{"id":"https://openalex.org/W3110992226","doi":"https://doi.org/10.1109/tim.2020.3041087","title":"Multiple-Order Graphical Deep Extreme Learning Machine for Unsupervised Fault Diagnosis of Rolling Bearing","display_name":"Multiple-Order Graphical Deep Extreme Learning Machine for Unsupervised Fault Diagnosis of Rolling Bearing","publication_year":2020,"publication_date":"2020-12-10","ids":{"openalex":"https://openalex.org/W3110992226","doi":"https://doi.org/10.1109/tim.2020.3041087","mag":"3110992226"},"language":"en","primary_location":{"id":"doi:10.1109/tim.2020.3041087","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2020.3041087","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Instrumentation and Measurement","raw_type":"journal-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/A5045686678","display_name":"Xiaoli Zhao","orcid":"https://orcid.org/0000-0002-9803-4158"},"institutions":[{"id":"https://openalex.org/I141945490","display_name":"University of British Columbia","ror":"https://ror.org/03rmrcq20","country_code":"CA","type":"education","lineage":["https://openalex.org/I141945490"]},{"id":"https://openalex.org/I4405260628","display_name":"University of British Columbia, Okanagan Campus","ror":"https://ror.org/04241wz75","country_code":"CA","type":"education","lineage":["https://openalex.org/I141945490","https://openalex.org/I4405260628"]},{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CA","CN"],"is_corresponding":true,"raw_author_name":"Xiaoli Zhao","raw_affiliation_strings":["School of Engineering, The University of British Columbia, Kelowna, Canada","School of Mechanical Engineering, Southeast University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Engineering, The University of British Columbia, Kelowna, Canada","institution_ids":["https://openalex.org/I141945490","https://openalex.org/I4405260628"]},{"raw_affiliation_string":"School of Mechanical Engineering, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029522441","display_name":"Minping Jia","orcid":"https://orcid.org/0000-0001-9010-2307"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Minping Jia","raw_affiliation_strings":["School of Mechanical Engineering, Southeast University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Mechanical Engineering, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004785718","display_name":"Junchi Bin","orcid":"https://orcid.org/0000-0002-6493-561X"},"institutions":[{"id":"https://openalex.org/I141945490","display_name":"University of British Columbia","ror":"https://ror.org/03rmrcq20","country_code":"CA","type":"education","lineage":["https://openalex.org/I141945490"]},{"id":"https://openalex.org/I4405260628","display_name":"University of British Columbia, Okanagan Campus","ror":"https://ror.org/04241wz75","country_code":"CA","type":"education","lineage":["https://openalex.org/I141945490","https://openalex.org/I4405260628"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Junchi Bin","raw_affiliation_strings":["School of Engineering, The University of British Columbia, Kelowna, Canada"],"affiliations":[{"raw_affiliation_string":"School of Engineering, The University of British Columbia, Kelowna, Canada","institution_ids":["https://openalex.org/I141945490","https://openalex.org/I4405260628"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015912576","display_name":"Teng Wang","orcid":"https://orcid.org/0000-0002-4836-1740"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Teng Wang","raw_affiliation_strings":["School of Mechanical Engineering, Southeast University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Mechanical Engineering, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100423697","display_name":"Zheng Liu","orcid":"https://orcid.org/0000-0002-7241-3483"},"institutions":[{"id":"https://openalex.org/I141945490","display_name":"University of British Columbia","ror":"https://ror.org/03rmrcq20","country_code":"CA","type":"education","lineage":["https://openalex.org/I141945490"]},{"id":"https://openalex.org/I4405260628","display_name":"University of British Columbia, Okanagan Campus","ror":"https://ror.org/04241wz75","country_code":"CA","type":"education","lineage":["https://openalex.org/I141945490","https://openalex.org/I4405260628"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Zheng Liu","raw_affiliation_strings":["School of Engineering, The University of British Columbia, Kelowna, Canada"],"affiliations":[{"raw_affiliation_string":"School of Engineering, The University of British Columbia, Kelowna, Canada","institution_ids":["https://openalex.org/I141945490","https://openalex.org/I4405260628"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5045686678"],"corresponding_institution_ids":["https://openalex.org/I141945490","https://openalex.org/I4405260628","https://openalex.org/I76569877"],"apc_list":null,"apc_paid":null,"fwci":5.8987,"has_fulltext":false,"cited_by_count":57,"citation_normalized_percentile":{"value":0.96865334,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"70","issue":null,"first_page":"1","last_page":"12"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9995999932289124,"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":0.9995999932289124,"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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9980000257492065,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9782999753952026,"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/computer-science","display_name":"Computer science","score":0.6421136856079102},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6303516626358032},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5800054669380188},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.5579996109008789},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5082278251647949},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.499570369720459},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4675155282020569},{"id":"https://openalex.org/keywords/extreme-learning-machine","display_name":"Extreme learning machine","score":0.46339207887649536},{"id":"https://openalex.org/keywords/bearing","display_name":"Bearing (navigation)","score":0.45039787888526917},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.433754026889801},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.42867666482925415},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4274354577064514},{"id":"https://openalex.org/keywords/nonlinear-dimensionality-reduction","display_name":"Nonlinear dimensionality reduction","score":0.4268278181552887},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36606308817863464},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.17811644077301025},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.15607085824012756},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.1485435664653778}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6421136856079102},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6303516626358032},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5800054669380188},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.5579996109008789},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5082278251647949},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.499570369720459},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4675155282020569},{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.46339207887649536},{"id":"https://openalex.org/C199978012","wikidata":"https://www.wikidata.org/wiki/Q1273815","display_name":"Bearing (navigation)","level":2,"score":0.45039787888526917},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.433754026889801},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.42867666482925415},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4274354577064514},{"id":"https://openalex.org/C151876577","wikidata":"https://www.wikidata.org/wiki/Q7049464","display_name":"Nonlinear dimensionality reduction","level":3,"score":0.4268278181552887},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36606308817863464},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.17811644077301025},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.15607085824012756},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.1485435664653778},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tim.2020.3041087","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2020.3041087","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Instrumentation and Measurement","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.6100000143051147}],"awards":[{"id":"https://openalex.org/G6920906837","display_name":null,"funder_award_id":"52075095","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W1888005072","https://openalex.org/W1967879920","https://openalex.org/W1997070301","https://openalex.org/W2001141328","https://openalex.org/W2053186076","https://openalex.org/W2072857564","https://openalex.org/W2105767494","https://openalex.org/W2111072639","https://openalex.org/W2116341502","https://openalex.org/W2160815625","https://openalex.org/W2204756796","https://openalex.org/W2301541953","https://openalex.org/W2393319904","https://openalex.org/W2415243320","https://openalex.org/W2567582497","https://openalex.org/W2594244167","https://openalex.org/W2612872092","https://openalex.org/W2746111230","https://openalex.org/W2783074568","https://openalex.org/W2803768804","https://openalex.org/W2886574666","https://openalex.org/W2913720397","https://openalex.org/W2919115771","https://openalex.org/W2935987343","https://openalex.org/W2940935128","https://openalex.org/W2977117446","https://openalex.org/W3000591445","https://openalex.org/W3037107365","https://openalex.org/W3089183752","https://openalex.org/W3095770430","https://openalex.org/W6687890955","https://openalex.org/W6716303938"],"related_works":["https://openalex.org/W2067443264","https://openalex.org/W31566076","https://openalex.org/W4297902562","https://openalex.org/W2741186499","https://openalex.org/W2804652951","https://openalex.org/W2556335056","https://openalex.org/W2002678693","https://openalex.org/W1584764049","https://openalex.org/W2743832667","https://openalex.org/W4386889652"],"abstract_inverted_index":{"The":[0],"intelligent":[1,18],"fault":[2,19,51,116,142],"diagnosis":[3,20,52,143],"powered":[4],"deep":[5],"learning":[6],"(DL)":[7],"is":[8,57,73,85,97,110],"widely":[9],"applied":[10,86],"in":[11,59],"various":[12],"practical":[13],"industries,":[14],"but":[15],"the":[16,25,33,63,78,89,94,107,111,120,135,138,148,152,165],"conventional":[17],"methods":[21],"cannot":[22],"fully":[23],"juggle":[24],"manifold":[26],"structure":[27],"information":[28,104,163],"with":[29,81],"multiple-order":[30,75,122,149],"similarity":[31],"from":[32,164],"massive":[34],"unlabeled":[35],"industrial":[36,167],"data.":[37,168],"Thus,":[38],"a":[39,173],"new":[40],"Multiple-Order":[41],"Graphical":[42],"Deep":[43],"Extreme":[44],"Learning":[45],"Machine":[46],"(MGDELM)":[47],"algorithm":[48,66,140,155],"for":[49,100,177],"unsupervised":[50,74,112],"(UFD)":[53],"of":[54,129,137],"rolling":[55,130],"bearing":[56,131],"proposed":[58,139,153],"this":[60],"study.":[61],"Specifically,":[62],"developed":[64],"MGDELM":[65,154],"mainly":[67],"contains":[68],"two":[69,127],"parts:":[70],"1)":[71],"one":[72],"feature":[76],"extraction,":[77],"first-order":[79],"proximity":[80,96],"Cauchy":[82],"graph":[83,123],"embedded":[84],"to":[87],"extract":[88,158],"local":[90,159],"structural":[91,103,162],"information,":[92],"and":[93,105,141,160],"second-order":[95],"simultaneously":[98],"employed":[99],"mining":[101],"global":[102,161],"2)":[106],"other":[108],"used":[109],"Fuzzy-C-Mean":[113],"(FCM)":[114],"into":[115],"clustering":[117],"built":[118],"on":[119],"extracted":[121],"embedding":[124],"features.":[125],"Empirically,":[126],"cases":[128],"failure":[132],"data":[133],"validate":[134],"effectiveness":[136],"method.":[144],"By":[145],"jointly":[146],"optimizing":[147],"objective":[150],"function,":[151],"can":[156],"synchronously":[157],"raw":[166],"This":[169],"study":[170],"also":[171],"provides":[172],"novel":[174],"promising":[175],"approach":[176],"UFD.":[178]},"counts_by_year":[{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":22},{"year":2022,"cited_by_count":12},{"year":2021,"cited_by_count":9}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
