Fırçasız DC Motorunun Eksen Kaçıklığı ve Kırık Mıknatıs Arızalarının Tespitinin Bilgisayar Benzetimi ile Yapılması

Bu çalışma fırçasız DC motorlarda (BLDC) oluşabilecek arızalar önceden belirlenerek motor çalışmasınındevamlılığının sağlanması ve oluşabilecek olumsuzlukları önlemek açısından önem taşımaktadır. Hem arıza tespitive arıza şiddetinin belirlenmesi hem de sabit mıknatıslı motorunun tasarımı sonlu elemanlar yöntemi kullanılarakgerçekleştirildi. Sonlu elemanlar yöntemi kullanılarak motor analizleri yapıldı. Sonlu elemanlar yöntemiylesağlıklı motor, arızalı motor ve bu arızaların farklı şiddetlerinde simülasyonlar gerçekleştirildi. Endüksiyon motoruiçin Hızlı Fourier Dönüşümü (FFT) uygun görülürken BLDC motoru için trapezoidal sinyal çıkışından dolayıDalgacık dönüşüm (WT) yöntemi kullanılarak analiz gerçekleştirilmiştir. Bu çalışmada daha az belirgin olmayandurum analiz edilmiştir. FFT ve WT ölçülenler ile iyi bir uyum içinde olduğunu göstermiştir. Önerilen yöntemikullanarak stator akımı ve stator geriliminin sabit mıknatıs arıza tespiti için yararlı olduğunu göstermiştir. Ayrıca,farklı sınıflandırıcılar kullanarak karşılaştırma yapılmıştır. İncelenen k-NN, MLP ve RF algoritması sınıflandırmada doğruluğunun oldukça kayda değer olduğu bulunmuştur.

Broken Rotor Magnet and Eccentricity Faults Detection of Brushless DC Motor Simulation Model

This study is important in order to ensure the continuity of the motor operation by preventing faults that may occur in brushless DC motors (BLDC) and to prevent possible negativities. Both the detection of the fault and the determination of the severity of the fault and the design of the permanent magnet motor were carried out using the finite element method. Motor analysis was performed using finite element method. Simulations of healthy motor, defective motor and different severities of these faults were performed by finite element method. While Fast Fourier Transform (FFT) was found suitable for induction motor, wavelet transform (WT) method was used for BLDC motor because of trapezoidal signal output. This study was less pronounced in the non-state analysis. FFT and WT were in good agreement with those measured. Using the proposed method, it has shown that stator current and stator voltage are useful for permanent magnet fault detection. In addition, comparisons were made using different classifiers. The accuracy of the k-NN, MLP and RF arguments used was found to be quite significant.

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