İndiksiyon Motorun Mekanik Arıza Teşhisinde Makine Öğrenme Yöntemlerinin Kullanılması

Yıl 2019, Sayı: 16, 881 - 904, 31.08.2019

Mehmet Minaz , Kadriye Yıldız

https://doi.org/10.31590/ejosat.596898

Cited By: 3

Öz

Elektrik makinalarında erken
arıza tespiti, arızanın büyüyüp hasarı yaymadan önüne geçilmesi açısından
oldukça önemlidir. Arızaların büyümeden öngörülüsü, motorun ömrünü
artırabildiğinden araştırmacıların ilgi odağı haline gelmiştir. Bu yönde
çalışan araştırmacılar endüstriyel düzeyde hızlı, yorumlaması kolay ve işletme
açısından uygulanabilirlik olan teknikler üzerine odaklanmıştır. Bu çalışmada indüksiyon
motorlarda oluşan kırık rotor çubuğu ve eksenden kaçıklık arızalarının
sonuçlarını sunmaktadır. Sağlıklı ve hatalı koşullar için bir indüksiyon motorun
sonlu elemanlar modeli (FEM) geliştirilmiş ve analiz edilmiştir. Arızalı bir
makinenin modeli, sağlıklı motorun fiziksel durum ve mekanik pozisyonları
değiştirilip farklı arıza şiddetleri oluşturularak akım, gerilim, akı ve tork sinyalleri
incelenmiştir. Bu farklı arıza şiddetlerine ait elektriksel sinyallerin verdiği
tepkiler karşılaştırılmıştır. Elde edilen akım sinyaline ait ham verilere hızlı
fourier yöntemi (FFT) uygulanarak işlenmiş veriler elde edilmiştir. Öznitelik
çıkarımı olarak kNN, MLP, RT gibi farklı sınıflandırma metotları
kullanılmıştır. Kırık rotor çubuğuna ait farklı arıza şiddetleri ile
ilgilenirken, eksantriklik arızasında ise statik eksantriklik, dinamik
eksantriklik ve karışık eksantriklik arızaları üzerinde durulmuştur. Ayrıca,
farklı sınıflandırmalar kullanarak karşılaştırma yapılmıştır. k-NN, MLP ve RF
algoritması sınıflandırma da doğruluğunun oldukça belirgin olduğu tespit
edilmiştir.

Anahtar Kelimeler

İndiksiyon motor, veri madenciliği, öznitelik çıkarımı, arıza tespiti

The Use of Machine Learning Methods For Induction Motor Mechanical Fault Diagnosis

Öz

Early fault detection in electrical machines, grow and damage is quite important in terms of preventing the fault from spreading. Predictions of fault from growth have become the focus of attention of researchers as they can increase the life of the motor. Researchers working in this field have focused on techniques that are fast on the industrial level, easy to interpret and applicable to the enterprise. In this study, it presents the results of broken rotor bar and eccentric faults in induction motor. The finite element model (FEM) of an induction motor was developed and analyzed for healthy and defective conditions. The model of a fault machine, the physical state of the healthy motor and the mechanical positions are changed and the current, voltage, flux and torque signals are examined by creating different fault intensities. The responses of electrical signals of these different fault intensities were compared. The processed data were obtained by applying the fast fourier method (FFT) to the raw data of the obtained current signal. As a feature extraction, kNN, MLP, RT with different classification methods are used for training purposes in diagnostics. While dealing with the different fault intensities of the broken rotor bar, static eccentricity, dynamic eccentricity and mixed eccentricity faults are emphasized in the case of eccentricity fault. In addition, comparisons were made using different classifications. The accuracy of k-NN, MLP and RF algorithm classification was found to be quite significant.

Anahtar Kelimeler

Induction motor, data mining, feature extraction, fault detection

Kaynakça

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