Kırık Rotor Çubuğu Arızalarının Belirlenmesinde Derin Öğrenme Yaklaşımları ve Motor Akım İmza Analizi

Öz

Asenkron motorlar, endüstriyel uygulamalarda sağladıkları basit ve sağlam yapı, maliyet etkinliği, kendiliğinden başlama kabiliyeti, yüksek verimlilik ve güvenilirlik gibi avantajlarla tercih edilir. Ancak, bu motorlarda zaman zaman karşılaşılan kırık rotor çubuğu gibi arızalar, performans düşüklüğüne ve işletme maliyetlerinin artmasına neden olabilir. Bu tür arızaların erken teşhisi için derin öğrenme modelleri giderek daha fazla kullanılmaktadır. Bu modeller, motor verilerinden karmaşık desenleri tanıyarak potansiyel arızaları önceden belirleyebilir, böylece zamanında müdahale ile motor ömrü uzatılabilir ve üretim sürekliliği sağlanabilir. Bu çalışma dört farklı derin öğrenme modeli kullanılarak asenkron motorlardaki kırık rotor çubuğu teşhisi gerçekleştirilmiştir. Hazır veri seti kullanılan çalışmada akım sinyalleri ile elde edilen görüntüler üzerinden ikili sınıflandırma yapılmıştır. Yapılan çalışma sonucunda %90 üzerinde başarım sağlanmıştır. Böylece derin öğrenme modellerinin asenkron motorlar üzerinde etkinliği kanıtlanmıştır.

Anahtar Kelimeler

• Asenkron Motor
• Arıza Teşhisi
• Kırık Rotor Çubuğu
• Vision Transformer Model
• Görüntü İşleme

Deep Learning Approaches and Motor Current Signature Analysis in Detection of Broken Rotor Bar Faults

Abstract

Induction motors are preferred in industrial applications due to their simple and robust structure, cost-effectiveness, self-starting capability, high efficiency, and reliability. However, faults like broken rotor bars occasionally encountered in these motors can lead to reduced performance and increased operating costs. Deep learning models are increasingly being used for the early detection of such faults. These models can recognize complex patterns in motor data to identify potential faults in advance, allowing for timely intervention, extending motor life, and ensuring production continuity. In this study, the diagnosis of broken rotor bars in induction motors was performed using four different deep learning models. Binary classification was conducted based on images obtained from current signals using a pre-existing dataset. The study achieved over 90% accuracy, thereby proving the effectiveness of deep learning models on induction motors.

Keywords

• Induction Motor
• Fault Diagnosis
• Broken Rotor Bar
• Vision Transformer Model
• Image Processing

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