{"id":"https://openalex.org/W3044574934","doi":"https://doi.org/10.3390/sym12081204","title":"Imbalanced Data Fault Diagnosis Based on an Evolutionary Online Sequential Extreme Learning Machine","display_name":"Imbalanced Data Fault Diagnosis Based on an Evolutionary Online Sequential Extreme Learning Machine","publication_year":2020,"publication_date":"2020-07-22","ids":{"openalex":"https://openalex.org/W3044574934","doi":"https://doi.org/10.3390/sym12081204","mag":"3044574934"},"language":"en","primary_location":{"id":"doi:10.3390/sym12081204","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym12081204","pdf_url":"https://www.mdpi.com/2073-8994/12/8/1204/pdf?version=1595485944","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2073-8994/12/8/1204/pdf?version=1595485944","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101637773","display_name":"Wei Hao","orcid":"https://orcid.org/0000-0002-2148-816X"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]},{"id":"https://openalex.org/I4210161003","display_name":"Qingdao Center of Resource Chemistry and New Materials","ror":"https://ror.org/05x85k702","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210089297","https://openalex.org/I4210161003"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wei Hao","raw_affiliation_strings":["Department of Information Technology, CRRC Qingdao Sifang Limited Company, Qingdao 266111, China","School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China"],"affiliations":[{"raw_affiliation_string":"Department of Information Technology, CRRC Qingdao Sifang Limited Company, Qingdao 266111, China","institution_ids":["https://openalex.org/I4210161003"]},{"raw_affiliation_string":"School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100610450","display_name":"Feng Liu","orcid":"https://orcid.org/0000-0002-4554-8307"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feng Liu","raw_affiliation_strings":["School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China"],"affiliations":[{"raw_affiliation_string":"School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China","institution_ids":["https://openalex.org/I21193070"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101637773"],"corresponding_institution_ids":["https://openalex.org/I21193070","https://openalex.org/I4210161003"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2165},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2165},"fwci":2.3313,"has_fulltext":false,"cited_by_count":27,"citation_normalized_percentile":{"value":0.90723748,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":93,"max":99},"biblio":{"volume":"12","issue":"8","first_page":"1204","last_page":"1204"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":1.0,"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/T12676","display_name":"Machine Learning and ELM","score":1.0,"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/T10663","display_name":"Advanced Battery Technologies Research","score":0.9829999804496765,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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":0.9628000259399414,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/undersampling","display_name":"Undersampling","score":0.7836718559265137},{"id":"https://openalex.org/keywords/oversampling","display_name":"Oversampling","score":0.7671351432800293},{"id":"https://openalex.org/keywords/resampling","display_name":"Resampling","score":0.7001307010650635},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6796377897262573},{"id":"https://openalex.org/keywords/extreme-learning-machine","display_name":"Extreme learning machine","score":0.6755748391151428},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5441305637359619},{"id":"https://openalex.org/keywords/axle","display_name":"Axle","score":0.5039140582084656},{"id":"https://openalex.org/keywords/euclidean-distance","display_name":"Euclidean distance","score":0.4708254635334015},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.4654472768306732},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.39086881279945374},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36440330743789673},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3417215347290039},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.1853187382221222},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1377861499786377},{"id":"https://openalex.org/keywords/bandwidth","display_name":"Bandwidth (computing)","score":0.06820434331893921}],"concepts":[{"id":"https://openalex.org/C136536468","wikidata":"https://www.wikidata.org/wiki/Q1225894","display_name":"Undersampling","level":2,"score":0.7836718559265137},{"id":"https://openalex.org/C197323446","wikidata":"https://www.wikidata.org/wiki/Q331222","display_name":"Oversampling","level":3,"score":0.7671351432800293},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.7001307010650635},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6796377897262573},{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.6755748391151428},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5441305637359619},{"id":"https://openalex.org/C129727815","wikidata":"https://www.wikidata.org/wiki/Q188209","display_name":"Axle","level":2,"score":0.5039140582084656},{"id":"https://openalex.org/C120174047","wikidata":"https://www.wikidata.org/wiki/Q847073","display_name":"Euclidean distance","level":2,"score":0.4708254635334015},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.4654472768306732},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.39086881279945374},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36440330743789673},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3417215347290039},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.1853187382221222},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1377861499786377},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.06820434331893921},{"id":"https://openalex.org/C165205528","wikidata":"https://www.wikidata.org/wiki/Q83371","display_name":"Seismology","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/sym12081204","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym12081204","pdf_url":"https://www.mdpi.com/2073-8994/12/8/1204/pdf?version=1595485944","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:e7ebf4ed13e544d4aa4658307aa017c0","is_oa":true,"landing_page_url":"https://doaj.org/article/e7ebf4ed13e544d4aa4658307aa017c0","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Symmetry, Vol 12, Iss 8, p 1204 (2020)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2073-8994/12/8/1204/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/sym12081204","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Symmetry","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/sym12081204","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym12081204","pdf_url":"https://www.mdpi.com/2073-8994/12/8/1204/pdf?version=1595485944","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3044574934.pdf","grobid_xml":"https://content.openalex.org/works/W3044574934.grobid-xml"},"referenced_works_count":23,"referenced_works":["https://openalex.org/W2026131661","https://openalex.org/W2033110225","https://openalex.org/W2040604977","https://openalex.org/W2090135786","https://openalex.org/W2090854192","https://openalex.org/W2143560894","https://openalex.org/W2158054309","https://openalex.org/W2346411765","https://openalex.org/W2348795632","https://openalex.org/W2361689256","https://openalex.org/W2362922403","https://openalex.org/W2367592867","https://openalex.org/W2547867778","https://openalex.org/W2765164841","https://openalex.org/W2768552067","https://openalex.org/W2779931100","https://openalex.org/W2954772338","https://openalex.org/W2981025102","https://openalex.org/W3001293080","https://openalex.org/W3005106957","https://openalex.org/W3006019444","https://openalex.org/W6656866805","https://openalex.org/W7000372966"],"related_works":["https://openalex.org/W32988189","https://openalex.org/W4389233021","https://openalex.org/W4308469503","https://openalex.org/W2904737874","https://openalex.org/W80466363","https://openalex.org/W2399571531","https://openalex.org/W2947132063","https://openalex.org/W4390415670","https://openalex.org/W2023639956","https://openalex.org/W3188632291"],"abstract_inverted_index":{"To":[0],"quickly":[1],"and":[2,78,119,129,175,188],"effectively":[3],"identify":[4],"an":[5,16,127],"axle":[6,144,158],"box":[7,145,159],"bearing":[8,146],"fault":[9,24],"of":[10,113,122,149,156,172],"high-speed":[11,150],"electric":[12],"multiple":[13],"units":[14],"(EMUs),":[15],"evolutionary":[17],"online":[18],"sequential":[19],"extreme":[20],"learning":[21],"machine":[22],"(OS-ELM)":[23],"diagnosis":[25,97,131],"method":[26,54,65,139,168],"for":[27,58,72,126],"imbalanced":[28],"data":[29,81,148,183],"was":[30,140,161],"proposed.":[31],"In":[32],"this":[33],"scheme,":[34],"the":[35,43,47,60,74,79,86,90,96,100,111,120,130,143,154,157,167,170,180],"resampling":[36,44],"scale":[37],"is":[38,55,70,93,106,133],"first":[39],"determined":[40],"according":[41],"to":[42,108,135,185],"empirical":[45],"formulation,":[46],"K-means":[48],"synthetic":[49],"minority":[50,61,181],"oversampling":[51,59],"technique":[52],"(SMOTE)":[53],"then":[56],"used":[57,107],"class":[62,76,182],"samples,":[63,77],"a":[64],"based":[66],"on":[67,142,152],"Euclidean":[68],"distance":[69],"applied":[71],"undersampling":[73],"majority":[75],"complex":[80],"features":[82],"are":[83],"extracted":[84],"from":[85],"reconstructed":[87,91],"dataset.":[88],"Second,":[89],"dataset":[92],"input":[94,114],"into":[95],"model.":[98],"Finally,":[99],"artificial":[101],"bee":[102],"colony":[103],"(ABC)":[104],"algorithm":[105],"globally":[109],"optimize":[110],"combination":[112],"weights,":[115],"hidden":[116,123],"layer":[117,124],"bias,":[118],"number":[121],"nodes":[125],"OS-ELM,":[128],"model":[132],"allowed":[134],"evolve.":[136],"The":[137],"proposed":[138],"tested":[141],"monitoring":[147],"EMUs,":[151],"which":[153],"position":[155],"bearings":[160],"symmetrical.":[162],"Numerical":[163],"testing":[164],"proved":[165],"that":[166],"has":[169],"characteristics":[171],"faster":[173],"detection":[174],"higher":[176],"classification":[177],"performance":[178],"regarding":[179],"compared":[184],"other":[186],"standard":[187],"classical":[189],"algorithms.":[190]},"counts_by_year":[{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}