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AKILLI SİSTEMLER VE UYGULAMALARI DERGİSİ
JOURNAL OF INTELLIGENT SYSTEMS WITH APPLICATIONS
J. Intell. Syst. Appl.
E-ISSN: 2667-6893
Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License.

Detection and Classification of Ring Failures by Artificial Neural Networks in Bearings

Rulmanlarda Bilezik Arızalarının Yapay Sinir Ağları ile Tespiti ve Sınıflandırılması

How to cite: Karabacak YE, Kahraman HT, Gümüşel L, Yılmaz C. Detection and classification of ring failures by artificial neural networks in bearings. Akıllı Sistemler ve Uygulamaları Dergisi (Journal of Intelligent Systems with Applications) 2018; 1(1): 31-35. DOI: 10.54856/jiswa.201805011

Full Text: PDF, in Turkish.

Total number of downloads: 519

Title: Detection and Classification of Ring Failures by Artificial Neural Networks in Bearings

Abstract: An effective way to improve the efficiency and extend the life of the machines is to determine the failures of the bearings during operation. Early detection of bearing failures also has critical importance in terms of production costs. Various maintenance methods are used to prevent the failures. Despite all the precautions, unexpected failures can occur and production operations can be failed. This situation, apart from conventional methods, requires a novel determination and diagnostic technique. In this study, artificial intelligence based methods are applied and models are developed in order to detect bearing failures early and to classify the type of failure. In the developed models, it is possible to detect the ring failures depending on different loads and bearing vibration information. In addition, a classification is carried out for the fault from the inner or outer race of the bearing. Determination of the fault, as well as the diagnosis of the class, will increase stability and productivity, especially in critical industrial applications.

Keywords: Bearing; ring failures; classification


Başlık: Rulmanlarda Bilezik Arızalarının Yapay Sinir Ağları ile Tespiti ve Sınıflandırılması

Özet: Makinelerin verimini arttırmanın ve ömrünü uzatmanın etkili bir yolu rulmanların çalışma sırasında arızalarının tespit edilmesidir. Rulman arızalarının erken tespit edilmesi üretim maliyetleri açısından da kritik öneme sahiptir. Arızaları önlemek için çeşitli bakım yöntemleri kullanılmaktadır. Tüm önlemlere rağmen beklenmedik arızalar oluşabilmekte ve üretim faaliyetleri aksayabilmektedir. Bu durum geleneksel yöntemlerin dışında sıra dışı bir arıza tespit ve teşhis tekniğine ihtiyacı doğurmuştur. Bu çalışmada rulman arızalarını erkenden tespit etmek ve arıza tipini sınıflandırmak amacıyla yapay zekâ tabanlı yöntemler uygulanmakta ve modeller geliştirilmektedir. Geliştirilen modellerde farklı yüklenmelere ve rulman titreşim bilgisine bağlı olarak bilezik hatası tespiti yapılmaktadır. Ayrıca hatanın iç ya da dış bilezikten kaynaklandığına yönelik bir sınıflandırma gerçekleştirilmektedir. Gerek hatanın tespiti gerekse de sınıfının belirlenmesi özellikle kritik endüstriyel uygulamalarda kararlı çalışmayı ve verimliliği artıracaktır.

Anahtar kelimeler: Rulman; bilezik hatası; sınıflandırma


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