{"id":"https://openalex.org/W4383112699","doi":"https://doi.org/10.1109/tim.2023.3291768","title":"A Multiscale Graph Convolutional Neural Network Framework for Fault Diagnosis of Rolling Bearing","display_name":"A Multiscale Graph Convolutional Neural Network Framework for Fault Diagnosis of Rolling Bearing","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4383112699","doi":"https://doi.org/10.1109/tim.2023.3291768"},"language":"en","primary_location":{"id":"doi:10.1109/tim.2023.3291768","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2023.3291768","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/A5052432499","display_name":"Peizhe Yin","orcid":null},"institutions":[{"id":"https://openalex.org/I59028903","display_name":"Ocean University of China","ror":"https://ror.org/04rdtx186","country_code":"CN","type":"education","lineage":["https://openalex.org/I59028903"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Peizhe Yin","raw_affiliation_strings":["Faculty of Information Science and Engineering, Ocean University of China, Qingdao, China"],"affiliations":[{"raw_affiliation_string":"Faculty of Information Science and Engineering, Ocean University of China, Qingdao, China","institution_ids":["https://openalex.org/I59028903"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058652390","display_name":"Jie Nie","orcid":"https://orcid.org/0000-0003-4952-7666"},"institutions":[{"id":"https://openalex.org/I59028903","display_name":"Ocean University of China","ror":"https://ror.org/04rdtx186","country_code":"CN","type":"education","lineage":["https://openalex.org/I59028903"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Nie","raw_affiliation_strings":["Faculty of Information Science and Engineering, Ocean University of China, Qingdao, China"],"affiliations":[{"raw_affiliation_string":"Faculty of Information Science and Engineering, Ocean University of China, Qingdao, China","institution_ids":["https://openalex.org/I59028903"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069127252","display_name":"Xinyue Liang","orcid":"https://orcid.org/0000-0003-4406-536X"},"institutions":[{"id":"https://openalex.org/I59028903","display_name":"Ocean University of China","ror":"https://ror.org/04rdtx186","country_code":"CN","type":"education","lineage":["https://openalex.org/I59028903"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinyue Liang","raw_affiliation_strings":["Faculty of Information Science and Engineering, Ocean University of China, Qingdao, China"],"affiliations":[{"raw_affiliation_string":"Faculty of Information Science and Engineering, Ocean University of China, Qingdao, China","institution_ids":["https://openalex.org/I59028903"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032859417","display_name":"Shusong Yu","orcid":"https://orcid.org/0000-0003-1554-5313"},"institutions":[{"id":"https://openalex.org/I59028903","display_name":"Ocean University of China","ror":"https://ror.org/04rdtx186","country_code":"CN","type":"education","lineage":["https://openalex.org/I59028903"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shusong Yu","raw_affiliation_strings":["Faculty of Information Science and Engineering, Ocean University of China, Qingdao, China"],"affiliations":[{"raw_affiliation_string":"Faculty of Information Science and Engineering, Ocean University of China, Qingdao, China","institution_ids":["https://openalex.org/I59028903"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100367824","display_name":"Chenglong Wang","orcid":"https://orcid.org/0000-0003-2262-4167"},"institutions":[{"id":"https://openalex.org/I59028903","display_name":"Ocean University of China","ror":"https://ror.org/04rdtx186","country_code":"CN","type":"education","lineage":["https://openalex.org/I59028903"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenglong Wang","raw_affiliation_strings":["Faculty of Information Science and Engineering, Ocean University of China, Qingdao, China"],"affiliations":[{"raw_affiliation_string":"Faculty of Information Science and Engineering, Ocean University of China, Qingdao, China","institution_ids":["https://openalex.org/I59028903"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001185571","display_name":"Weizhi Nie","orcid":"https://orcid.org/0000-0002-0578-8138"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weizhi Nie","raw_affiliation_strings":["School of Electrical and Information Engineering, Tianjin University, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Information Engineering, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002424076","display_name":"Xiangqian Ding","orcid":null},"institutions":[{"id":"https://openalex.org/I59028903","display_name":"Ocean University of China","ror":"https://ror.org/04rdtx186","country_code":"CN","type":"education","lineage":["https://openalex.org/I59028903"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangqian Ding","raw_affiliation_strings":["Faculty of Information Science and Engineering, Ocean University of China, Qingdao, China"],"affiliations":[{"raw_affiliation_string":"Faculty of Information Science and Engineering, Ocean University of China, Qingdao, China","institution_ids":["https://openalex.org/I59028903"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5052432499"],"corresponding_institution_ids":["https://openalex.org/I59028903"],"apc_list":null,"apc_paid":null,"fwci":7.6803,"has_fulltext":false,"cited_by_count":42,"citation_normalized_percentile":{"value":0.97991191,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"72","issue":null,"first_page":"1","last_page":"13"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9940999746322632,"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"}},"topics":[{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9940999746322632,"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/T11062","display_name":"Gear and Bearing Dynamics Analysis","score":0.9782999753952026,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9103999733924866,"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/computer-science","display_name":"Computer science","score":0.7142478227615356},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6373118758201599},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5803053379058838},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5091885924339294},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.488369882106781},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4854348301887512},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4767276346683502},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3384069502353668},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3312166929244995}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7142478227615356},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6373118758201599},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5803053379058838},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5091885924339294},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.488369882106781},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4854348301887512},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4767276346683502},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3384069502353668},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3312166929244995}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tim.2023.3291768","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2023.3291768","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":[],"awards":[{"id":"https://openalex.org/G2570400918","display_name":null,"funder_award_id":"2020YFB1711700","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W1995094777","https://openalex.org/W2116341502","https://openalex.org/W2139906443","https://openalex.org/W2763269460","https://openalex.org/W2964015378","https://openalex.org/W2964051675","https://openalex.org/W2964321699","https://openalex.org/W2985882473","https://openalex.org/W2996977991","https://openalex.org/W3013328650","https://openalex.org/W3019762726","https://openalex.org/W3088477608","https://openalex.org/W3095770430","https://openalex.org/W3110510730","https://openalex.org/W3121022549","https://openalex.org/W3129806918","https://openalex.org/W3135695944","https://openalex.org/W3148438775","https://openalex.org/W3149374493","https://openalex.org/W3157123770","https://openalex.org/W3161559204","https://openalex.org/W3168417671","https://openalex.org/W3199798098","https://openalex.org/W3213302843","https://openalex.org/W3213543645","https://openalex.org/W4205165532","https://openalex.org/W4221101570","https://openalex.org/W4226070250","https://openalex.org/W4280589929","https://openalex.org/W4281490075","https://openalex.org/W4285114768","https://openalex.org/W4285818347","https://openalex.org/W4291910433","https://openalex.org/W4297733535","https://openalex.org/W4308485707","https://openalex.org/W6720006811","https://openalex.org/W6726873649","https://openalex.org/W6771832018","https://openalex.org/W6803811376","https://openalex.org/W6810634126","https://openalex.org/W6811238527","https://openalex.org/W6838913300"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4321487865","https://openalex.org/W4313906399","https://openalex.org/W4239306820","https://openalex.org/W4391266461","https://openalex.org/W2590798552","https://openalex.org/W2811106690","https://openalex.org/W2947043951","https://openalex.org/W4399188509","https://openalex.org/W2318112981"],"abstract_inverted_index":{"Fault":[0],"diagnosis":[1,50],"for":[2,117],"rolling":[3],"bearing":[4,17],"has":[5,52],"been":[6],"an":[7,213],"important":[8],"engineering":[9],"problem":[10],"through":[11],"decades.":[12],"To":[13],"detect":[14],"the":[15,21,24,29,71,74,100,133,147,200,235,245,249,254],"damaged":[16],"surface,":[18],"engineers":[19],"analyze":[20],"features":[22,94,134,151,183,207],"from":[23,95],"extracted":[25],"vibration":[26,136],"signals":[27,104,137],"of":[28,135,149,188,238],"machine.":[30],"As":[31],"artificial":[32],"intelligence":[33],"rapidly":[34],"develops":[35],"and":[36,172,211],"provides":[37],"favorable":[38],"effects":[39],"in":[40,57,69,84],"data":[41],"analytics,":[42],"using":[43],"deep":[44],"learning":[45],"technology":[46],"to":[47,224,252],"attack":[48],"fault":[49],"problems":[51],"attracted":[53],"increasing":[54],"research":[55],"interests":[56],"recent":[58],"years.":[59],"However":[60],"most":[61],"existing":[62],"methods":[63],"do":[64],"not":[65],"provide":[66],"satisfactory":[67],"performance":[68],"mining":[70,187],"relationship":[72,102],"between":[73,103],"signals.":[75],"Even":[76],"graph":[77,114,162,169,175,219,226],"convolutional":[78,115],"networks":[79],"that":[80,146,260],"can":[81,152,179,232],"perform":[82],"well":[83],"non-Euclidean":[85],"Spaces":[86],"have":[87],"limitations,":[88],"since":[89],"it":[90],"only":[91],"considers":[92],"extracting":[93],"a":[96,112,126,160,173,194,209],"single":[97],"scale,":[98],"ignoring":[99],"potential":[101],"at":[105,228],"various":[106,206],"scales.":[107],"For":[108],"salvation,":[109],"we":[110,123,158,243],"propose":[111,193],"multi-scale":[113,127,161],"network(MS-GCN)":[116],"this":[118],"specific":[119],"problem.":[120],"In":[121,156,241],"MS-GCN,":[122],"put":[124],"forward":[125],"feature":[128],"extraction":[129],"module,":[130,164,177],"which":[131,165,178,203,231],"extracts":[132],"two":[138,167],"times":[139],"under":[140],"diverse":[141],"receptive":[142],"field":[143],"ranges,":[144],"ensuring":[145],"regularities":[148],"signal":[150,239],"be":[153],"fully":[154,180],"discovered.":[155],"addition,":[157],"devise":[159],"iteration":[163,170,176],"incorporates":[166],"single-scale":[168],"modules":[171],"cross-scale":[174],"retain":[181],"local":[182],"based":[184,198],"on":[185,199,248],"extensive":[186],"global":[189],"information.":[190],"We":[191],"also":[192],"mutual":[195],"fusion":[196],"module":[197],"Bayesian":[201],"method,":[202],"forcibly":[204],"manipulates":[205],"as":[208],"prior":[210],"achieves":[212],"convincing":[214],"result.":[215],"The":[216,257],"horizontal":[217],"visibility":[218],"(HVG)":[220],"method":[221,255],"is":[222],"used":[223],"construct":[225],"models":[227],"multiple":[229],"scales,":[230],"better":[233],"capture":[234],"hidden":[236],"information":[237],"vibrations.":[240],"experiments,":[242],"verify":[244],"proposed":[246],"model":[247,262],"CWRU":[250],"dataset":[251],"evaluate":[253],"performance.":[256],"results":[258],"show":[259],"our":[261],"significantly":[263],"improved":[264],"accuracy":[265],"compared":[266],"with":[267],"other":[268],"state-of-the-art":[269],"methods.":[270]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":18},{"year":2024,"cited_by_count":21},{"year":2023,"cited_by_count":2}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}