{"id":"https://openalex.org/W7205680762","doi":null,"title":"Noise eliminated ensemble empirical mode decomposition scalogram analysis for rotating machinery fault diagnosis","display_name":"Noise eliminated ensemble empirical mode decomposition scalogram analysis for rotating machinery fault diagnosis","publication_year":2022,"publication_date":"2022-03-01","ids":{"openalex":"https://openalex.org/W7205680762"},"language":"en","primary_location":{"id":"pmh:oai:umpir.ump.edu.my:35956","doi":null,"is_oa":true,"landing_page_url":"https://umpir.ump.edu.my/id/eprint/35956/1/declarationform.pdf","pdf_url":"https://umpir.ump.edu.my/id/eprint/35956/2/atikfaysalmaster_2022.pdf","source":{"id":"https://openalex.org/S4377196561","display_name":"UMP Institutional Repository (Universiti Malaysia Pahang)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I102913810","host_organization_name":"Universiti Malaysia Pahang Al-Sultan Abdullah","host_organization_lineage":["https://openalex.org/I102913810"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Thesis"},"volume":null,"type":"dissertation","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://umpir.ump.edu.my/id/eprint/35956/2/atikfaysalmaster_2022.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5148966401","display_name":"Faysal Atik","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Atik, Faysal","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5148966401"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"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/T12737","display_name":"Electrical Fault Detection and Protection","score":0.0,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/rotating-machinery-fault-diagnosis","display_name":"rotating machinery fault diagnosis","score":0.949999988079071},{"id":"https://openalex.org/keywords/scalogram","display_name":"scalogram","score":0.609000027179718},{"id":"https://openalex.org/keywords/convolutional-neural-networks","display_name":"convolutional neural networks","score":0.847000002861023},{"id":"https://openalex.org/keywords/generative-adversarial-network-data-augmentation","display_name":"generative adversarial network data augmentation","score":0.8040000200271606},{"id":"https://openalex.org/keywords/bearing-fault-classification","display_name":"bearing fault classification","score":0.8270000219345093},{"id":"https://openalex.org/keywords/vibration-signal-analysis","display_name":"vibration signal analysis","score":0.6600000262260437},{"id":"https://openalex.org/keywords/white-noise-reduction","display_name":"white noise reduction","score":0.5720000267028809},{"id":"https://openalex.org/keywords/ensemble-empirical-mode-decomposition","display_name":"ensemble empirical mode decomposition","score":0.7409999966621399},{"id":"https://openalex.org/keywords/continuous-wavelet-transform","display_name":"continuous wavelet transform","score":0.9430000185966492},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"feature extraction","score":0.6069999933242798},{"id":"https://openalex.org/keywords/machine-condition-monitoring","display_name":"machine condition monitoring","score":0.824999988079071},{"id":"https://openalex.org/keywords/data-augmentation","display_name":"data augmentation","score":0.5329999923706055},{"id":"https://openalex.org/keywords/k-nearest-neighbors","display_name":"k-nearest neighbors","score":0.6309999823570251},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"support vector machine","score":1.0}],"concepts":[{"id":"https://openalex.org/C25570617","wikidata":"https://www.wikidata.org/wiki/Q1006462","display_name":"Hilbert\u2013Huang transform","level":3,"score":0.756600022315979},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7562000155448914},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7232999801635742},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.666100025177002},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6347000002861023},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4740000069141388},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4659999907016754},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4350999891757965},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.40939998626708984},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.40709999203681946},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.3968000113964081},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.38420000672340393},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.33869999647140503},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.3384000062942505},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.3334999978542328},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.33309999108314514},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.3273000121116638},{"id":"https://openalex.org/C157709420","wikidata":"https://www.wikidata.org/wiki/Q3739974","display_name":"FastICA","level":4,"score":0.32580000162124634},{"id":"https://openalex.org/C112633086","wikidata":"https://www.wikidata.org/wiki/Q381287","display_name":"White noise","level":2,"score":0.31450000405311584},{"id":"https://openalex.org/C78201319","wikidata":"https://www.wikidata.org/wiki/Q685727","display_name":"Grayscale","level":3,"score":0.29589998722076416},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.2946000099182129},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.29019999504089355},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.28790000081062317},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.28139999508857727},{"id":"https://openalex.org/C46286280","wikidata":"https://www.wikidata.org/wiki/Q2414958","display_name":"Discrete wavelet transform","level":4,"score":0.27649998664855957},{"id":"https://openalex.org/C155777637","wikidata":"https://www.wikidata.org/wiki/Q2736187","display_name":"Wavelet packet decomposition","level":4,"score":0.274399995803833},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.27410000562667847},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.2635999917984009},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2581999897956848}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:umpir.ump.edu.my:35956","doi":null,"is_oa":true,"landing_page_url":"https://umpir.ump.edu.my/id/eprint/35956/1/declarationform.pdf","pdf_url":"https://umpir.ump.edu.my/id/eprint/35956/2/atikfaysalmaster_2022.pdf","source":{"id":"https://openalex.org/S4377196561","display_name":"UMP Institutional Repository (Universiti Malaysia Pahang)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I102913810","host_organization_name":"Universiti Malaysia Pahang Al-Sultan Abdullah","host_organization_lineage":["https://openalex.org/I102913810"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Thesis"}],"best_oa_location":{"id":"pmh:oai:umpir.ump.edu.my:35956","doi":null,"is_oa":true,"landing_page_url":"https://umpir.ump.edu.my/id/eprint/35956/1/declarationform.pdf","pdf_url":"https://umpir.ump.edu.my/id/eprint/35956/2/atikfaysalmaster_2022.pdf","source":{"id":"https://openalex.org/S4377196561","display_name":"UMP Institutional Repository (Universiti Malaysia Pahang)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I102913810","host_organization_name":"Universiti Malaysia Pahang Al-Sultan Abdullah","host_organization_lineage":["https://openalex.org/I102913810"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Thesis"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320323310","display_name":"Universiti Malaysia Pahang","ror":"https://ror.org/01704wp68"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7205680762.pdf","grobid_xml":"https://content.openalex.org/works/W7205680762.grobid-xml"},"referenced_works_count":163,"referenced_works":["https://openalex.org/W613766092","https://openalex.org/W937050127","https://openalex.org/W1694307164","https://openalex.org/W1805782476","https://openalex.org/W1960210438","https://openalex.org/W1968722641","https://openalex.org/W1970784519","https://openalex.org/W1973048907","https://openalex.org/W1979539910","https://openalex.org/W1982516149","https://openalex.org/W1985002319","https://openalex.org/W1985437849","https://openalex.org/W1986397312","https://openalex.org/W1988185109","https://openalex.org/W2000821612","https://openalex.org/W2009465763","https://openalex.org/W2012120314","https://openalex.org/W2014683958","https://openalex.org/W2016179185","https://openalex.org/W2022243308","https://openalex.org/W2027362370","https://openalex.org/W2027442485","https://openalex.org/W2028119131","https://openalex.org/W2028586143","https://openalex.org/W2042016670","https://openalex.org/W2045849043","https://openalex.org/W2047514504","https://openalex.org/W2056259492","https://openalex.org/W2057094080","https://openalex.org/W2059192589","https://openalex.org/W2063466743","https://openalex.org/W2063530343","https://openalex.org/W2064535781","https://openalex.org/W2066980082","https://openalex.org/W2071293002","https://openalex.org/W2085435733","https://openalex.org/W2094800275","https://openalex.org/W2112796928","https://openalex.org/W2116932910","https://openalex.org/W2118623933","https://openalex.org/W2120390927","https://openalex.org/W2122482402","https://openalex.org/W2124136621","https://openalex.org/W2132549764","https://openalex.org/W2140501168","https://openalex.org/W2145713550","https://openalex.org/W2152965239","https://openalex.org/W2160097539","https://openalex.org/W2160421187","https://openalex.org/W2163605009","https://openalex.org/W2169721303","https://openalex.org/W2189573485","https://openalex.org/W2258884143","https://openalex.org/W2281636071","https://openalex.org/W2296704245","https://openalex.org/W2401688955","https://openalex.org/W2404692435","https://openalex.org/W2463378445","https://openalex.org/W2464717612","https://openalex.org/W2467390032","https://openalex.org/W2485614840","https://openalex.org/W2487852963","https://openalex.org/W2509779719","https://openalex.org/W2518980640","https://openalex.org/W2519854288","https://openalex.org/W2521267171","https://openalex.org/W2548275288","https://openalex.org/W2598228258","https://openalex.org/W2606436201","https://openalex.org/W2735326783","https://openalex.org/W2735533514","https://openalex.org/W2747883260","https://openalex.org/W2761196906","https://openalex.org/W2762841298","https://openalex.org/W2766238033","https://openalex.org/W2768149039","https://openalex.org/W2768753204","https://openalex.org/W2768988016","https://openalex.org/W2770344288","https://openalex.org/W2775795276","https://openalex.org/W2790195878","https://openalex.org/W2791694051","https://openalex.org/W2795061970","https://openalex.org/W2803530897","https://openalex.org/W2803978172","https://openalex.org/W2807056420","https://openalex.org/W2808254136","https://openalex.org/W2808286220","https://openalex.org/W2808300203","https://openalex.org/W2810292802","https://openalex.org/W2810444707","https://openalex.org/W2887280559","https://openalex.org/W2888400328","https://openalex.org/W2896726808","https://openalex.org/W2897895022","https://openalex.org/W2900367617","https://openalex.org/W2901258825","https://openalex.org/W2901901222","https://openalex.org/W2902151785","https://openalex.org/W2902395336","https://openalex.org/W2912803327","https://openalex.org/W2913801035","https://openalex.org/W2916091221","https://openalex.org/W2916423030","https://openalex.org/W2919115771","https://openalex.org/W2919710279","https://openalex.org/W2926366943","https://openalex.org/W2927522928","https://openalex.org/W2940589124","https://openalex.org/W2948406239","https://openalex.org/W2959284230","https://openalex.org/W2960185383","https://openalex.org/W2962793481","https://openalex.org/W2966008650","https://openalex.org/W2971479067","https://openalex.org/W2972449950","https://openalex.org/W2978077076","https://openalex.org/W2982073446","https://openalex.org/W2983932299","https://openalex.org/W2984277833","https://openalex.org/W2990077825","https://openalex.org/W3002644008","https://openalex.org/W3002879843","https://openalex.org/W3003453509","https://openalex.org/W3005050847","https://openalex.org/W3005393741","https://openalex.org/W3009157567","https://openalex.org/W3010546705","https://openalex.org/W3011171540","https://openalex.org/W3014644090","https://openalex.org/W3015228214","https://openalex.org/W3016159209","https://openalex.org/W3021972511","https://openalex.org/W3029816221","https://openalex.org/W3033236487","https://openalex.org/W3038673834","https://openalex.org/W3041550768","https://openalex.org/W3042440267","https://openalex.org/W3044003250","https://openalex.org/W3081283712","https://openalex.org/W3085543912","https://openalex.org/W3087179963","https://openalex.org/W3087922815","https://openalex.org/W3091352767","https://openalex.org/W3093903390","https://openalex.org/W3094694204","https://openalex.org/W3094764353","https://openalex.org/W3098270842","https://openalex.org/W3107759795","https://openalex.org/W3110074784","https://openalex.org/W3116938925","https://openalex.org/W3124120036","https://openalex.org/W3134481555","https://openalex.org/W3134978274","https://openalex.org/W3137617768","https://openalex.org/W3158597650","https://openalex.org/W3160363968","https://openalex.org/W3173322341","https://openalex.org/W3198062697","https://openalex.org/W3204719278","https://openalex.org/W4232602468","https://openalex.org/W4247150125","https://openalex.org/W6922592913"],"related_works":["https://openalex.org/W2750146274","https://openalex.org/W3163025711","https://openalex.org/W2801457104","https://openalex.org/W3018759860","https://openalex.org/W4386823098","https://openalex.org/W4296986248","https://openalex.org/W4283751174","https://openalex.org/W4319778021","https://openalex.org/W4313528128","https://openalex.org/W3093076841"],"abstract_inverted_index":{"Rotating":[0],"machinery":[1,397],"is":[2,24,34,52],"one":[3],"type":[4],"of":[5,64,115,147,175,208,229,333],"major":[6],"industrial":[7],"component":[8],"that":[9,270,295,347],"suffers":[10],"from":[11,182,222,252,287,299,364],"various":[12],"faults":[13],"and":[14,29,49,68,99,154,198,243,255,264,290,309,328,354,367,372,392],"damage":[15],"due":[16],"to":[17,21,54,61,72,90,125,150,321,369],"the":[18,65,92,96,101,127,168,173,176,183,209,216,223,244,271,280,285,288,296,300,305,317,323,330,338,341,344,348,376],"constant":[19],"workload":[20],"which":[22],"it":[23],"subjected.":[25],"Therefore,":[26],"a":[27,69,144,389],"fast":[28],"reliable":[30],"fault":[31,46,66,292,398],"diagnosis":[32],"method":[33,60,259,303,384,394],"essential":[35],"for":[36,112,164,375,395],"machine":[37,236,247,272],"condition":[38],"monitoring.":[39],"Artificial":[40],"intelligence":[41],"can":[42,385],"be":[43,386],"applied":[44,111,163,315],"in":[45,95],"feature":[47,58,178,185],"extraction":[48,59,179,186],"classification.":[50],"It":[51],"crucial":[53],"use":[55],"an":[56,79],"effective":[57],"obtain":[62],"most":[63],"information":[67],"robust":[70,393],"classifier":[71,109,132,217,339,349,378],"classify":[73],"those":[74],"features.":[75],"In":[76],"this":[77],"study,":[78],"improved":[80,363],"method,":[81,180],"noise-eliminated":[82],"ensemble":[83,190],"empirical":[84,191],"mode":[85,192],"decomposition":[86,193],"(NEEEMD),":[87],"was":[88,110,123,135,162,211,231,260,314],"proposed":[89,124,177,258,281,301,318,383],"reduce":[91,126],"white":[93],"noise":[94],"intrinsic":[97],"functions":[98],"retain":[100],"optimum":[102],"ensembles.":[103],"A":[104,119],"convolution":[105,157],"neural":[106],"network":[107,160],"(CNN)":[108],"classification":[113],"because":[114],"its":[116],"feature-learning":[117],"ability.":[118],"generalised":[120,391],"CNN":[121,142,230,282,324],"architecture":[122],"model":[128],"training":[129,148,169,334,337],"time.":[130],"The":[131,206,227,257,267,360,382],"input":[133],"used":[134,387],"64\u00d764":[136],"pixel":[137],"RGB":[138],"scalogram":[139,297],"samples.":[140],"However,":[141],"requires":[143],"large":[145],"amount":[146,332],"data":[149,165],"achieve":[151],"high":[152],"accuracy":[153,278,356],"robustness.":[155,359],"Deep":[156],"generative":[158],"adversarial":[159],"(DCGAN)":[161],"augmentation":[166],"during":[167],"phase.":[170],"To":[171],"evaluate":[172],"effectiveness":[174,207],"scalograms":[181,210,320],"related":[184],"methods":[187],"such":[188],"as":[189,388],"(EEMD),":[194],"complementary":[195],"EEMD":[196],"(CEEMD)":[197],"continuous":[199],"wavelet":[200],"transform":[201],"(CWT)":[202],"were":[203],"also":[204,212],"classified.":[205],"validated":[213,261],"by":[214],"comparing":[215],"performance":[218,326],"using":[219,249,262,311,340,380],"greyscale":[220],"samples":[221,298],"raw":[224],"vibration":[225],"signals.":[226],"ability":[228],"compared":[232],"with":[233,316,357],"two":[234],"traditional":[235],"learning":[237,273],"algorithms,":[238],"k":[239],"nearest":[240],"neighbour":[241],"(kNN)":[242],"support":[245],"vector":[246],"(SVM),":[248],"statistical":[250],"features":[251],"EEMD,":[253],"CEEMD":[254],"NEEEMD.":[256,381],"bearing":[263,289],"blade":[265,291],"datasets.":[266],"results":[268,345],"show":[269],"algorithms":[274],"achieved":[275],"comparatively":[276],"lower":[277],"than":[279],"model.":[283],"All":[284],"outputs":[286],"classifiers":[293],"demonstrated":[294],"NEEEMD":[302,319],"obtained":[304,350],"highest":[306],"accuracy,":[307],"sensitivity":[308],"robustness":[310],"CNN.":[312],"DCGAN":[313],"enhance":[322],"classifier\u2019s":[325],"further":[327],"identify":[329],"optimal":[331],"data.":[335],"After":[336],"augmented":[342],"samples,":[343],"showed":[346],"even":[351],"higher":[352],"validation":[353],"test":[355,361],"greater":[358],"accuracies":[362],"98%,":[365],"96.31%":[366],"92.25%":[368],"99.6%,":[370],"98.29%":[371],"93.59%,":[373],"respectively,":[374],"different":[377],"models":[379],"more":[390],"rotating":[396],"diagnosis.":[399]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2026-09-02T00:00:00"}