COMPARISON OF CLASSIFICATION ALGORITHMS FOR ANOMALY DETECTION IN ENERGY OPTIMIZATION OF HIGH RACK STORAGE SYSTEMS

Yıl 2020, , 89 - 109, 31.12.2020

Cihan Bayraktar , Hadi Gökçen

https://doi.org/10.33461/uybisbbd.790369

Cited By: 2

Öz

The production systems digitized by ensuring healthy data flow between the units and the smart factory structures that are automated in line with this digitization process find more and more places in the production industry. Although such systems have provided important developments and technological advances in production processes, they also bring with it various problems. One of these is the process of quickly detecting and resolving an abnormal situation occurring in autonomous production systems. In this context, various studies have been carried out recently for anomaly detection. One of the most studied areas for anomaly detection is machine learning algorithms. In this study, the performances of various machine learning algorithms were tested on two different data sets obtained from a prototype study on energy optimization of high storage systems. As a result, learning models created with Artificial Neural Networks, C4.5 Decision Tree, Random Forest and k Nearest Neighbor algorithms have achieved a high performance rate in detecting anomalies within the tested data sets. Among these algorithms, the Random Forest algorithm has attracted attention with its accuracy performance of approximately 98%.

Anahtar Kelimeler

Anomaly Detection, Classification, Machine Learning, Smart Factories

YÜKSEK RAFLI DEPOLAMA SİSTEMLERİNİN ENERJİ OPTİMİZASYONUNDA ANOMALİ TESPİTİ İÇİN SINIFLAMA ALGORİTMALARININ KARŞILAŞTIRILMASI

Öz

Birimler arasında sağlıklı veri akışının sağlanması ile dijitalleşen üretim sistemleri ve bu dijitalleşme süreci doğrultusunda otomatikleşen zeki fabrika yapıları gün geçtikçe üretim endüstrisinde kendisine daha fazla yer bulmaktadır. Bu tür sistemler, üretim önemli gelişmeler ve teknolojik ilerlemeler sağlamış olsa da çeşitli sorunları da beraberinde getirmektedir. Bunlardan bir tanesi de otonom çalışan üretim sistemlerinde gerçekleşen bir anormal durumun hızlı bir şekilde tespit edilerek, çözüme kavuşturulması sürecidir. Bu kapsamda son zamanlarda anomali tespiti için çeşitli çalışmalar yapılmaktadır. Anomali tespiti konusunda en çok destek alınan alanlardan bir tanesi de makine öğrenmesi algoritmalarıdır. Bu çalışmada, yüksek depolama sistemlerinin enerji optimizasyonu hakkında uygulanmış bir prototip çalışmadan elde edilmiş olan iki farklı veri seti üzerinde çeşitli makine öğrenmesi algoritmalarının performansları test edilmiştir. Sonuç olarak, Yapay Sinir Ağları, C4.5 Karar Ağacı, Rastgele Orman ve k En Yakın Komşu algoritmaları ile oluşturulan öğrenme modelleri, test edilen veri setleri içerisindeki anomalileri tespit etme konusunda yüksek başarım oranı elde etmişlerdir. Özellikle bu algoritmalar içerisinde Rastgele Orman algoritması yaklaşık %98 seviyesindeki doğruluk performansı ile dikkat çekmiştir.

Anahtar Kelimeler

Anomali Tespiti, Sınıflandırma, Makine Öğrenmesi, Zeki Fabrikalar

Kaynakça

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