{"id":"https://openalex.org/W7127327481","doi":"https://doi.org/10.1093/jcde/qwag010","title":"Noise-robust fault diagnosis for rotating equipment in marine vessels using a band-selective envelope attention network without noisy data training","display_name":"Noise-robust fault diagnosis for rotating equipment in marine vessels using a band-selective envelope attention network without noisy data training","publication_year":2026,"publication_date":"2026-02-01","ids":{"openalex":"https://openalex.org/W7127327481","doi":"https://doi.org/10.1093/jcde/qwag010"},"language":"en","primary_location":{"id":"doi:10.1093/jcde/qwag010","is_oa":true,"landing_page_url":"https://doi.org/10.1093/jcde/qwag010","pdf_url":null,"source":{"id":"https://openalex.org/S2485147958","display_name":"Journal of Computational Design and Engineering","issn_l":"2288-4300","issn":["2288-4300","2288-5048"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational Design and Engineering","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1093/jcde/qwag010","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5111538320","display_name":"Hyung Jin Kim","orcid":null},"institutions":[{"id":"https://openalex.org/I191879574","display_name":"Inha University","ror":"https://ror.org/01easw929","country_code":"KR","type":"education","lineage":["https://openalex.org/I191879574"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hyung-Jin Kim","raw_affiliation_strings":["Department of Naval Architecture and Ocean Engineering, INHA University , Incheon 22212 ,"],"raw_orcid":"https://orcid.org/0000-0003-4979-9637","affiliations":[{"raw_affiliation_string":"Department of Naval Architecture and Ocean Engineering, INHA University , Incheon 22212 ,","institution_ids":["https://openalex.org/I191879574"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5124917714","display_name":"Jang Hyun Lee","orcid":null},"institutions":[{"id":"https://openalex.org/I191879574","display_name":"Inha University","ror":"https://ror.org/01easw929","country_code":"KR","type":"education","lineage":["https://openalex.org/I191879574"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jang Hyun Lee","raw_affiliation_strings":["Department of Naval Architecture and Ocean Engineering, INHA University , Incheon 22212 ,"],"raw_orcid":"https://orcid.org/0000-0002-2387-5163","affiliations":[{"raw_affiliation_string":"Department of Naval Architecture and Ocean Engineering, INHA University , Incheon 22212 ,","institution_ids":["https://openalex.org/I191879574"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1650,"currency":"USD","value_usd":1650},"apc_paid":{"value":1650,"currency":"USD","value_usd":1650},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.1415044,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":"2","first_page":"213","last_page":"228"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9437000155448914,"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":0.9437000155448914,"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/T13050","display_name":"Oil and Gas Production Techniques","score":0.003000000026077032,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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":0.00279999990016222,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5752999782562256},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.5295000076293945},{"id":"https://openalex.org/keywords/envelope","display_name":"Envelope (radar)","score":0.46470001339912415},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.4625000059604645},{"id":"https://openalex.org/keywords/impulse-noise","display_name":"Impulse noise","score":0.45179998874664307},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.44749999046325684},{"id":"https://openalex.org/keywords/spectral-envelope","display_name":"Spectral envelope","score":0.43959999084472656},{"id":"https://openalex.org/keywords/data-pre-processing","display_name":"Data pre-processing","score":0.4068000018596649},{"id":"https://openalex.org/keywords/additive-white-gaussian-noise","display_name":"Additive white Gaussian noise","score":0.4043000042438507},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3781000077724457}],"concepts":[{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5752999782562256},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.5295000076293945},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5188999772071838},{"id":"https://openalex.org/C65155139","wikidata":"https://www.wikidata.org/wiki/Q5380912","display_name":"Envelope (radar)","level":3,"score":0.46470001339912415},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.4625000059604645},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45989999175071716},{"id":"https://openalex.org/C127372701","wikidata":"https://www.wikidata.org/wiki/Q16979398","display_name":"Impulse noise","level":3,"score":0.45179998874664307},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.44749999046325684},{"id":"https://openalex.org/C54926389","wikidata":"https://www.wikidata.org/wiki/Q7575188","display_name":"Spectral envelope","level":2,"score":0.43959999084472656},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.4068000018596649},{"id":"https://openalex.org/C169334058","wikidata":"https://www.wikidata.org/wiki/Q353292","display_name":"Additive white Gaussian noise","level":3,"score":0.4043000042438507},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.4041000008583069},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3781000077724457},{"id":"https://openalex.org/C112633086","wikidata":"https://www.wikidata.org/wiki/Q381287","display_name":"White noise","level":2,"score":0.3675000071525574},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.3587999939918518},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.34790000319480896},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.3395000100135803},{"id":"https://openalex.org/C70836080","wikidata":"https://www.wikidata.org/wiki/Q837940","display_name":"Impulse (physics)","level":2,"score":0.328000009059906},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.3192000091075897},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3084000051021576},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.3057999908924103},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3018999993801117},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.2996000051498413},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.2944999933242798},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.29260000586509705},{"id":"https://openalex.org/C100675267","wikidata":"https://www.wikidata.org/wiki/Q1371624","display_name":"Background noise","level":2,"score":0.2856000065803528},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2809000015258789},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.2773999869823456},{"id":"https://openalex.org/C178351263","wikidata":"https://www.wikidata.org/wiki/Q3922399","display_name":"Cyclostationary process","level":3,"score":0.2759000062942505},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2648000121116638},{"id":"https://openalex.org/C2778116611","wikidata":"https://www.wikidata.org/wiki/Q25110567","display_name":"Frequency band","level":3,"score":0.26420000195503235},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.263700008392334},{"id":"https://openalex.org/C92545706","wikidata":"https://www.wikidata.org/wiki/Q902174","display_name":"Radio spectrum","level":2,"score":0.25529998540878296},{"id":"https://openalex.org/C55037315","wikidata":"https://www.wikidata.org/wiki/Q5421151","display_name":"Experimental data","level":2,"score":0.25110000371932983}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1093/jcde/qwag010","is_oa":true,"landing_page_url":"https://doi.org/10.1093/jcde/qwag010","pdf_url":null,"source":{"id":"https://openalex.org/S2485147958","display_name":"Journal of Computational Design and Engineering","issn_l":"2288-4300","issn":["2288-4300","2288-5048"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational Design and Engineering","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1093/jcde/qwag010","is_oa":true,"landing_page_url":"https://doi.org/10.1093/jcde/qwag010","pdf_url":null,"source":{"id":"https://openalex.org/S2485147958","display_name":"Journal of Computational Design and Engineering","issn_l":"2288-4300","issn":["2288-4300","2288-5048"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational Design and Engineering","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.8351061344146729,"display_name":"Life below water","id":"https://metadata.un.org/sdg/14"}],"awards":[{"id":"https://openalex.org/G2821458003","display_name":null,"funder_award_id":"#73110-1","funder_id":"https://openalex.org/F4320321370","funder_display_name":"Inha University"}],"funders":[{"id":"https://openalex.org/F4320321370","display_name":"Inha University","ror":"https://ror.org/01easw929"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1971521993","https://openalex.org/W1972125873","https://openalex.org/W1985437849","https://openalex.org/W2046674752","https://openalex.org/W2106822551","https://openalex.org/W2793062918","https://openalex.org/W2915377492","https://openalex.org/W3045070619","https://openalex.org/W3082506936","https://openalex.org/W3095653363","https://openalex.org/W3132755470","https://openalex.org/W3158078777","https://openalex.org/W3167879867","https://openalex.org/W3193472521","https://openalex.org/W3208063610","https://openalex.org/W4200568209","https://openalex.org/W4220795705","https://openalex.org/W4233465500","https://openalex.org/W4291449359","https://openalex.org/W4310975219","https://openalex.org/W4363679534","https://openalex.org/W4382584879","https://openalex.org/W4387415178","https://openalex.org/W4390341214","https://openalex.org/W4391560484","https://openalex.org/W4391850302","https://openalex.org/W4391932216","https://openalex.org/W4396858790","https://openalex.org/W4399802564","https://openalex.org/W4400391358","https://openalex.org/W4403137676","https://openalex.org/W4408735329","https://openalex.org/W4412499123","https://openalex.org/W4412746177"],"related_works":[],"abstract_inverted_index":{"Abstract":[0],"This":[1,219,274],"paper":[2],"addresses":[3],"the":[4,124,152,244,277],"problem":[5],"of":[6,71,74,176,202,280,295],"performance":[7,31,210,269],"degradation":[8],"that":[9,137,146,224],"occurs":[10],"under":[11,211,270],"low":[12],"signal-to-noise":[13],"ratio":[14],"(SNR)":[15],"conditions":[16,175,213],"when":[17],"diagnosing":[18],"rotating":[19,296],"machinery":[20],"faults":[21],"in":[22,38,182,198,231,239,286,291],"marine":[23,178,287],"environments.":[24],"Artificial":[25],"intelligence-based":[26],"rotating-machinery":[27],"fault":[28,174,240,245,282],"diagnosis":[29,227,283],"exhibits":[30],"limitations":[32],"owing":[33],"to":[34,59,84,204],"restricted":[35],"data":[36,52,165,169,252],"availability":[37],"real":[39,232],"environments":[40,234],"and":[41,77,115,127,172,194,242,263],"low-SNR":[42,212],"conditions.":[43,87],"Previous":[44],"studies":[45],"either":[46],"trained":[47,259],"models":[48],"on":[49],"noise-free":[50],"laboratory":[51,184],"or":[53],"incorporated":[54],"noise-reduction":[55],"modules":[56],"during":[57,166],"preprocessing":[58],"mitigate":[60],"this":[61],"problem.":[62],"However,":[63],"such":[64],"approaches":[65],"remain":[66],"limited":[67],"by":[68,141,189,235],"a":[69,94,112,177,183,221,292],"lack":[70],"prior":[72],"knowledge":[73],"noise":[75,86,142,193,197,230,272],"characteristics":[76],"amplitude,":[78],"as":[79,81],"well":[80],"poor":[82],"generalization":[83],"unfamiliar":[85],"To":[88],"overcome":[89],"these":[90],"limitations,":[91],"we":[92,186],"propose":[93],"band-selective":[95],"envelope":[96,118],"attention":[97,132],"network":[98],"(B-SEAN).":[99],"The":[100,130],"proposed":[101],"model":[102,153],"divides":[103],"raw":[104],"vibration":[105],"signals":[106,160,241],"into":[107],"multiple":[108],"frequency":[109],"bands":[110,136],"using":[111,123,157],"band-splitting":[113],"scheme":[114],"constructs":[116],"stable":[117],"spectra":[119],"for":[120],"each":[121],"band":[122,131],"Hilbert":[125],"transform":[126],"Welch\u2019s":[128],"method.":[129],"module":[133],"automatically":[134],"downweighs":[135],"are":[138,155],"severely":[139],"distorted":[140],"while":[143],"emphasizing":[144],"those":[145],"preserve":[147],"fault-related":[148],"features.":[149,246],"More":[150],"importantly,":[151],"parameters":[154],"optimized":[156],"only":[158,285],"clean":[159],"without":[161,260],"injecting":[162],"any":[163],"noisy":[164,261],"training.":[167],"Using":[168],"from":[170],"normal":[171],"seven":[173],"fuel-supply":[179],"pump":[180],"measured":[181],"environment,":[185],"performed":[187],"verification":[188],"synthesizing":[190],"white":[191],"Gaussian":[192,195],"impulse":[196],"an":[199],"SNR":[200],"range":[201,294],"5":[203],"\u22126":[205],"dB.":[206],"B-SEAN":[207,249],"maintained":[208],"superior":[209],"compared":[214],"with":[215],"existing":[216],"noise-trained":[217],"models.":[218],"demonstrates":[220,276],"practical":[222,278],"method":[223],"enables":[225],"reliable":[226],"despite":[228],"strong":[229],"operating":[233],"detecting":[236],"nonlinear":[237],"variations":[238],"highlighting":[243],"In":[247],"conclusion,":[248],"satisfies":[250],"both":[251],"efficiency":[253],"requirements":[254],"(as":[255,266],"it":[256,267],"can":[257],"be":[258],"data)":[262],"high":[264],"robustness":[265],"maintains":[268],"various":[271],"conditions).":[273],"study":[275],"potential":[279],"noise-resilient":[281],"not":[284],"vessels":[288],"but":[289],"also":[290],"wide":[293],"machinery.":[297]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-02-04T00:00:00"}