ANOMALY DETECTION FOR GEAR MANUFACTURING DOWNTIME IN THE AUTOMOTIVE SECTOR USING RARE ITEMSET MINING

Basit Öğe Kaydı
dc.contributor.authorBİRANT, DERYA
dc.contributor.authorTAŞER, Pelin YILDIRIM
dc.contributor.authorAkdaş, Devrim Naz
dc.date.accessioned2024-03-09T20:43:13Z
dc.date.available2024-03-09T20:43:13Z
dc.date.issued2022
dc.departmentİzmir Bakırçay Üniversitesien_US
dc.description.abstractDowntimes in manufacturing significantly influence productivity, and their analysis is necessary for successful and flexible production. Although some classification and regression studies have been performed on the machine downtime in the manufacturing area, the rare itemset mining (RIM) technique has never been implemented in the existing downtime studies until now. Besides, anomaly detection for gear manufacturing downtime in the automotive sector using RIM is yet to be explored. To bridge this gap, this study proposes the application of the RIM method for detecting anomalies in gear manufacturing downtime of earth moving machinery for the first time. In this study, the Rare Pattern Growth (RP-Growth) algorithm was executed on a real-world dataset consisting of downtimes in gear manufacturing of earth moving machinery to discover rare itemsets that indicate anomalies in downtimes. In the experiments, the rare itemsets (anomalies) in the downtime data were detected using different minimum support (minsup) and minimum rare support (minraresup) threshold values. The obtained results were also evaluated in terms of the number of itemsets, execution time, and maximum memory usage. The experimental results show that the proposed approach, called Anomaly Detection with Rare Itemset Mining (ADRIM), is an effective method for detecting anomalies in machine downtimes and can be successfully used in the manufacturing area, especially in the automotive sector.en_US
dc.identifier.doi10.46460/ijiea.1067365
dc.identifier.endpage204en_US
dc.identifier.issn2587-1943
dc.identifier.issue2en_US
dc.identifier.startpage199en_US
dc.identifier.trdizinid1161247en_US
dc.identifier.urihttps://doi.org/10.46460/ijiea.1067365
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/1161247
dc.identifier.urihttps://hdl.handle.net/20.500.14034/1712
dc.identifier.volume6en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.relation.ispartofInternational Journal of Innovative Engineering Applicationsen_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.titleANOMALY DETECTION FOR GEAR MANUFACTURING DOWNTIME IN THE AUTOMOTIVE SECTOR USING RARE ITEMSET MININGen_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
1712.pdf
Boyut:
879.56 KB
Biçim:
Adobe Portable Document Format
İndir

Koleksiyon

TR-Dizin İndeksli Yayınlar Koleksiyonu