{"id":"https://openalex.org/W4415883193","doi":"https://doi.org/10.1109/jiot.2025.3628852","title":"Interpretable Convolutional Sparse Modal Unrolling Network for Bearing Fault Diagnosis Across Unseen Time-Varying Working Conditions","display_name":"Interpretable Convolutional Sparse Modal Unrolling Network for Bearing Fault Diagnosis Across Unseen Time-Varying Working Conditions","publication_year":2025,"publication_date":"2025-11-04","ids":{"openalex":"https://openalex.org/W4415883193","doi":"https://doi.org/10.1109/jiot.2025.3628852"},"language":null,"primary_location":{"id":"doi:10.1109/jiot.2025.3628852","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2025.3628852","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"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 Internet of Things Journal","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/A5100884847","display_name":"Zheng Yuan","orcid":"https://orcid.org/0009-0001-8639-6522"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuan Zheng","raw_affiliation_strings":["School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China"],"raw_orcid":"https://orcid.org/0009-0001-8639-6522","affiliations":[{"raw_affiliation_string":"School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063044163","display_name":"Guolin He","orcid":"https://orcid.org/0000-0002-6313-0078"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guolin He","raw_affiliation_strings":["School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-6313-0078","affiliations":[{"raw_affiliation_string":"School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052037779","display_name":"Wei Feng","orcid":"https://orcid.org/0000-0002-9845-999X"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210145761","display_name":"Shenzhen Institutes of Advanced Technology","ror":"https://ror.org/04gh4er46","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210145761"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Feng","raw_affiliation_strings":["Chinese Academy of Sciences, Shenzhen Institute of Advanced Technology, Shenzhen, China","Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0002-9845-999X","affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences, Shenzhen Institute of Advanced Technology, Shenzhen, China","institution_ids":["https://openalex.org/I4210145761","https://openalex.org/I19820366"]},{"raw_affiliation_string":"Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China","institution_ids":["https://openalex.org/I4210145761","https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010684849","display_name":"Weihua Li","orcid":"https://orcid.org/0000-0002-7493-1399"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weihua Li","raw_affiliation_strings":["School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-7493-1399","affiliations":[{"raw_affiliation_string":"School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.35998094,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":"2","first_page":"2302","last_page":"2316"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9943000078201294,"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":0.9943000078201294,"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/T10534","display_name":"Structural Health Monitoring Techniques","score":0.0010000000474974513,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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":0.00039999998989515007,"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/discriminative-model","display_name":"Discriminative model","score":0.7364000082015991},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6123999953269958},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5292999744415283},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.48330000042915344},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.44670000672340393},{"id":"https://openalex.org/keywords/traffic-sign-recognition","display_name":"Traffic sign recognition","score":0.40299999713897705},{"id":"https://openalex.org/keywords/modal","display_name":"Modal","score":0.3955000042915344},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.357699990272522}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7670000195503235},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7364000082015991},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6304000020027161},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6123999953269958},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5292999744415283},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.48330000042915344},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.44670000672340393},{"id":"https://openalex.org/C6528762","wikidata":"https://www.wikidata.org/wiki/Q1574298","display_name":"Traffic sign recognition","level":4,"score":0.40299999713897705},{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.3955000042915344},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.357699990272522},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3522999882698059},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.35109999775886536},{"id":"https://openalex.org/C77637269","wikidata":"https://www.wikidata.org/wiki/Q7002051","display_name":"Neural coding","level":2,"score":0.3447999954223633},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.3361999988555908},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.32519999146461487},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32269999384880066},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.30090001225471497},{"id":"https://openalex.org/C76970557","wikidata":"https://www.wikidata.org/wiki/Q1869750","display_name":"Loop unrolling","level":3,"score":0.2978000044822693},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.28839999437332153},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.27320000529289246},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26980000734329224},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.265500009059906},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2556999921798706}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jiot.2025.3628852","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2025.3628852","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"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 Internet of Things Journal","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2507281339","display_name":null,"funder_award_id":"52575112","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3920565250","display_name":null,"funder_award_id":"2024YFB4709200","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G5209087489","display_name":null,"funder_award_id":"2025A1515011145","funder_id":"https://openalex.org/F4320337111","funder_display_name":"Basic and Applied Basic Research Foundation of Guangdong Province"},{"id":"https://openalex.org/G5586220171","display_name":null,"funder_award_id":"2025A1515011145","funder_id":"https://openalex.org/F4320320671","funder_display_name":"National Research Foundation"},{"id":"https://openalex.org/G8837241273","display_name":null,"funder_award_id":"52275111","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null},{"id":"https://openalex.org/F4320337111","display_name":"Basic and Applied Basic Research Foundation of Guangdong Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W3133902371","https://openalex.org/W3209651137","https://openalex.org/W3214433557","https://openalex.org/W3215227219","https://openalex.org/W4210940432","https://openalex.org/W4309164266","https://openalex.org/W4324290713","https://openalex.org/W4377235413","https://openalex.org/W4381059913","https://openalex.org/W4386883054","https://openalex.org/W4388450989","https://openalex.org/W4388505528","https://openalex.org/W4388839491","https://openalex.org/W4390575651","https://openalex.org/W4391312545","https://openalex.org/W4391731444","https://openalex.org/W4391830532","https://openalex.org/W4394699109","https://openalex.org/W4394904081","https://openalex.org/W4399295412","https://openalex.org/W4399337167","https://openalex.org/W4400727031","https://openalex.org/W4401732332","https://openalex.org/W4403789392","https://openalex.org/W4403863647","https://openalex.org/W4404238616","https://openalex.org/W4404563153","https://openalex.org/W4404735122","https://openalex.org/W4404953247","https://openalex.org/W4405100229","https://openalex.org/W4405219730","https://openalex.org/W4406259158","https://openalex.org/W4406754035","https://openalex.org/W4407129082","https://openalex.org/W4407565754","https://openalex.org/W4408017423","https://openalex.org/W4408259257","https://openalex.org/W4408867736","https://openalex.org/W4409380261","https://openalex.org/W4409494836","https://openalex.org/W4409643096","https://openalex.org/W4409901552","https://openalex.org/W4410086612","https://openalex.org/W4410342714","https://openalex.org/W4410525564","https://openalex.org/W4412030455","https://openalex.org/W6884631086"],"related_works":[],"abstract_inverted_index":{"Bearings":[0],"frequently":[1],"operate":[2],"under":[3,109,165],"time-varying":[4,69,81,114,167],"working":[5,70,121],"conditions":[6],"where":[7],"speed":[8,168],"profiles":[9],"may":[10],"be":[11],"unseen":[12,68,120,166],"for":[13,26,63,105,154],"intelligent":[14],"diagnostic":[15,214],"models.":[16],"Frontier":[17],"research":[18],"succeeds":[19],"in":[20,39,50,158],"extracting":[21],"discriminative":[22,178],"and":[23,32,42,91,171,192],"invariant":[24],"features":[25],"accurate":[27,155],"diagnosis.":[28],"However,":[29],"their":[30],"extraction":[31],"generalization":[33],"mechanisms":[34],"lack":[35],"physical":[36],"interpretations,":[37],"resulting":[38],"dubious":[40],"generalizability":[41],"high":[43],"data":[44],"dependency.":[45],"To":[46,117],"tackle":[47],"the":[48,176,208],"challenges":[49],"practical":[51],"scenarios,":[52],"this":[53],"study":[54],"incorporates":[55],"fault":[56,65,132],"mechanism,":[57],"proposing":[58],"a":[59,84,110],"fully":[60],"interpretable":[61,80,124],"network":[62,77],"credible":[64],"diagnosis":[66],"across":[67,119],"conditions,":[71,122],"called":[72],"convolutional":[73,103,194],"sparse":[74,86,106],"modal":[75,100],"unrolling":[76],"(CSMUNet).":[78],"For":[79],"feature":[82,179],"extraction,":[83,180],"novel":[85],"coding":[87],"algorithm":[88,95,98],"is":[89,127,136,163,205],"conceived":[90,128],"forms":[92],"CSMUNet":[93,204],"via":[94],"unrolling.":[96],"The":[97,134,160,201],"utilizes":[99],"response":[101],"as":[102],"dictionary":[104],"vector":[107],"optimization,":[108],"masking":[111],"constraint":[112],"of":[113,142,147,203],"impulsive":[115,131],"moments.":[116],"generalize":[118],"an":[123],"domain-invariant":[125,209],"representation":[126],"based":[129],"on":[130],"mechanism.":[133],"input":[135],"divided":[137],"by":[138],"equiangular":[139],"span":[140],"instead":[141],"time":[143],"length,":[144],"enabling":[145],"impulses":[146],"different":[148],"samples":[149],"to":[150,185],"have":[151],"constant":[152],"numbers":[153],"moment":[156],"recognition":[157],"CSMUNet.":[159],"proposed":[161],"model":[162],"tested":[164],"with":[169,181,207],"simulation":[170],"experiment":[172],"data.":[173],"Results":[174],"demonstrate":[175],"superior":[177],"key":[182],"metrics":[183],"rising":[184],"3.2":[186],"times":[187],"higher":[188],"than":[189],"raw":[190],"inputs":[191],"classical":[193],"models":[195],"reaching":[196],"over":[197],"35%":[198],"accuracy":[199],"improvement.":[200],"performance":[202],"interpreted":[206],"representation,":[210],"which":[211],"enhances":[212],"its":[213],"credibility.":[215]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-11-04T00:00:00"}
