{"id":"https://openalex.org/W4404238616","doi":"https://doi.org/10.1109/tnnls.2024.3483954","title":"Matching Pursuit Network: An Interpretable Sparse Time-Frequency Representation Method Toward Mechanical Fault Diagnosis","display_name":"Matching Pursuit Network: An Interpretable Sparse Time-Frequency Representation Method Toward Mechanical Fault Diagnosis","publication_year":2024,"publication_date":"2024-11-11","ids":{"openalex":"https://openalex.org/W4404238616","doi":"https://doi.org/10.1109/tnnls.2024.3483954","pmid":"https://pubmed.ncbi.nlm.nih.gov/39527434"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2024.3483954","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2024.3483954","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"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 Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5074854788","display_name":"Huibin Lin","orcid":null},"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":"Huibin Lin","raw_affiliation_strings":["School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-5952-9003","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/A5006713946","display_name":"Xiaofeng Huang","orcid":"https://orcid.org/0000-0001-8571-4667"},"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":"Xiaofeng Huang","raw_affiliation_strings":["School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China"],"raw_orcid":null,"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/A5102729596","display_name":"Zhuyun Chen","orcid":"https://orcid.org/0000-0002-6100-7332"},"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":"Zhuyun Chen","raw_affiliation_strings":["School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-6100-7332","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":null,"display_name":"Ciyang Xi","orcid":"https://orcid.org/0009-0009-5040-5990"},"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":"Ciyang Xi","raw_affiliation_strings":["School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China"],"raw_orcid":"https://orcid.org/0009-0009-5040-5990","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":"last","author":{"id":null,"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":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I90610280"],"apc_list":null,"apc_paid":null,"fwci":18.1826,"has_fulltext":false,"cited_by_count":64,"citation_normalized_percentile":{"value":0.99658764,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"36","issue":"7","first_page":"12377","last_page":"12388"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9498000144958496,"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":0.9498000144958496,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9488999843597412,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9168000221252441,"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/matching-pursuit","display_name":"Matching pursuit","score":0.9076480865478516},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.6486899852752686},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.6157383322715759},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5661176443099976},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5380309224128723},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.507536768913269},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4965277314186096},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.49525395035743713},{"id":"https://openalex.org/keywords/time\u2013frequency-analysis","display_name":"Time\u2013frequency analysis","score":0.49210232496261597},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3285142779350281},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2781212329864502},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.21906524896621704},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.17299887537956238},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.13947007060050964},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.13768038153648376},{"id":"https://openalex.org/keywords/seismology","display_name":"Seismology","score":0.08691206574440002},{"id":"https://openalex.org/keywords/political-science","display_name":"Political science","score":0.057987987995147705}],"concepts":[{"id":"https://openalex.org/C156872377","wikidata":"https://www.wikidata.org/wiki/Q6786281","display_name":"Matching pursuit","level":3,"score":0.9076480865478516},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6486899852752686},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.6157383322715759},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5661176443099976},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5380309224128723},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.507536768913269},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4965277314186096},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49525395035743713},{"id":"https://openalex.org/C142433447","wikidata":"https://www.wikidata.org/wiki/Q7806653","display_name":"Time\u2013frequency analysis","level":3,"score":0.49210232496261597},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3285142779350281},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2781212329864502},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.21906524896621704},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.17299887537956238},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.13947007060050964},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.13768038153648376},{"id":"https://openalex.org/C165205528","wikidata":"https://www.wikidata.org/wiki/Q83371","display_name":"Seismology","level":1,"score":0.08691206574440002},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.057987987995147705},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnnls.2024.3483954","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2024.3483954","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"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 Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:39527434","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/39527434","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":"IEEE transactions on neural networks and learning systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1416275298","display_name":null,"funder_award_id":"2023A1515240021","funder_id":"https://openalex.org/F4320337111","funder_display_name":"Basic and Applied Basic Research Foundation of Guangdong Province"},{"id":"https://openalex.org/G3302854289","display_name":null,"funder_award_id":"2022A1515011238","funder_id":"https://openalex.org/F4320337111","funder_display_name":"Basic and Applied Basic Research Foundation of Guangdong Province"},{"id":"https://openalex.org/G5055887590","display_name":null,"funder_award_id":"51875207","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5927682106","display_name":null,"funder_award_id":"52205101","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6206169322","display_name":null,"funder_award_id":"2023A1515240021","funder_id":"https://openalex.org/F4320320671","funder_display_name":"National Research Foundation"},{"id":"https://openalex.org/G6606386783","display_name":null,"funder_award_id":"2022A1515011238","funder_id":"https://openalex.org/F4320320671","funder_display_name":"National Research Foundation"},{"id":"https://openalex.org/G8264242158","display_name":null,"funder_award_id":"52475102","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/F4320337111","display_name":"Basic and Applied Basic Research Foundation of Guangdong Province","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W2020351623","https://openalex.org/W2030252539","https://openalex.org/W2100620757","https://openalex.org/W2126133656","https://openalex.org/W2132085292","https://openalex.org/W2133665775","https://openalex.org/W2151693816","https://openalex.org/W2163630665","https://openalex.org/W2592939477","https://openalex.org/W2744986420","https://openalex.org/W2769073591","https://openalex.org/W2802633978","https://openalex.org/W2804568838","https://openalex.org/W2964597528","https://openalex.org/W2999653953","https://openalex.org/W3103574138","https://openalex.org/W3106793101","https://openalex.org/W3122347867","https://openalex.org/W4200312990","https://openalex.org/W4220815429","https://openalex.org/W4283800329","https://openalex.org/W4284889614","https://openalex.org/W4292387424","https://openalex.org/W4294982764","https://openalex.org/W4312909545","https://openalex.org/W4324085214","https://openalex.org/W4324290713","https://openalex.org/W4360770901","https://openalex.org/W4366773120","https://openalex.org/W4379382293","https://openalex.org/W4379930959","https://openalex.org/W4380451824","https://openalex.org/W4381059913","https://openalex.org/W4384819582","https://openalex.org/W4387619006","https://openalex.org/W4391468021","https://openalex.org/W4391616987","https://openalex.org/W4392901727","https://openalex.org/W4395689618"],"related_works":["https://openalex.org/W2011611369","https://openalex.org/W4297791310","https://openalex.org/W4372266926","https://openalex.org/W4245251483","https://openalex.org/W1998873033","https://openalex.org/W2116148865","https://openalex.org/W1981874526","https://openalex.org/W2366556501","https://openalex.org/W2965458591","https://openalex.org/W1520740474"],"abstract_inverted_index":{"Rotatory":[0],"machinery":[1,46],"commonly":[2],"operates":[3],"in":[4,45,139],"complex":[5],"environments":[6],"with":[7,54,83,127],"strong":[8],"noise":[9],"and":[10,22,41,149,161,175,179],"variable":[11,49],"working":[12],"conditions.":[13],"Time-frequency":[14],"representation":[15,64,185],"offers":[16],"a":[17,52,60,79],"valuable":[18],"method":[19],"for":[20,30,74,158],"capturing":[21],"analyzing":[23],"nonstationary":[24],"characteristics,":[25],"making":[26],"it":[27],"particularly":[28],"suitable":[29],"identifying":[31],"transient":[32],"fault-related":[33],"features.":[34],"However,":[35],"despite":[36],"these":[37],"advantages,":[38],"extracting":[39],"robust":[40,174],"interpretable":[42,91,176],"fault":[43,76,146,151],"features":[44,100,178],"operating":[47],"under":[48],"speeds":[50],"remains":[51],"challenge":[53],"existing":[55],"techniques.":[56],"In":[57],"this":[58],"article,":[59],"novel":[61],"sparse":[62],"time-frequency":[63,102,177,184],"(STFR)":[65],"method,":[66],"named":[67],"matching":[68,92],"pursuit":[69,93],"network":[70,81],"(MPNet)":[71],"is":[72,87,131],"proposed":[73,169],"mechanical":[75],"diagnosis.":[77],"First,":[78],"deep":[80],"structure":[82],"signal":[84,111],"decomposition":[85],"capability":[86],"constructed":[88],"by":[89],"well-defined":[90],"(MP)":[94],"units":[95],"to":[96,112,119,133],"automatically":[97],"learn":[98],"discriminative":[99],"from":[101,153],"inputs.":[103],"Then,":[104],"the":[105,114,124,135,168,182],"weights":[106],"of":[107],"each":[108],"effective":[109],"component":[110],"reconstruct":[113],"raw":[115],"input":[116],"are":[117,156],"designed":[118],"measure":[120],"their":[121],"contributions.":[122],"Accordingly,":[123],"optimization":[125],"criterion":[126],"structural":[128],"similarity":[129],"metric":[130],"produced":[132],"realize":[134],"model":[136,159],"parameter":[137],"update":[138],"an":[140],"end-to-end":[141],"manner.":[142],"Finally,":[143],"phenomenological":[144],"model-based":[145],"simulation":[147],"signals":[148,152],"real":[150],"gearbox":[154],"experiments":[155],"used":[157],"training":[160],"testing,":[162],"respectively.":[163],"The":[164],"results":[165],"show":[166],"that":[167],"approach":[170],"can":[171],"well":[172],"extract":[173],"obviously":[180],"outperforms":[181],"state-of-the-art":[183],"methods.":[186]},"counts_by_year":[{"year":2026,"cited_by_count":24},{"year":2025,"cited_by_count":35},{"year":2024,"cited_by_count":5}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}