Elektrik Motorlarının Arıza Tespitinin Otomatik Olarak Yapılmasında Kullanılan Yapay Öğrenme Yöntemleri

Elektrik motorlarının arıza tespiti ve arıza tespitinde kullanılan özelliklerin bir sınıflandırıcıda eğitilerek arıza tespitinin otomatik olarak yapılması üzerine yapılan çalışmalar gittikçe popüler hale gelmektedir. Bu çalışmada elektrik motorlarının arızalarının tespiti ve arıza şiddetinin belirlenmesinde etkin olan sinyal özellikleri kullanılarak motorun farklı yük ve hız durumlarında karakteristik özelliklerinin arıza şiddetine göre değişim miktarı sınıflandırıcı ağlarda eğitim ve test verileri olarak kullanılması, sınıflandırmanın başarısında etkili olmaktadır. Bu çalışmada arıza tespitinin otomatik olarak belirlenmesi amacıyla kullanılan çok katmanlı yapay sinir ağları MLP , destek vektör makinası SVM , k-en yakın komşuluk yöntemi kNN , karar ağacı DT ve Random Forest RF gibi örüntü tanıma ÖT yöntem ve algoritmaları anlatılmaktadır

Artificial Learning Methods for Automatic Fault Detection in Electric Motors

Studies on the automatic detection of faults and diagnostics of electrical motors by training them in a classifier are becoming increasingly popular. In this study, using the signal characteristics which are effective in determining the faults of the electric motors and determining the fault severity, the effect of changing the characteristics of the motor in different load and speed conditions according to the severity of the fault is used as training and test data in the classification networks. Pattern recognition methods such as multi-layer artificial neural networks MLP , support vector machine SVM , k-nearest neighbors method kNN , decision tree DT and random forest RF methods and algorithms are explained In this study as well

Keywords:

Otomatik motor arıza tespiti, Yapay öğrenme Sınıflandırma,

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