM":[107],"performs":[109],"better,":[110],"achieving":[111,139],"accuracy":[113,141],"rate":[114,142],"98.3%.":[116],"Finally,":[117],"strategy":[121],"based":[122],"voting":[124],"employed":[126],"integrate":[128],"results":[131],"from":[132],"LSTM,":[134],"CNN,":[135],"DNN":[137],"models,":[138],"98.5%.":[144],"This":[145],"approach":[146],"improves":[147],"overall":[149],"performance.":[151]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}