{"id":"https://openalex.org/W3090622696","doi":"https://doi.org/10.1109/ijcnn48605.2020.9207133","title":"Metric Learning for Electrical Submersible Pump Fault Diagnosis","display_name":"Metric Learning for Electrical Submersible Pump Fault Diagnosis","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3090622696","doi":"https://doi.org/10.1109/ijcnn48605.2020.9207133","mag":"3090622696"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn48605.2020.9207133","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9207133","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","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/A5033081030","display_name":"Lucas Henrique Sousa Mello","orcid":"https://orcid.org/0000-0003-3601-8782"},"institutions":[{"id":"https://openalex.org/I51235708","display_name":"Universidade Federal do Esp\u00edrito Santo","ror":"https://ror.org/05sxf4h28","country_code":"BR","type":"education","lineage":["https://openalex.org/I51235708"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Lucas Henrique Sousa Mello","raw_affiliation_strings":["Department of Computer Science, Federal University of Esp\u00edrito Santo, Vit\u00f3ria, ES, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Federal University of Esp\u00edrito Santo, Vit\u00f3ria, ES, Brazil","institution_ids":["https://openalex.org/I51235708"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005499007","display_name":"Marcos Pellegrini Ribeiro","orcid":"https://orcid.org/0000-0002-8605-942X"},"institutions":[{"id":"https://openalex.org/I32393484","display_name":"Petrobras (Brazil)","ror":"https://ror.org/0235kyq22","country_code":"BR","type":"company","lineage":["https://openalex.org/I32393484"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Marcos Pellegrini Ribeiro","raw_affiliation_strings":["CENPES/PDP/TE Petrobras, Rio de Janeiro, RJ, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CENPES/PDP/TE Petrobras, Rio de Janeiro, RJ, Brazil","institution_ids":["https://openalex.org/I32393484"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101714551","display_name":"Thiago Oliveira dos Santos","orcid":"https://orcid.org/0000-0003-2515-1852"},"institutions":[{"id":"https://openalex.org/I51235708","display_name":"Universidade Federal do Esp\u00edrito Santo","ror":"https://ror.org/05sxf4h28","country_code":"BR","type":"education","lineage":["https://openalex.org/I51235708"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Thiago Oliveira Santos","raw_affiliation_strings":["Department of Computer Science, Federal University of Esp\u00edrito Santo, Vit\u00f3ria, ES, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Federal University of Esp\u00edrito Santo, Vit\u00f3ria, ES, Brazil","institution_ids":["https://openalex.org/I51235708"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007198974","display_name":"Fl\u00e1vio Miguel Varej\u00e3o","orcid":"https://orcid.org/0000-0002-5444-1974"},"institutions":[{"id":"https://openalex.org/I51235708","display_name":"Universidade Federal do Esp\u00edrito Santo","ror":"https://ror.org/05sxf4h28","country_code":"BR","type":"education","lineage":["https://openalex.org/I51235708"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Flavio Miguel Varejao","raw_affiliation_strings":["Department of Computer Science, Federal University of Esp\u00edrito Santo, Vit\u00f3ria, ES, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Federal University of Esp\u00edrito Santo, Vit\u00f3ria, ES, Brazil","institution_ids":["https://openalex.org/I51235708"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023566044","display_name":"Alexandre Rodrigues","orcid":"https://orcid.org/0000-0002-7619-2681"},"institutions":[{"id":"https://openalex.org/I51235708","display_name":"Universidade Federal do Esp\u00edrito Santo","ror":"https://ror.org/05sxf4h28","country_code":"BR","type":"education","lineage":["https://openalex.org/I51235708"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Alexandre Loureiros Rodrigues","raw_affiliation_strings":["Department of Statistics, Federal University of Esp\u00edrito Santo, Vit\u00f3ria, ES, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, Federal University of Esp\u00edrito Santo, Vit\u00f3ria, ES, Brazil","institution_ids":["https://openalex.org/I51235708"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.7246,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.71085313,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13050","display_name":"Oil and Gas Production Techniques","score":0.9993000030517578,"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"}},"topics":[{"id":"https://openalex.org/T13050","display_name":"Oil and Gas Production Techniques","score":0.9993000030517578,"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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9955999851226807,"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/T11220","display_name":"Water Systems and Optimization","score":0.9905999898910522,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7701941728591919},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6934725642204285},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.6074880957603455},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6018801331520081},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.580490231513977},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5731173753738403},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.5698449015617371},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5592164397239685},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.534076452255249},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5205696225166321},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4993119239807129},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4681447148323059},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4485531747341156},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.36097243428230286},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13715136051177979}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7701941728591919},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6934725642204285},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.6074880957603455},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6018801331520081},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.580490231513977},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5731173753738403},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.5698449015617371},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5592164397239685},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.534076452255249},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5205696225166321},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4993119239807129},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4681447148323059},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4485531747341156},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36097243428230286},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13715136051177979},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn48605.2020.9207133","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9207133","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.6499999761581421}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1480376833","https://openalex.org/W2096733369","https://openalex.org/W2101234009","https://openalex.org/W2119821739","https://openalex.org/W2120199131","https://openalex.org/W2122111042","https://openalex.org/W2170505850","https://openalex.org/W2330820318","https://openalex.org/W2577263376","https://openalex.org/W2734475324","https://openalex.org/W2803697948","https://openalex.org/W2899771611","https://openalex.org/W2911964244","https://openalex.org/W2963775347","https://openalex.org/W2969656782","https://openalex.org/W3099206234","https://openalex.org/W4239510810","https://openalex.org/W6638791942","https://openalex.org/W6675354045","https://openalex.org/W6756040250"],"related_works":["https://openalex.org/W4367336074","https://openalex.org/W3154045278","https://openalex.org/W4379620016","https://openalex.org/W4393666307","https://openalex.org/W3210764983","https://openalex.org/W4393443811","https://openalex.org/W4367335949","https://openalex.org/W3089416646","https://openalex.org/W4396816114","https://openalex.org/W4380048833"],"abstract_inverted_index":{"Machine":[0],"learning":[1,19,26],"classification":[2,152],"algorithms":[3],"are":[4,113],"highly":[5],"dependent":[6],"of":[7,11,34,81,84,124],"a":[8,17,35,51,56,76],"dataset":[9],"composed":[10],"high-level":[12],"features.":[13],"In":[14],"this":[15],"paper,":[16],"deep":[18],"approach":[20,48],"is":[21,86,140],"combined":[22],"with":[23,55,88],"traditional":[24],"machine":[25],"classifiers":[27,96],"in":[28,115,122],"order":[29],"to":[30,132],"circumvent":[31],"the":[32,66,79,89,133,136,147],"need":[33],"specialist":[36,77],"for":[37,60,151],"extracting":[38,61],"relevant":[39,62],"features":[40,63,117],"from":[41,65],"one":[42],"dimensional":[43],"frequency-domain":[44],"vibration":[45],"signals.":[46],"Our":[47],"relies":[49],"on":[50],"convolutional":[52],"architecture":[53],"trained":[54,114],"triplet":[57],"loss":[58],"function":[59],"directly":[64],"raw":[67],"data.":[68],"A":[69],"previously":[70],"hand-crafted":[71,148],"feature":[72,92,138,149],"set,":[73,150],"created":[74],"by":[75],"over":[78],"course":[80],"many":[82],"years":[83],"research,":[85],"compared":[87,121],"newly":[90],"extracted":[91,137],"set.":[93],"Six":[94],"conventional":[95],"models":[97],"(K-Nearest":[98],"Neighbors,":[99],"Support":[100],"Vector":[101],"Machine,":[102],"Decision":[103],"Tree,":[104],"Random":[105],"Forest,":[106],"Quadratic":[107],"Discriminant":[108],"Analysis":[109],"and":[110,120],"Naive":[111],"Bayes)":[112],"both":[116],"set":[118,139],"separately":[119],"terms":[123],"macro":[125],"F-measure.":[126],"Results":[127],"shows":[128],"statistical":[129],"evidence":[130],"towards":[131],"acceptance":[134],"that":[135],"as":[141,143],"good":[142],"or":[144],"better":[145],"than":[146],"purposes.":[153]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}