TY  - JOUR
T1  - AKILLI FABRİKALARDAKİ OTONOM TAŞIYICILAR İÇİN BULANIK MANTIK TABANLI ANOMALİ TESPİTİ
TT  - FUZZY LOGIC BASED ANOMALY DETECTION FOR AUTONOMOUS TRANSPORT VEHICLES IN SMART FACTORIES
AU  - Örnek, Özlem
AU  - Gülbandılar, Eyyüp
AU  - Yazıcı, Ahmet
PY  - 2020
DA  - April
Y2  - 2020
DO  - 10.31796/ogummf.619239
JF  - Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi
JO  - ESOGÜ Müh Mim Fak Derg
PB  - Eskişehir Osmangazi University
WT  - DergiPark
SN  - 2630-5712
SP  - 53
EP  - 61
VL  - 28
IS  - 1
LA  - tr
AB  - Dijital dönüşüm sanayidekibirçok sürecin veri odaklı yeni yaklaşımlarla ele alınmasını gereklikılmaktadır. Bu bağlamda Endüstri 4.0 ile beraber akıllı fabrikalarda da önemlidijital dönüşümün olması beklenmektedir. Akıllı fabrikalardaki dijital dönüşümekatkı sağlayacak en önemli teknolojilerden bir tanesi de otonom taşıyıcı araç(OTA)’lardır. OTA’ların fabrikaiçerisindeki görevlerini verimli bir şeklide gerçekleştirmeleri ve beklenmedikbir problem veya aksama olduğunda insan müdahalesi olmadan bu durumun veriüzerinden tespiti önemlidir. Bu çalışmada, Bulanık mantık ile OTA’ların fabrikaiçerisindeki trafik ağında oluşabilecek beklenmedik durma, yavaşlama vb.kaynaklı anormal durumlar tespit edilmektedir. Yapılan testlerde önerilenyöntemin %84,62 başarıyla sonuç verdiği gözlenmiştir.
KW  - Anomali Tespiti
KW  - Bulanık Mantık
KW  - Akıllı Fabrika
KW  - Otonom Araç
N2  - Digitaltransformation requires new data-oriented approaches in industry. In thiscontext, it is expected that there will be significant digital transformationin the smart factories with Industry 4.0. One of the most importanttechnologies that will contribute to digital transformation in smart factoriesis the autonomous transport vehicle (ATV). ATVs are expected to perform theirtasks in the factory in an efficient manner. And it is also expected to detect anunexpected problem or any failure via the data without human intervention. Thisstudy aimed determining abnormal conditions of traffic network such asunexpected stop and deceleration by using fuzzy logic in the factory. The performedtests show that the proposed method results success (84.62%).
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UR  - https://doi.org/10.31796/ogummf.619239
L1  - https://dergipark.org.tr/en/download/article-file/1031703
ER  -