Industrial Fault Detection and Classification with the Optimal Windows Size Approach

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dc.contributor.authorAyana, Omer
dc.contributor.authorInan, Ali
dc.date.accessioned2025-01-06T17:37:49Z
dc.date.available2025-01-06T17:37:49Z
dc.date.issued2024
dc.description32nd IEEE Signal Processing and Communications Applications Conference (SIU) -- MAY 15-18, 2024 -- Tarsus Univ Campus, Mersin, TURKEY
dc.description.abstractVarious important issues in industrial production processes such as product quality, process safety and supply continuity are diretly related to machine faults that occur in production and distribution stages. In addition to economic losses, machine faults also result in industrial accidents. Early diagnosis of possible faults would cut down possible losses. To date, various solutions on fault detection has been proposed. Existing solutions either detect faults after they occur or misdiagnose them due to complexity caused by operating over multiple measurements. In this study, to the best our knowledge, we propose a supervised model that optimally determines the window size for both fault detection and classification problems. Additionally, in order to determine the features that are more heavily related with the problem, we apply the binary version (BCS) of the nature-inspired Cuckoo Search Algorithm (CSA) for feature selection. Our results indicate that determining the window size appropriately has a significant impact on accuracy and feature selection increases the F-score roughly around 13%.
dc.description.sponsorshipIEEE,IEEE Turkey,Koluman & Berdan,Loodos,Figes,Turkcell,Yildirim Elect
dc.identifier.doi10.1109/SIU61531.2024.10601128
dc.identifier.isbn979-8-3503-8897-8
dc.identifier.isbn979-8-3503-8896-1
dc.identifier.issn2165-0608
dc.identifier.scopus2-s2.0-85200903347
dc.identifier.scopusquality0
dc.identifier.urihttps://doi.org/10.1109/SIU61531.2024.10601128
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2380
dc.identifier.wosWOS:001297894700314
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isotr
dc.publisherIEEE
dc.relation.ispartof32nd Ieee Signal Processing and Communications Applications Conference, Siu 2024
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectFault Detection
dc.subjectFault Classification
dc.subjectCuckoo Search Algorithm
dc.subjectFeature Selection
dc.subjectTime Series
dc.titleIndustrial Fault Detection and Classification with the Optimal Windows Size Approach
dc.title.alternativeOptimal Pencere Boyutu Yaklaşımı ile Endüstriyel Arıza Tespiti ve Sınıflandırması
dc.typeConference Object

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