{"id":"https://openalex.org/W1670629349","doi":"https://doi.org/10.1109/indin.2015.7281893","title":"Semi-supervised label consistent dictionary learning for machine fault classification","display_name":"Semi-supervised label consistent dictionary learning for machine fault classification","publication_year":2015,"publication_date":"2015-07-01","ids":{"openalex":"https://openalex.org/W1670629349","doi":"https://doi.org/10.1109/indin.2015.7281893","mag":"1670629349"},"language":"en","primary_location":{"id":"doi:10.1109/indin.2015.7281893","is_oa":false,"landing_page_url":"https://doi.org/10.1109/indin.2015.7281893","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE 13th International Conference on Industrial Informatics (INDIN)","raw_type":"proceedings-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/A5101193453","display_name":"Weiming Jiang","orcid":null},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Weiming Jiang","raw_affiliation_strings":["School of Computer Science and Technology, Soochow University, Suzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Soochow University, Suzhou, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100423029","display_name":"Zhao Zhang","orcid":"https://orcid.org/0000-0002-5703-7969"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhao Zhang","raw_affiliation_strings":["Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, China","School of Computer Science and Technology, Soochow University, Suzhou, China"],"affiliations":[{"raw_affiliation_string":"Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, China","institution_ids":[]},{"raw_affiliation_string":"School of Computer Science and Technology, Soochow University, Suzhou, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013713640","display_name":"Fanzhang Li","orcid":"https://orcid.org/0000-0003-4318-3081"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fanzhang Li","raw_affiliation_strings":["Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, China","School of Computer Science and Technology, Soochow University, Suzhou, China"],"affiliations":[{"raw_affiliation_string":"Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, China","institution_ids":[]},{"raw_affiliation_string":"School of Computer Science and Technology, Soochow University, Suzhou, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100425448","display_name":"Li Zhang","orcid":"https://orcid.org/0000-0001-7914-0679"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Zhang","raw_affiliation_strings":["Soochow University, Suzhou, Jiangsu, CN"],"affiliations":[{"raw_affiliation_string":"Soochow University, Suzhou, Jiangsu, CN","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061195038","display_name":"Mingbo Zhao","orcid":"https://orcid.org/0000-0003-0381-4360"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Mingbo Zhao","raw_affiliation_strings":["Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong"],"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong","institution_ids":["https://openalex.org/I168719708"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101193453"],"corresponding_institution_ids":["https://openalex.org/I3923682"],"apc_list":null,"apc_paid":null,"fwci":0.6498,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.68208905,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1124","last_page":"1129"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9945999979972839,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9945999979972839,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9914000034332275,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9891999959945679,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7333075404167175},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7025858163833618},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.5953772068023682},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5200985670089722},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.4655474126338959},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.45133429765701294},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4145018756389618}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7333075404167175},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7025858163833618},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.5953772068023682},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5200985670089722},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.4655474126338959},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.45133429765701294},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4145018756389618},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/indin.2015.7281893","is_oa":false,"landing_page_url":"https://doi.org/10.1109/indin.2015.7281893","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE 13th International Conference on Industrial Informatics (INDIN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7099999785423279}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W639205600","https://openalex.org/W1529156551","https://openalex.org/W1551219034","https://openalex.org/W1963932623","https://openalex.org/W1967103180","https://openalex.org/W1988978972","https://openalex.org/W1991572937","https://openalex.org/W2027805700","https://openalex.org/W2027922120","https://openalex.org/W2053186076","https://openalex.org/W2053429611","https://openalex.org/W2077695545","https://openalex.org/W2082855665","https://openalex.org/W2113268027","https://openalex.org/W2127271355","https://openalex.org/W2129812935","https://openalex.org/W2130951397","https://openalex.org/W2132709984","https://openalex.org/W2133858838","https://openalex.org/W2153663612","https://openalex.org/W2160547390","https://openalex.org/W2163112044","https://openalex.org/W2172174689","https://openalex.org/W4245176872","https://openalex.org/W6620727959","https://openalex.org/W6679570432","https://openalex.org/W6684753728"],"related_works":["https://openalex.org/W2965546495","https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W259157601","https://openalex.org/W4205463238","https://openalex.org/W2110523656","https://openalex.org/W1482209366","https://openalex.org/W2404514746","https://openalex.org/W1652783584"],"abstract_inverted_index":{"In":[0],"this":[1,103],"paper,":[2],"we":[3,105],"mainly":[4],"present":[5,106],"a":[6,45,62,114,122],"Semi-Supervised":[7],"Label":[8],"Consistent":[9],"KSVD":[10,31,111],"(S":[11],"<sup":[12,28,108],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[13,29,109],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</sup>":[14,30,110],"KSVD)":[15],"algorithm":[16,164],"for":[17,168],"representing":[18],"and":[19,49],"classifying":[20],"machine":[21,158,169],"faults.":[22],"The":[23,125],"formulation":[24],"of":[25,56,94,138],"our":[26,163],"S":[27,107],"is":[32,44,128],"an":[33],"improvement":[34],"to":[35,51,60,73,129,134],"the":[36,88,92,131,136],"recent":[37],"label":[38,117],"consistent":[39],"K-SVD":[40],"(LC-KSVD),":[41],"because":[42],"LC-KSVD":[43,95],"fully":[46],"supervised":[47,53,143],"approach,":[48],"needs":[50],"use":[52],"class":[54],"information":[55],"all":[57],"training":[58],"data":[59],"compute":[61],"reconstructive":[63],"&":[64],"discriminative":[65],"dictionary.":[66],"But":[67],"labeled":[68],"signals":[69,79,140],"are":[70],"often":[71],"expensive":[72],"obtain,":[74],"while":[75],"in":[76,99],"contrast":[77],"unlabeled":[78,139],"can":[80,147,151],"be":[81,97,152],"easily":[82],"captured":[83],"with":[84],"low":[85],"expense":[86],"from":[87],"real":[89],"world.":[90],"Thus,":[91],"application":[93],"may":[96],"constrained":[98],"reality.":[100],"To":[101],"address":[102],"problem,":[104],"through":[112],"involving":[113],"computationally":[115],"efficient":[116],"propagation":[118],"(LP)":[119],"process":[120,133],"as":[121],"preprocessing":[123],"step.":[124],"core":[126],"idea":[127],"employ":[130],"LP":[132],"estimate":[135],"labels":[137],"so":[141],"that":[142,146,162],"prior":[144],"knowledge":[145],"significantly":[148],"enhance":[149],"classification":[150],"increased.":[153],"Simulation":[154],"results":[155],"on":[156],"several":[157],"fault":[159,170],"datasets":[160],"demonstrate":[161],"delivers":[165],"promising":[166],"performance":[167],"classification.":[171]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2015,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}