{"id":"https://openalex.org/W4395035061","doi":"https://doi.org/10.3390/s24092674","title":"A New Method for Bearing Fault Diagnosis across Machines Based on Envelope Spectrum and Conditional Metric Learning","display_name":"A New Method for Bearing Fault Diagnosis across Machines Based on Envelope Spectrum and Conditional Metric Learning","publication_year":2024,"publication_date":"2024-04-23","ids":{"openalex":"https://openalex.org/W4395035061","doi":"https://doi.org/10.3390/s24092674","pmid":"https://pubmed.ncbi.nlm.nih.gov/38732779"},"language":"en","primary_location":{"id":"doi:10.3390/s24092674","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s24092674","pdf_url":"https://www.mdpi.com/1424-8220/24/9/2674/pdf?version=1713874706","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"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":"Sensors","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1424-8220/24/9/2674/pdf?version=1713874706","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5017279981","display_name":"Xu Yang","orcid":"https://orcid.org/0000-0002-9891-5967"},"institutions":[{"id":"https://openalex.org/I4210115515","display_name":"Nanyang Institute of Technology","ror":"https://ror.org/0203c2755","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210115515"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xu Yang","raw_affiliation_strings":["School of Intelligent Manufacturing, Nanyang Institute of Technology, Nanyang 473004, China"],"raw_orcid":"https://orcid.org/0000-0002-9891-5967","affiliations":[{"raw_affiliation_string":"School of Intelligent Manufacturing, Nanyang Institute of Technology, Nanyang 473004, China","institution_ids":["https://openalex.org/I4210115515"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101783317","display_name":"Junfeng Yang","orcid":"https://orcid.org/0000-0002-1768-5603"},"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":true,"raw_author_name":"Junfeng Yang","raw_affiliation_strings":["School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063477447","display_name":"Yupeng Jin","orcid":null},"institutions":[{"id":"https://openalex.org/I129604602","display_name":"The University of Sydney","ror":"https://ror.org/0384j8v12","country_code":"AU","type":"education","lineage":["https://openalex.org/I129604602"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Yupeng Jin","raw_affiliation_strings":["School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, Sydney, NSW 2050, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, Sydney, NSW 2050, Australia","institution_ids":["https://openalex.org/I129604602"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011800383","display_name":"Zhongchao Liu","orcid":"https://orcid.org/0000-0002-6958-5292"},"institutions":[{"id":"https://openalex.org/I4210115515","display_name":"Nanyang Institute of Technology","ror":"https://ror.org/0203c2755","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210115515"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhongchao Liu","raw_affiliation_strings":["School of Intelligent Manufacturing, Nanyang Institute of Technology, Nanyang 473004, China"],"raw_orcid":"https://orcid.org/0000-0002-6958-5292","affiliations":[{"raw_affiliation_string":"School of Intelligent Manufacturing, Nanyang Institute of Technology, Nanyang 473004, China","institution_ids":["https://openalex.org/I4210115515"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101783317"],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":{"value":2400,"currency":"CHF","value_usd":2673},"apc_paid":{"value":2400,"currency":"CHF","value_usd":2673},"fwci":2.6449,"has_fulltext":true,"cited_by_count":14,"citation_normalized_percentile":{"value":0.90163934,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"24","issue":"9","first_page":"2674","last_page":"2674"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9995999932289124,"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":0.9995999932289124,"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":0.9922999739646912,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9549000263214111,"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/discriminative-model","display_name":"Discriminative model","score":0.8127057552337646},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6691393852233887},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6312611699104309},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6165447235107422},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.5818275809288025},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.5737735629081726},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5292467474937439},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.49496668577194214},{"id":"https://openalex.org/keywords/conditional-probability-distribution","display_name":"Conditional probability distribution","score":0.48628222942352295},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47802528738975525},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.46623867750167847},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4501186013221741},{"id":"https://openalex.org/keywords/envelope","display_name":"Envelope (radar)","score":0.443266898393631},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4207629859447479},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.2309800684452057},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10344982147216797}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8127057552337646},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6691393852233887},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6312611699104309},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6165447235107422},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.5818275809288025},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.5737735629081726},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5292467474937439},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.49496668577194214},{"id":"https://openalex.org/C43555835","wikidata":"https://www.wikidata.org/wiki/Q2300258","display_name":"Conditional probability distribution","level":2,"score":0.48628222942352295},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47802528738975525},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.46623867750167847},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4501186013221741},{"id":"https://openalex.org/C65155139","wikidata":"https://www.wikidata.org/wiki/Q5380912","display_name":"Envelope (radar)","level":3,"score":0.443266898393631},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4207629859447479},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2309800684452057},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10344982147216797},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.0},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","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/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"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/C165205528","wikidata":"https://www.wikidata.org/wiki/Q83371","display_name":"Seismology","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.3390/s24092674","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s24092674","pdf_url":"https://www.mdpi.com/1424-8220/24/9/2674/pdf?version=1713874706","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"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":"Sensors","raw_type":"journal-article"},{"id":"pmid:38732779","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/38732779","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:11085194","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/11085194","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11085194/pdf/sensors-24-02674.pdf","source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"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":"Sensors (Basel)","raw_type":"Text"},{"id":"pmh:oai:doaj.org/article:3a7aedea7ac44a6d9a0560aa054ebc73","is_oa":true,"landing_page_url":"https://doaj.org/article/3a7aedea7ac44a6d9a0560aa054ebc73","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors, Vol 24, Iss 9, p 2674 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/s24092674","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s24092674","pdf_url":"https://www.mdpi.com/1424-8220/24/9/2674/pdf?version=1713874706","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"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":"Sensors","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7300000190734863}],"awards":[{"id":"https://openalex.org/G1230367457","display_name":null,"funder_award_id":"2023JBZX006","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G588690372","display_name":null,"funder_award_id":"2022YJS155","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G6448534195","display_name":null,"funder_award_id":"242102241053","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"}],"funders":[{"id":"https://openalex.org/F4320329689","display_name":"Nanyang Institute of Technology","ror":"https://ror.org/0203c2755"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4395035061.pdf"},"referenced_works_count":31,"referenced_works":["https://openalex.org/W1980545827","https://openalex.org/W2002053764","https://openalex.org/W2033800551","https://openalex.org/W2146641075","https://openalex.org/W2775523681","https://openalex.org/W2794869810","https://openalex.org/W2891319189","https://openalex.org/W2898375427","https://openalex.org/W2904218127","https://openalex.org/W2921133554","https://openalex.org/W2945520991","https://openalex.org/W2965875348","https://openalex.org/W3009370740","https://openalex.org/W3031466690","https://openalex.org/W3092600489","https://openalex.org/W3126227157","https://openalex.org/W3134397144","https://openalex.org/W3157039246","https://openalex.org/W3167996502","https://openalex.org/W3176663948","https://openalex.org/W3178546485","https://openalex.org/W3198038800","https://openalex.org/W3199240287","https://openalex.org/W3209651137","https://openalex.org/W4200212283","https://openalex.org/W4206495689","https://openalex.org/W4213359304","https://openalex.org/W4285146412","https://openalex.org/W4285245107","https://openalex.org/W4286373773","https://openalex.org/W4301394837"],"related_works":["https://openalex.org/W2965546495","https://openalex.org/W2180954594","https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W2052835778","https://openalex.org/W259157601","https://openalex.org/W4205463238","https://openalex.org/W2049003611","https://openalex.org/W2127804977"],"abstract_inverted_index":{"In":[0,67],"recent":[1],"years,":[2],"most":[3,47],"research":[4],"on":[5,78,116,160,181],"bearing":[6,73],"fault":[7,49,74],"diagnosis":[8,50,75],"has":[9],"assumed":[10],"that":[11,173],"the":[12,21,53,57,62,65,100,104,122,131,145,149,153,174,178],"source":[13,132],"domain":[14,17,40,94,135],"and":[15,81,126,133,151,164],"target":[16,134],"data":[18,123],"come":[19],"from":[20,103,130],"same":[22],"machine.":[23],"The":[24,170],"differences":[25,125],"in":[26,32],"equipment":[27],"lead":[28],"to":[29,91,97,120,143],"a":[30,72,109],"decrease":[31],"diagnostic":[33],"accuracy.":[34],"To":[35],"address":[36],"this":[37,68],"issue,":[38],"unsupervised":[39],"adaptation":[41],"techniques":[42],"have":[43],"been":[44],"introduced.":[45],"However,":[46],"cross-device":[48,162],"models":[51],"overlook":[52],"discriminative":[54,101],"information":[55,102],"under":[56],"marginal":[58],"distribution,":[59,106],"which":[60],"restricts":[61],"performance":[63,147,180],"of":[64,148],"models.":[66],"paper,":[69],"we":[70,107],"propose":[71],"method":[76,176],"based":[77,115],"envelope":[79,86],"spectrum":[80],"conditional":[82,117],"metric":[83,118],"learning.":[84],"First,":[85],"spectral":[87],"analysis":[88,157],"is":[89,158],"used":[90],"extract":[92,127],"frequency":[93],"features.":[95],"Then,":[96],"fully":[98],"utilize":[99],"label":[105],"construct":[108],"deep":[110],"Siamese":[111],"convolutional":[112],"neural":[113],"network":[114],"learning":[119],"eliminate":[121],"distribution":[124],"common":[128],"features":[129],"data.":[136],"Finally,":[137],"dynamic":[138],"weighting":[139],"factors":[140],"are":[141],"employed":[142],"improve":[144],"convergence":[146],"model":[150],"optimize":[152],"training":[154],"process.":[155],"Experimental":[156],"conducted":[159],"12":[161],"tasks":[163],"compared":[165],"with":[166],"other":[167],"relevant":[168],"methods.":[169],"results":[171],"show":[172],"proposed":[175],"achieves":[177],"best":[179],"all":[182],"three":[183],"evaluation":[184],"metrics.":[185]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}
