{"id":"https://openalex.org/W7224833784","doi":"https://doi.org/10.21595/jve.2026.26815","title":"Channel-attention-based time-frequency dual-branch network for bearing fault diagnosis in warehouse logistics sorting machines","display_name":"Channel-attention-based time-frequency dual-branch network for bearing fault diagnosis in warehouse logistics sorting machines","publication_year":2026,"publication_date":"2026-10-30","ids":{"openalex":"https://openalex.org/W7224833784","doi":"https://doi.org/10.21595/jve.2026.26815"},"language":"en","primary_location":{"id":"doi:10.21595/jve.2026.26815","doi":"https://doi.org/10.21595/jve.2026.26815","is_oa":true,"landing_page_url":"https://doi.org/10.21595/jve.2026.26815","pdf_url":"https://www.extrica.com/article/26815/pdf","source":{"id":"https://openalex.org/S4607912","display_name":"Journal of Vibroengineering","issn_l":"1392-8716","issn":["1392-8716","2538-8460"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-d","jufo-1","poland-40","russia-white-list-3"],"host_organization":"https://openalex.org/P4324044484","host_organization_name":"JVE International","host_organization_lineage":["https://openalex.org/P4324044484"],"host_organization_lineage_names":["JVE International"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Vibroengineering","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.extrica.com/article/26815/pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5160173943","display_name":"Changhua Chen","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I102345215","display_name":"Xihua University","ror":"https://ror.org/04gwtvf26","country_code":"CN","type":"education","lineage":["https://openalex.org/I102345215"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Changhua Chen","raw_affiliation_strings":["Xihua University , Chengdu , 610039 , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xihua University , Chengdu , 610039 , China","institution_ids":["https://openalex.org/I102345215"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5160176747","display_name":"Xiangyang Deng","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I102345215","display_name":"Xihua University","ror":"https://ror.org/04gwtvf26","country_code":"CN","type":"education","lineage":["https://openalex.org/I102345215"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangyang Deng","raw_affiliation_strings":["Xihua University , Chengdu , 610039 , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xihua University , Chengdu , 610039 , China","institution_ids":["https://openalex.org/I102345215"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010267120","display_name":"Xinqi Wan","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I102345215","display_name":"Xihua University","ror":"https://ror.org/04gwtvf26","country_code":"CN","type":"education","lineage":["https://openalex.org/I102345215"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinqi Wan","raw_affiliation_strings":["Xihua University , Chengdu , 610039 , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xihua University , Chengdu , 610039 , China","institution_ids":["https://openalex.org/I102345215"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5160173943"],"corresponding_institution_ids":["https://openalex.org/I102345215"],"apc_list":{"value":1050,"currency":"EUR","value_usd":1132},"apc_paid":{"value":1050,"currency":"EUR","value_usd":1132},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.77360177,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":1.0,"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":1.0,"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":0.0,"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":0.0,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/bearing-fault-diagnosis","display_name":"bearing fault diagnosis","score":1.0},{"id":"https://openalex.org/keywords/channel-attention","display_name":"channel attention","score":0.8960000276565552},{"id":"https://openalex.org/keywords/continuous-wavelet-transform","display_name":"continuous wavelet transform","score":0.9070000052452087},{"id":"https://openalex.org/keywords/bearing-vibration-signals","display_name":"bearing vibration signals","score":0.800000011920929},{"id":"https://openalex.org/keywords/temporal-convolutional-network","display_name":"temporal convolutional network","score":0.8159999847412109},{"id":"https://openalex.org/keywords/multiscale-convolutional-neural-network","display_name":"multi-scale convolutional neural network","score":0.9290000200271606},{"id":"https://openalex.org/keywords/bearing-condition-monitoring","display_name":"bearing condition monitoring","score":0.7829999923706055},{"id":"https://openalex.org/keywords/inner-race-fault","display_name":"inner race fault","score":0.7269999980926514},{"id":"https://openalex.org/keywords/outer-race-fault","display_name":"outer race fault","score":0.9940000176429749},{"id":"https://openalex.org/keywords/rolling-element-fault","display_name":"rolling element fault","score":0.8740000128746033}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.800000011920929},{"id":"https://openalex.org/C111696304","wikidata":"https://www.wikidata.org/wiki/Q2303697","display_name":"Sorting","level":2,"score":0.6577000021934509},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5824000239372253},{"id":"https://openalex.org/C199978012","wikidata":"https://www.wikidata.org/wiki/Q1273815","display_name":"Bearing (navigation)","level":2,"score":0.5723999738693237},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5577999949455261},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.5512999892234802},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.5170000195503235},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5026999711990356},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49939998984336853},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.45730000734329224},{"id":"https://openalex.org/C198394728","wikidata":"https://www.wikidata.org/wiki/Q3695508","display_name":"Vibration","level":2,"score":0.44190001487731934},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.42579999566078186},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.41839998960494995},{"id":"https://openalex.org/C197424946","wikidata":"https://www.wikidata.org/wiki/Q1165717","display_name":"Waveform","level":3,"score":0.4129999876022339},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.41280001401901245},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.3752000033855438},{"id":"https://openalex.org/C102392041","wikidata":"https://www.wikidata.org/wiki/Q592860","display_name":"Sliding window protocol","level":3,"score":0.3621000051498413},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.3199999928474426},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.299699991941452},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.2921000123023987},{"id":"https://openalex.org/C2776895703","wikidata":"https://www.wikidata.org/wiki/Q6736207","display_name":"Main bearing","level":3,"score":0.2793999910354614},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.27320000529289246},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.2644999921321869},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.25870001316070557},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2558000087738037}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.21595/jve.2026.26815","doi":"https://doi.org/10.21595/jve.2026.26815","is_oa":true,"landing_page_url":"https://doi.org/10.21595/jve.2026.26815","pdf_url":"https://www.extrica.com/article/26815/pdf","source":{"id":"https://openalex.org/S4607912","display_name":"Journal of Vibroengineering","issn_l":"1392-8716","issn":["1392-8716","2538-8460"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-d","jufo-1","poland-40","russia-white-list-3"],"host_organization":"https://openalex.org/P4324044484","host_organization_name":"JVE International","host_organization_lineage":["https://openalex.org/P4324044484"],"host_organization_lineage_names":["JVE International"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Vibroengineering","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.21595/jve.2026.26815","doi":"https://doi.org/10.21595/jve.2026.26815","is_oa":true,"landing_page_url":"https://doi.org/10.21595/jve.2026.26815","pdf_url":"https://www.extrica.com/article/26815/pdf","source":{"id":"https://openalex.org/S4607912","display_name":"Journal of Vibroengineering","issn_l":"1392-8716","issn":["1392-8716","2538-8460"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-d","jufo-1","poland-40","russia-white-list-3"],"host_organization":"https://openalex.org/P4324044484","host_organization_name":"JVE International","host_organization_lineage":["https://openalex.org/P4324044484"],"host_organization_lineage_names":["JVE International"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Vibroengineering","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.5378999710083008}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7224833784.pdf","grobid_xml":"https://content.openalex.org/works/W7224833784.grobid-xml"},"referenced_works_count":15,"referenced_works":["https://openalex.org/W1964511482","https://openalex.org/W2031423206","https://openalex.org/W2060304859","https://openalex.org/W2584994008","https://openalex.org/W2752782242","https://openalex.org/W2790195878","https://openalex.org/W2810292802","https://openalex.org/W2884585870","https://openalex.org/W2998506103","https://openalex.org/W3154639332","https://openalex.org/W4403406972","https://openalex.org/W4411568838","https://openalex.org/W7124427170","https://openalex.org/W7129598829","https://openalex.org/W7160131419"],"related_works":["https://openalex.org/W7169666051","https://openalex.org/W4388866825","https://openalex.org/W4383988808","https://openalex.org/W7221192670","https://openalex.org/W4415294869","https://openalex.org/W4393140485","https://openalex.org/W7156317737","https://openalex.org/W7128534050","https://openalex.org/W7213363554","https://openalex.org/W4411802128"],"abstract_inverted_index":{"To":[0],"address":[1],"the":[2,91,99,141,148,156,160],"difficulty":[3],"of":[4,106,159],"fully":[5],"characterizing":[6],"both":[7,41],"local":[8,64],"impacts":[9],"and":[10,48,66,98,110,133,145,151,165],"time-frequency":[11,31,55,79],"information":[12],"in":[13,180],"bearing":[14,117,177],"vibration":[15,37,118],"signals":[16],"from":[17],"automated":[18],"logistics":[19,182],"sorting":[20],"equipment":[21],"using":[22,116],"a":[23,29,43,49,71],"single":[24],"representation,":[25],"this":[26],"study":[27],"develops":[28],"channel-attention-based":[30],"dual-branch":[32,161],"fault":[33],"diagnosis":[34],"model.":[35],"Each":[36],"window":[38],"is":[39,87],"represented":[40],"as":[42],"normalized":[44],"one-dimensional":[45],"time-series":[46],"input":[47],"two-dimensional":[50],"continuous":[51],"wavelet":[52],"transform":[53],"(CWT)":[54],"map.":[56],"A":[57],"temporal":[58,67],"convolutional":[59,73],"network":[60,75],"(TCN)":[61],"branch":[62,77],"extracts":[63],"waveform":[65],"variation":[68],"features,":[69,97],"while":[70],"multi-scale":[72,166],"neural":[74],"(CNN)":[76],"captures":[78],"textures":[80],"at":[81,121],"different":[82],"receptive":[83],"fields.":[84],"Channel":[85],"attention":[86],"introduced":[88],"separately":[89],"into":[90],"two":[92,100],"branches":[93],"to":[94,129],"reweight":[95],"informative":[96],"representations":[101],"are":[102,114],"concatenated":[103],"for":[104,176],"classification":[105],"normal,":[107],"inner-race,":[108],"outer-race,":[109],"rolling-element":[111],"conditions.":[112],"Experiments":[113],"conducted":[115],"records":[119],"collected":[120],"1200":[122],"r/min,":[123],"with":[124],"non-overlapping":[125],"time":[126],"windows":[127],"used":[128],"construct":[130],"training,":[131],"validation,":[132],"held-out":[134],"evaluation":[135],"subsets.":[136],"The":[137,169],"proposed":[138],"model":[139],"achieves":[140],"highest":[142],"mean":[143],"accuracy":[144],"Macro-F1":[146],"among":[147],"baselines":[149],"considered,":[150],"component":[152],"ablation":[153],"further":[154],"supports":[155],"combined":[157],"use":[158],"architecture,":[162],"channel":[163],"attention,":[164],"feature":[167],"extraction.":[168],"method":[170],"provides":[171],"an":[172],"experimentally":[173],"supported":[174],"reference":[175],"condition":[178],"monitoring":[179],"warehouse":[181],"equipment.":[183]},"counts_by_year":[],"updated_date":"2026-11-01T11:40:01.658920","created_date":"2026-10-31T00:00:00"}