Multi-Label Classification of Simultaneous Faults of Induction Motors by Vibration Signals

Year 2024, , 1296 - 1314, 31.07.2024

Mustafa Ercire , Abdurrahman Ünsal

https://doi.org/10.29130/dubited.1288799

Abstract

It is important to diagnose the fault of induction motors which are widely used in the industry. The common faults of induction motors consist of mechanical and electrical faults. Various methods are used to detect faults. Commonly used fault detection methods use stator current, supply voltage, vibration, heat, and sound data. In general, a specific method is generally used for each type of fault. Advanced diagnostic methods should be when the simultaneous multiple faults are present. It this study, broken rotor bars, inner-race bearing faults, outer-race bearing faults, short-circuit stator winding faults, and eccentricity faults are investigated individually, in simultaneous two and three groups. Three-axis vibration signals are used in the study. The feature vectors are calculated from the statistical features of vibration signals. The feature vector are used as input in the multi-label classification method. For the multi-label classification, Binary Relevance (BR), Label Powerset (LP) and Classifier Chain (CC) problem transformation methods are used. Naïve Bayes (NB), K-Nearest Neighbors (K-NN), Decision Tree (DT) and Support Vector Machine (SVM) methods are used as base classifier. When LP method and NB classifier are used together, 99.9% accuracy was achieved. When CC method and DT classifier are used together 99.3% accuracy was achieved. When BR method and DT classifier are used together 97.8% accuracy was achieved. LP as the problem transformation method and DT as the classifier give the best accuracy rate among the other methods.

Keywords

Multi Label Classification, Induction Motor Faults, Vibration Analysis, Multi Faults

Asenkron Motor Eş Zamanlı Çoklu Arızalarının Titreşim Sinyalleri ile Çok Etiketli Sınıflandırılması

Abstract

Endüstride yaygın olarak kullanılan asenkron motorlarda meydana gelen arızaların tespiti büyük öneme sahiptir. Asenkron motorlarda yaygın olarak meydana gelen arızalar elektriksel ve mekanik arızalardan oluşmaktadır. Arızaların tespiti için çeşitli yöntemler kullanılmaktadır. Yaygın kullanılan arıza tespit yöntemleri stator akımı, besleme gerilimi, titreşim, ısı ve ses gibi verileri kullanmaktadır. Bu yöntemlerde genel olarak her bir arıza türü için belirli bir yöntem kullanılmaktadır. Birden çok arızanın eş zamanlı olarak meydana geldiği durumlar için ileri seviye arıza tespit yöntemlerinin kullanılması gerekir. Bu çalışmada, asenkron motorlarda meydana gelen rotor kırığı, dış-bilezik rulman arızası, iç-bilezik rulman arızası, eksenel kaçıklık ve stator sargısı kısa-devre arızaları tekil, eş zamanlı-ikili ve eş zamanlı-üçlü olarak incelenmiştir. İncelemede 3-eksen titreşim verileri kullanılmıştır. Titreşim verilerinin istatistiksel değerlerinden öznitelik vektörleri çıkarılmıştır. Öznitelik vektörü çok etiketli sınıflandırma yönteminde girdi olarak kullanılmıştır. Çok etiketli sınıflandırma için İkili Alâka Düzeyi (Binary Relevance, BR), Etiket Güç Seti (Label Powerset, LP) ve Sınıflandırıcı Zinciri (Classifier Chain, CC) problem dönüşüm yöntemleri kullanılmıştır. Temel sınıflandırıcı olarak ise Naive Bayes (NB), K-En Yakın Komşu (K-Nearest Neighbors, K-NN), Karar Ağacı (Decision Tree, DT) ve Destek Vektör Makinesi (Support Vector Machine, SVM) yöntemleri kullanılmıştır. LP yöntemi ile NB sınıflandırıcısının birlikte kullanımında %99,9 doğrulukta, CC yöntemi ile DT sınıflandırıcısının birlikte kullanımında %99,3 doğrulukta ve BR yöntemi ile DT sınıflandırıcısının birlikte kullanımında %97,8 doğrulukta sınıflandırma başarımına ulaşılmıştır. Problem dönüştürme yöntemi olarak LP, sınıflandırıcı olarak ise DT en yüksek başarım oranını vermektedir.

Keywords

Çok etiketli sınıflandırma, Asenkron motor arızaları, Titreşim analizi, Çoklu arıza

Thanks

Bu çalışmada “116E302” kodlu “Asenkron Motorlar Arızalarının Tespiti ve Entropi Analizi ile Arıza Şiddetinin Belirlenmesi” başlıklı TÜBİTAK projesi verileri kullanılmıştır. TÜBİTAK Başkanlığına teşekkür ederiz.

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