Yapay Sinir Ağlarıyla Asenkron Motor Çoklu Arızalarının Tespiti ve Sınıflandırılması

Asenkron motor arızalarının tespiti asenkron motorların bakımı için kritik bir konudur. Stator akımı analizi motor arızalarını tespit etmek için yaygın olarak kullanılan bir yöntemdir. Asenkron motor arızalarının tespitine yönelik çok sayıda çalışma bulunmaktadır. Ancak çoklu arıza tespiti ve sınıflandırılmasına yönelik sınırlı sayıda çalışma yapılmıştır. Bu çalışmada, 3 kW bilezikli bir asenkron motorun stator kısa-devre arızaları, kırık rotor çubuğu ile iç bilezik ve dış bilezik rulman arızalarının tespiti ve sınıflandırılması çok katmanlı geri yayılım algoritmasına sahip yapay sinir ağı (YSA) modelleri ile gerçekleştirilmektedir. Çalışma üç aşamada gerçekleştirildi. Birinci aşamada asenkron motor tekil arızalarla birlikte test edildi. Asenkron motor stator sargısı %1, %2, %3, %4 ve %5 oranında kısa-devre edilerek, rotorda üç çubuk kırılarak ve motorun yük tarafı rulmanının iç bileziğinde ve dış bileziğinde arızalar oluşturularak ayrı ayrı test edildi. İkinci aşamada motor, %3 ve %5 stator sargısı kısa-devre arızalarıyla birlikte üç çubuğu kırık rotor ile test edildi. Üçüncü aşamada ise asenkron motor %3 ve %5 stator sargısı kısa-devre arızası, üç çubuğu kırık rotor, iç bileziği ve dış bileziği arızalı rulman ile birlikte test edildi. Bütün testlerde motor tam yük altında çalıştırılmıştır. Sunulan yöntem ile çoklu arızaların tespiti ve sınıflandırılması gerçekleştirilmiştir. Yapılan çalışmada, çoklu arıza tespitinde en yüksek başarım oranı %87 olarak elde edildi. Elde edilen sonuçlarla sunulan yöntemin uygulanabilirliği gösterilmiştir.

Anahtar Kelimeler:

Asenkron motorlar, arıza tipleri, çoklu arıza, arıza tespiti, geri yayılım yapay sinir ağları

Detection and Classification of Multiple Faults of Induction Motors by Using Artificial Neural Networks

Detection of induction motor faults is a critical issue for the maintenance of induction motors. Analysis of the stator current is a widely used method to detect the faults of induction motors. There are many of studies on the detection of faults of induction motors but few studies on the detection of multiple faults are reported. In this study, the detection and classification of short-circuit faults of stator winding, broken rotor bars and inner/outer race bearing faults of a 3 kW squarel-cage induction motor are implemented by ANN. The study was carried out in three stages. In the first stage, the induction motor was tested with single faults including 1%, 2%, 3%, 4% and 5% short-circuited stator windings, three broken rotor bars, and inner/outer race bearing faults. In the second stage, induction motor was tested with 3% and 5% short-circuit stator windings and with three broken rotor bars. In the third stage, induction motor was tested with 3% and 5% short-circuit stator windings, rotor with three broken bars and inner/outer race bearing faults. The induction motor has been tested under full load. The detection and classification of multiple faults were realized by the proposed method. The highest performance rate in the detection of multiple faults was achieved with 87% accuracy rate. The resuts shows the applicability of the proposed method.

Keywords:

Induction motors, fault types, multiple faults, fault detection, backpropagation artificial neural network,

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