Fırat DİŞLİ, Mehmet GEDİKPINAR, Abdulkadir SENGUR

Kırık Rotor Çubuğu Sayısının Ampirik Mod Ayrışımı ve Makine Öğrenmesi Yaklaşımları İle Belirlenmesi

Endüstriyel sürücü sistemlerinde verimlilikleri, sağlamlıkları, güç ve boyut çeşitlilikleri nedeniyle asenkron motorlar sıklıkla kullanılmaktadırlar. Asenkron motorlarda meydana gelen kırık rotor çubuğu arızaları, sistemin verimliliğini doğrudan etkilediğinden arıza teşhisi gittikçe önem kazanmaktadır. Kırık rotor çubuğu arızalarının teşhisi için hem stator akım sinyali hem de motor titreşim sinyali kullanılmaktadır. Son zamanlarda bu konuda yapılan çalışmalarda bazı sinyal işlemle teknikleri ile birlikte makine öğrenmesi yöntemleri kullanılmaktadır. Bu çalışmada, ampirik mod ayrışımı (AMA) ve makine öğrenmesi yöntemleri kullanılarak kırık rotor çubuğu sayısının sınıflandırılması gerçekleştirilmiştir. İlk olarak arızalı motor veri setinden alınan bir faz akımı ve motor titreşim sinyali filtrelenip zarflanmıştır. İkinci adımda bu sinyaller AMA yöntemiyle 5 adet içsel mod fonksiyonuna (İMF) ayrıştırılıp spektral entropi ve anlık frekans öznitelikleri elde edilmiştir. Üçüncü adımda bu öznitelikler uç uca eklenip yeni öznitelik vektörü oluşturulmuştur. Dördüncü adımda, öznitelik vektörleri destek vektör makinesi (DVM), k en yakın komşu (KEK) ve karar ağacı (KA) makine öğrenmesi yöntemleriyle sınıflandırılmıştır. Başarı parametresi olarak sınıflandırma doğruluğu kullanılmış ve en yüksek başarı %93,9 ile DVM sınıflandırma yönteminden elde edilmiştir. Çalışmanın sonunda literatürde aynı veri seti için yapılan çalışmalar ile performans karşılaştırılması yapılmış ve bunların sonucunda kırık rotor çubuğu sayısının sınıflandırılmasının AMA ve DVM ile yapılabileceği görülmüştür.

Anahtar Kelimeler:

Ampirik mod ayrışımı, Asenkron motor, Kırık rotor çubuğu, Makine öğrenmesi

Determination of The Number of Broken Rotor Bars by Empirical Mode Decomposition and Machine Learning Approaches

Induction motors are frequently used in the industrial drive systems due to their efficiency, robustness, power and size diversity. Diagnosis is becoming increasingly important as broken rotor bar failures in induction motors directly affect the efficiency of the system. Both the stator current signal and the motor vibration signal are used to diagnose broken rotor bar faults. In recent studies on this subject, machine-learning methods are used together with some signal processing techniques. In this study, determination of the number of broken rotor bars was performed using empirical mode decomposition (EMD) and machine learning methods. Firstly, a phase current and vibration signal taken from the faulty motor data set are filtered and enveloped. In the second step, these signals were decomposed into five intrinsic mode functions (IMF) using by the EMD method, and their spectral entropy and instantaneous frequency features were obtained. In the third step, these features are added end-to-end and a new feature vector is created. In the last step, feature vectors are classified by support vector machine (SVM), k nearest neighbor (KNN) and decision tree (DT) machine learning methods. Classification accuracy was used as the success parameter and the highest success in classification was obtained with SVM, with a classification accuracy of 93.9%. Final of the study, performance comparisons were made with the studies conducted for the same data set in the literature. As a result, it has been seen that the classification of the number of broken rotor bars can be done successfully with EMD and SVM.

Keywords:

Broken rotor bar Induction motor, Empirical mode decomposition, Instantaneous frequency, Machine learning,

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