İndiksiyon Motorun Mekanik Arıza Teşhisinde Makine Öğrenme Yöntemlerinin Kullanılması

Elektrik makinalarında erken arıza tespiti, arızanın büyüyüp hasarı yaymadan önüne geçilmesi açısından oldukça önemlidir. Arızalarınbüyümeden öngörülüsü, motorun ömrünü artırabildiğinden araştırmacıların ilgi odağı haline gelmiştir. Bu yönde çalışan araştırmacılarendüstriyel düzeyde hızlı, yorumlaması kolay ve işletme açısından uygulanabilirlik olan teknikler üzerine odaklanmıştır. Bu çalışmadaindüksiyon motorlarda oluşan kırık rotor çubuğu ve eksenden kaçıklık arızalarının sonuçlarını sunmaktadır. Sağlıklı ve hatalı koşullariçin bir indüksiyon motorun sonlu elemanlar modeli (FEM) geliştirilmiş ve analiz edilmiştir. Arızalı bir makinenin modeli, sağlıklımotorun fiziksel durum ve mekanik pozisyonları değiştirilip farklı arıza şiddetleri oluşturularak akım, gerilim, akı ve tork sinyalleriincelenmiştir. Bu farklı arıza şiddetlerine ait elektriksel sinyallerin verdiği tepkiler karşılaştırılmıştır. Elde edilen akım sinyaline aitham verilere hızlı fourier yöntemi (FFT) uygulanarak işlenmiş veriler elde edilmiştir. Öznitelik çıkarımı olarak kNN, MLP, RT gibifarklı sınıflandırma metotları ile arıza teşhisinde eğitim amaçlı kullanılmıştır. Kırık rotor çubuğuna ait farklı arıza şiddetleri ileilgilenirken, 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 doğruluğununoldukça belirgin olduğu tespit edilmiştir.

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

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 kNN, MLP and RF algorithm classification was found to be quite significant.

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