{"id":"https://openalex.org/W4367669677","doi":"https://doi.org/10.1115/1.4062453","title":"Combinational Framework for Classification of Bearing Faults in Rotating Machines","display_name":"Combinational Framework for Classification of Bearing Faults in Rotating Machines","publication_year":2023,"publication_date":"2023-05-03","ids":{"openalex":"https://openalex.org/W4367669677","doi":"https://doi.org/10.1115/1.4062453"},"language":"en","primary_location":{"id":"doi:10.1115/1.4062453","is_oa":false,"landing_page_url":"https://doi.org/10.1115/1.4062453","pdf_url":null,"source":{"id":"https://openalex.org/S173178594","display_name":"Journal of Computing and Information Science in Engineering","issn_l":"1530-9827","issn":["1530-9827","1944-7078"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310316053","host_organization_name":"ASM International","host_organization_lineage":["https://openalex.org/P4310316053"],"host_organization_lineage_names":["ASM International"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computing and Information Science in Engineering","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/A5061973550","display_name":"Sujit Kumar","orcid":"https://orcid.org/0000-0002-7408-7394"},"institutions":[{"id":"https://openalex.org/I3131484930","display_name":"National Institute of Technology Nagaland","ror":"https://ror.org/04cbvzp68","country_code":"IN","type":"education","lineage":["https://openalex.org/I3131484930"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Sujit Kumar","raw_affiliation_strings":["National Institute of Technology Nagaland Department of Electrical and Electronics Engineering, , Dimapur 797103 , India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Institute of Technology Nagaland Department of Electrical and Electronics Engineering, , Dimapur 797103 , India","institution_ids":["https://openalex.org/I3131484930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040444979","display_name":"D. Ganga","orcid":null},"institutions":[{"id":"https://openalex.org/I3131484930","display_name":"National Institute of Technology Nagaland","ror":"https://ror.org/04cbvzp68","country_code":"IN","type":"education","lineage":["https://openalex.org/I3131484930"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"D. Ganga","raw_affiliation_strings":["National Institute of Technology Nagaland Department of Electrical and Electronics Engineering, , Dimapur 797103 , India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Institute of Technology Nagaland Department of Electrical and Electronics Engineering, , Dimapur 797103 , India","institution_ids":["https://openalex.org/I3131484930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I3131484930"],"apc_list":null,"apc_paid":null,"fwci":1.3589,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.80378671,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"24","issue":"2","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":0.9998000264167786,"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":0.9998000264167786,"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":0.9973000288009644,"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/T11138","display_name":"Tribology and Lubrication Engineering","score":0.9796000123023987,"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/hilbert\u2013huang-transform","display_name":"Hilbert\u2013Huang transform","score":0.8158199787139893},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6433203220367432},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6165645718574524},{"id":"https://openalex.org/keywords/bearing","display_name":"Bearing (navigation)","score":0.6122704744338989},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5881563425064087},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.5850212574005127},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5668712854385376},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5226866006851196},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4876750707626343},{"id":"https://openalex.org/keywords/vibration","display_name":"Vibration","score":0.4681903123855591},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.46812501549720764},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.4366891384124756},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.3509833812713623},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3449613153934479},{"id":"https://openalex.org/keywords/white-noise","display_name":"White noise","score":0.20059314370155334}],"concepts":[{"id":"https://openalex.org/C25570617","wikidata":"https://www.wikidata.org/wiki/Q1006462","display_name":"Hilbert\u2013Huang transform","level":3,"score":0.8158199787139893},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6433203220367432},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6165645718574524},{"id":"https://openalex.org/C199978012","wikidata":"https://www.wikidata.org/wiki/Q1273815","display_name":"Bearing (navigation)","level":2,"score":0.6122704744338989},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5881563425064087},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.5850212574005127},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5668712854385376},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5226866006851196},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4876750707626343},{"id":"https://openalex.org/C198394728","wikidata":"https://www.wikidata.org/wiki/Q3695508","display_name":"Vibration","level":2,"score":0.4681903123855591},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.46812501549720764},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.4366891384124756},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3509833812713623},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3449613153934479},{"id":"https://openalex.org/C112633086","wikidata":"https://www.wikidata.org/wiki/Q381287","display_name":"White noise","level":2,"score":0.20059314370155334},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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/C165205528","wikidata":"https://www.wikidata.org/wiki/Q83371","display_name":"Seismology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1115/1.4062453","is_oa":false,"landing_page_url":"https://doi.org/10.1115/1.4062453","pdf_url":null,"source":{"id":"https://openalex.org/S173178594","display_name":"Journal of Computing and Information Science in Engineering","issn_l":"1530-9827","issn":["1530-9827","1944-7078"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310316053","host_organization_name":"ASM International","host_organization_lineage":["https://openalex.org/P4310316053"],"host_organization_lineage_names":["ASM International"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computing and Information Science in Engineering","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W64138146","https://openalex.org/W2046349715","https://openalex.org/W2052504522","https://openalex.org/W2064675550","https://openalex.org/W2067802406","https://openalex.org/W2140336071","https://openalex.org/W2258884143","https://openalex.org/W2321143615","https://openalex.org/W2404692435","https://openalex.org/W2735326783","https://openalex.org/W2744790985","https://openalex.org/W2747883260","https://openalex.org/W2750146274","https://openalex.org/W2790195878","https://openalex.org/W2886755908","https://openalex.org/W2892066135","https://openalex.org/W2978157082","https://openalex.org/W3001472289","https://openalex.org/W3011203142","https://openalex.org/W3109842401","https://openalex.org/W3126242280","https://openalex.org/W3126741961","https://openalex.org/W3188514081","https://openalex.org/W4213237138","https://openalex.org/W4220680263","https://openalex.org/W4281758145","https://openalex.org/W4307811464","https://openalex.org/W4309223237","https://openalex.org/W6774983417","https://openalex.org/W6789994915"],"related_works":["https://openalex.org/W3014107421","https://openalex.org/W2081563414","https://openalex.org/W2363056446","https://openalex.org/W2359718298","https://openalex.org/W2377062149","https://openalex.org/W2076661204","https://openalex.org/W2380939102","https://openalex.org/W2089603224","https://openalex.org/W2361368121","https://openalex.org/W1585144779"],"abstract_inverted_index":{"Abstract":[0],"In":[1,76],"rotating":[2],"machines,":[3],"roller":[4],"bearings":[5],"are":[6,129,137],"important":[7],"and":[8,38,91,134,182],"prone":[9],"to":[10,47,69,88,187],"frequent":[11],"faults.":[12,56,255],"Hence,":[13],"accurate":[14,52],"classification":[15,252],"of":[16,24,33,54,59,100,115,140,152,168,193,203,216,226,233,241,253],"bearing":[17,55,85,106,254],"faults":[18,169],"is":[19,82,180],"significant":[20,153],"in":[21,45,65,73,150,170,235,245],"the":[22,31,51,66,121,165,204,214,217,239],"maintenance":[23],"machines.":[25],"Toward":[26],"this,":[27,77],"a":[28,172],"framework":[29,68,196,210,244],"using":[30],"combination":[32],"signal":[34],"processing,":[35],"machine":[36],"learning,":[37],"deep":[39,178],"learning":[40],"algorithms":[41,136],"has":[42,197],"been":[43,63,198],"proposed":[44,67,209],"contrast":[46],"traditional":[48],"approaches":[49],"for":[50,104,200,250],"identification":[53],"The":[57,93,191,208,229],"benefits":[58],"each":[60],"algorithm":[61],"have":[62,120],"reaped":[64],"overcome":[70],"challenges":[71],"met":[72],"fault":[74,189],"identification.":[75],"ensemble":[78],"empirical":[79],"mode":[80,96],"decomposition":[81],"applied":[83],"on":[84,144],"vibration":[86,127,248],"signals":[87,99,128],"reduce":[89],"nonstationarity":[90],"noise.":[92],"12":[94],"intrinsic":[95],"function":[97],"(IMF)":[98],"24k":[101],"length":[102],"obtained":[103],"three":[105],"conditions":[107],"at":[108],"four":[109],"different":[110,201],"speeds":[111],"constituted":[112],"feature":[113,147,154],"space":[114,148,155],"dimension":[116],"[36*8*24,000].":[117],"IMFs":[118],"that":[119],"highest":[122],"correlation":[123],"coefficient":[124],"with":[125,184,222],"raw":[126,223],"selected":[130,145],"as":[131],"features":[132,186],"[3*8*24,000],":[133],"intelligent":[135],"applied.":[138],"Application":[139],"principal":[141],"component":[142],"analysis":[143],"IMF":[146],"resulted":[149],"extraction":[151],"retaining":[156],"temporal":[157,166],"characteristics":[158],"along":[159],"two":[160],"major":[161],"components":[162],"[3*2*24,000].":[163],"Considering":[164],"dependence":[167],"signals,":[171],"stacked":[173,205,218],"long":[174],"short-term":[175],"memory":[176],"(LSTM)":[177],"network":[179],"chosen":[181],"trained":[183,221],"extracted":[185],"improve":[188],"classification.":[190],"performance":[192,215],"this":[194],"developed":[195,242],"evaluated":[199],"metrics":[202],"LSTM":[206,219],"model.":[207],"also":[211],"satisfactorily":[212],"surpassed":[213],"model":[220],"data,":[224],"capable":[225],"auto-feature":[227],"learning.":[228],"comparative":[230],"results":[231],"inclusive":[232],"models":[234],"relevant":[236],"literature":[237],"illustrate":[238],"efficacy":[240],"combinational":[243],"handling":[246],"dynamic":[247],"data":[249],"precise":[251]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
