Identification and Classification of Imbalance Faults in Induction Motors Using Surrogate Models

Abstract

Induction motors, with their robust structures, low maintenance costs, and high reliability, have a wide range of applications in the industry. However, these motors are susceptible to electrical and mechanical faults caused by environmental and operational conditions. Fault types include issues such as bearing problems, stator winding faults, and rotor bar breakages, with mechanical imbalance faults emerging as a critical issue that adversely affects motor performance. This study aims to compare the performance of surrogate models (RBF and KRG) with deep learning models (RNN, GRU, LSTM) for diagnosing imbalance faults in induction motors. For this purpose, the experimentally collected current (Ia, Ib, Ic) and vibration (X, Y, Z) signals were analyzed in the frequency domain, and the features obtained through FFT were used in the classification processes for three classes (Healthy, DA_1, DA_2). According to the results, the RBF model exhibited the best performance with 97.78% accuracy and 97.64% precision, while the KRG model achieved a notable success with 93.89% accuracy and 93.71% precision. In contrast, the highest-performing deep learning models, RNN and LSTM, demonstrated lower performance with 87.22% accuracy and 87.23% precision. The RBF model outperformed the highest-accuracy deep learning model, RNN, by achieving a 12.11% improvement in accuracy and an 11.93% improvement in precision, proving to be a superior tool for diagnosing imbalance faults. Particularly, the RBF model achieved 100% accuracy in the DA_2 class, effectively distinguishing it from other classes due to its distinct features. These findings demonstrate that surrogate models offer an effective solution for diagnosing faults in induction motors by providing high accuracy and precision with limited data requirements and low computational cost.

Keywords

• Asynchronous Motor Fault Diagnosis
• Surrogate Model
• Imbalance Faults
• Vibration Analysis
• Multi-Model Classification

Identification and Diagnosis of Asynchronous Motor Imbalance Faults Using Surrogate Models

Abstract

Asenkron motorlar, sağlam yapıları, düşük bakım maliyetleri ve yüksek güvenilirlikleri ile endüstride geniş bir kullanım alanına sahiptir. Ancak, bu motorlar çevresel ve operasyonel koşullardan kaynaklanan elektriksel ve mekanik arızalara maruz kalabilmektedir. Arıza türleri arasında rulman problemleri, stator sargı hataları ve rotor çubuğu kırılmaları gibi sorunlar yer almakta, özellikle mekanik dengesizlik arızaları motor performansını olumsuz etkileyen kritik bir sorun olarak öne çıkmaktadır. Bu çalışma, asenkron motorlarda dengesizlik arızalarının teşhis edilmesine yönelik yeni bir yaklaşım olan vekil modeller (RBF ve KRG) ile derin öğrenme modellerinin (RNN, GRU, LSTM) performansını karşılaştırmayı amaçlamaktadır. Bu amaçla, deneysel olarak toplanan akım (Ia, Ib, Ic) ve titreşim (X, Y, Z) sinyalleri, frekans alanında analiz edilmiş ve FFT ile elde edilen özellikler, üç sınıf (Sağlıklı, DA_1, DA_2) için sınıflandırma süreçlerinde kullanılmıştır. Sonuçlara göre, RBF modeli, %97.78 doğruluk ve %97.64 keskinlik oranı ile en iyi performansı sergilemiş, KRG modeli ise %93.89 doğruluk ve %93.71 keskinlik oranı ile dikkate değer bir başarı göstermiştir. Buna karşılık, derin öğrenme modellerinden en yüksek doğruluk oranına sahip olan RNN ve LSTM %87.22 doğruluk ve %87.23 keskinlik oranı ile daha düşük bir performans göstermiştir. RBF modeli, en yüksek doğruluklu derin öğrenme modeli olan RNN’e göre doğruluk oranında %12.11, keskinlik oranında ise %11.93'lük bir artış sağlamış, bu da dengesizlik arızalarının teşhisinde üstün bir araç olduğunu kanıtlamıştır. Özellikle DA_2 sınıfında %100 doğruluk oranına ulaşarak, belirgin özellikleri sayesinde diğer sınıflardan ayrışmıştır. Bu bulgular, vekil modellerin sınırlı veri gereksinimi ve düşük hesaplama maliyetiyle birlikte yüksek doğruluk ve keskinlik oranları sunarak, asenkron motor arıza teşhisinde etkili bir çözüm sunduğunu göstermektedir.

Keywords

• Asenkron Motor Arıza Teşhisi
• Vekil Model
• Dengesizlik Arızası
• Titreşim Analizi
• Çoklu Model Sınıflandırma

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