{"id":"https://openalex.org/W7131907531","doi":"https://doi.org/10.1109/access.2026.3668971","title":"Finding One\u2019s Bearings in the Hyperparameter Landscape of a Wide-Kernel Convolutional Fault Detector","display_name":"Finding One\u2019s Bearings in the Hyperparameter Landscape of a Wide-Kernel Convolutional Fault Detector","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7131907531","doi":"https://doi.org/10.1109/access.2026.3668971"},"language":"en","primary_location":{"id":"doi:10.1109/access.2026.3668971","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3668971","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3668971","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5073865464","display_name":"Dan Hudson","orcid":"https://orcid.org/0000-0002-2917-4659"},"institutions":[{"id":"https://openalex.org/I170658231","display_name":"Osnabr\u00fcck University","ror":"https://ror.org/04qmmjx98","country_code":"DE","type":"education","lineage":["https://openalex.org/I170658231"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Dan Hudson","raw_affiliation_strings":["Semantic Information Systems Group, Osnabrück University, Osnabrück, Germany"],"affiliations":[{"raw_affiliation_string":"Semantic Information Systems Group, Osnabrück University, Osnabrück, Germany","institution_ids":["https://openalex.org/I170658231"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122623333","display_name":"Jurgen van den Hoogen","orcid":null},"institutions":[{"id":"https://openalex.org/I170658231","display_name":"Osnabr\u00fcck University","ror":"https://ror.org/04qmmjx98","country_code":"DE","type":"education","lineage":["https://openalex.org/I170658231"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Jurgen van den Hoogen","raw_affiliation_strings":["Semantic Information Systems Group, Osnabrück University, Osnabrück, Germany"],"affiliations":[{"raw_affiliation_string":"Semantic Information Systems Group, Osnabrück University, Osnabrück, Germany","institution_ids":["https://openalex.org/I170658231"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5127278872","display_name":"Martin Atzmueller","orcid":null},"institutions":[{"id":"https://openalex.org/I170658231","display_name":"Osnabr\u00fcck University","ror":"https://ror.org/04qmmjx98","country_code":"DE","type":"education","lineage":["https://openalex.org/I170658231"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Martin Atzmueller","raw_affiliation_strings":["Semantic Information Systems Group, Osnabrück University, Osnabrück, Germany"],"affiliations":[{"raw_affiliation_string":"Semantic Information Systems Group, Osnabrück University, Osnabrück, Germany","institution_ids":["https://openalex.org/I170658231"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5073865464"],"corresponding_institution_ids":["https://openalex.org/I170658231"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.67419642,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"39094","last_page":"39119"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.10090000182390213,"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"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.10090000182390213,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.06350000202655792,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.04560000076889992,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.9502999782562256},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.667900025844574},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5950999855995178},{"id":"https://openalex.org/keywords/hyperparameter-optimization","display_name":"Hyperparameter optimization","score":0.5799000263214111},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5633000135421753},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.531000018119812},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5130000114440918},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4819999933242798},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.413100004196167}],"concepts":[{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.9502999782562256},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7871999740600586},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.667900025844574},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5950999855995178},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5885000228881836},{"id":"https://openalex.org/C10485038","wikidata":"https://www.wikidata.org/wiki/Q48996162","display_name":"Hyperparameter optimization","level":3,"score":0.5799000263214111},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5633000135421753},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.531000018119812},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5130000114440918},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5047000050544739},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4819999933242798},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.413100004196167},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.3921000063419342},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.34860000014305115},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.3456000089645386},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.34049999713897705},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.3375000059604645},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3156000077724457},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3077999949455261},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2856000065803528},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.27799999713897705},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.2750000059604645},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.26489999890327454},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.2551000118255615},{"id":"https://openalex.org/C2776879701","wikidata":"https://www.wikidata.org/wiki/Q25048660","display_name":"Multiple kernel learning","level":4,"score":0.2529999911785126},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.251800000667572},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.25029999017715454}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2026.3668971","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3668971","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:52e4a0758f3348b6b11fd16ce7bcf742","is_oa":true,"landing_page_url":"https://doaj.org/article/52e4a0758f3348b6b11fd16ce7bcf742","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 14, Pp 39094-39119 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3668971","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3668971","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Climate action","score":0.43531277775764465,"id":"https://metadata.un.org/sdg/13"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"State-of-the-art":[0],"algorithms":[1],"are":[2],"reported":[3],"to":[4,20,30,62,70,85,102,150,167,175,205,223,236,245],"be":[5,46,119],"almost":[6],"perfect":[7],"at":[8,23],"distinguishing":[9],"the":[10,54,72,75,108,111,139,143,153,170,214,238],"vibrations":[11],"arising":[12],"from":[13,129],"healthy":[14],"and":[15,51,83,134,190],"damaged":[16],"machine":[17],"bearings,":[18],"according":[19],"benchmark":[21,132],"datasets":[22],"least.":[24],"However,":[25],"what":[26],"about":[27],"their":[28],"application":[29],"new":[31,63,249],"data?":[32],"In":[33],"this":[34],"paper,":[35],"we":[36,98,157,228],"confirm":[37],"that":[38,53,138],"neural":[39,81,242],"networks":[40],"for":[41,92],"bearing":[42],"fault":[43],"detection":[44],"can":[45,58],"crippled":[47],"by":[48,126,184,195,230],"incorrect":[49],"hyperparameterisation,":[50],"also":[52],"correct":[55],"hyperparameter":[56],"settings":[57],"change":[59],"when":[60],"transitioning":[61],"data.":[64,154,250],"The":[65,177],"paper":[66],"combines":[67],"multiple":[68],"methods":[69],"explain":[71],"behaviour":[73],"of":[74,77,110,161,179,188,240],"hyperparameters":[76,94,105,239],"a":[78,114,218,224],"wide-kernel":[79],"convolutional":[80,112],"network":[82,243],"how":[84,101,235],"set":[86,103,237],"them.":[87],"Since":[88],"guidance":[89,233],"already":[90],"exists":[91],"generic":[93],"like":[95],"minibatch":[96],"size,":[97],"focus":[99],"on":[100,234,248],"architecture-specific":[104],"such":[106],"as":[107],"width":[109],"kernels,":[113],"topic":[115],"which":[116],"might":[117],"otherwise":[118],"obscure.":[120],"We":[121,201],"reflect":[122],"different":[123,131,186],"data":[124],"properties":[125],"fusing":[127],"information":[128],"seven":[130],"datasets,":[133],"our":[135,241],"results":[136],"show":[137],"kernel":[140,171,220],"size":[141,172],"in":[142,146,152,164,207],"first":[144],"layer":[145],"particular":[147],"is":[148,182,193,212,221],"sensitive":[149],"changes":[151],"Looking":[155],"deeper,":[156],"use":[158],"manipulated":[159],"copies":[160],"one":[162],"dataset":[163],"an":[165],"attempt":[166],"spot":[168],"why":[169,217],"sometimes":[173],"needs":[174],"change.":[176],"relevance":[178],"sampling":[180],"rate":[181],"studied":[183,194],"using":[185],"levels":[187],"resampling,":[189],"spectral":[191],"content":[192],"increasingly":[196],"filtering":[197],"out":[198],"high":[199],"frequencies.":[200],"find":[202],"that,":[203],"contrary":[204],"speculation":[206],"earlier":[208],"work,":[209],"high-frequency":[210],"noise":[211],"not":[213],"main":[215],"reason":[216],"wide":[219],"preferable":[222],"narrow":[225],"kernel.":[226],"Finally,":[227],"conclude":[229],"stating":[231],"clear":[232],"architecture":[244],"work":[246],"effectively":[247]},"counts_by_year":[],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2026-02-28T00:00:00"}