AKILLI FABRİKALARDAKİ OTONOM TAŞIYICILAR İÇİN BULANIK MANTIK TABANLI ANOMALİ TESPİTİ

Year 2020, , 53 - 61, 15.04.2020

Özlem Örnek , Eyyüp Gülbandılar , Ahmet Yazıcı

https://doi.org/10.31796/ogummf.619239

Cited By: 1

Abstract

Dijital dönüşüm sanayideki
birçok sürecin veri odaklı yeni yaklaşımlarla ele alınmasını gerekli
kılmaktadır. Bu bağlamda Endüstri 4.0 ile beraber akıllı fabrikalarda da önemli
dijital dönüşümün olması beklenmektedir. Akıllı fabrikalardaki dijital dönüşüme
katkı sağlayacak en önemli teknolojilerden bir tanesi de otonom taşıyıcı araç
(OTA)’lardır.  OTA’ların fabrika
içerisindeki görevlerini verimli bir şeklide gerçekleştirmeleri ve beklenmedik
bir problem veya aksama olduğunda insan müdahalesi olmadan bu durumun veri
üzerinden tespiti önemlidir. Bu çalışmada, Bulanık mantık ile OTA’ların fabrika
içerisindeki trafik ağında oluşabilecek beklenmedik durma, yavaşlama vb.
kaynaklı anormal durumlar tespit edilmektedir. Yapılan testlerde önerilen
yöntemin %84,62 başarıyla sonuç verdiği gözlenmiştir.

Keywords

Anomali Tespiti, Bulanık Mantık, Akıllı Fabrika, Otonom Araç

Supporting Institution

Türkiye Bilimsel ve Teknolojik Araştırma Kurumu’nun (TÜBİTAK)

Project Number

116E731

FUZZY LOGIC BASED ANOMALY DETECTION FOR AUTONOMOUS TRANSPORT VEHICLES IN SMART FACTORIES

Abstract

Digital
transformation requires new data-oriented approaches in industry. In this
context, it is expected that there will be significant digital transformation
in the smart factories with Industry 4.0. One of the most important
technologies that will contribute to digital transformation in smart factories
is the autonomous transport vehicle (ATV). ATVs are expected to perform their
tasks in the factory in an efficient manner. And it is also expected to detect an
unexpected problem or any failure via the data without human intervention. This
study aimed determining abnormal conditions of traffic network such as
unexpected stop and deceleration by using fuzzy logic in the factory. The performed
tests show that the proposed method results success (84.62%).  

Keywords

Anomaly Detection, Fuzzy Logic, Smart Factories, Autonomous Vehicle

Project Number

116E731

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