A Sound Based Method for Fault Classification with Support Vector Machines in UAV Motors

Ferhat Yol; Ayhan Altınors; Orhan Yaman

A Sound Based Method for Fault Classification with Support Vector Machines in UAV Motors

Abstract

In this study, a machine learning-based method is proposed for Brushless DC (BLDC) motors used in unmanned aerial vehicles (UAV). Shaft failure, magnet failure, propeller failure, and bearing failure are common failures in BLDC motors. These fault types are created on UAV engines. Sound recordings were taken from the engines for each failure type. While collecting the dataset, the motors were run at a constant speed. First of all, sound data was collected for the sound engine. Then, fixed time-length audio recordings were taken for 4 fault classes at a constant speed and a data set was created. This dataset consists of five classes. In order to reduce the data size in these sounds, Average Filter, Average Polling, and Normalization processes were applied, respectively. Then, the Chi2 Method was used for feature selection. In the next step, the Support Vector Machine (SVM) algorithm is used to classify the obtained features. In classification, 96.70% accuracy was calculated with the Cubic SVM algorithm.

Keywords

Brushless motors , Fault detection , Machine learning , Support vector machines , Unmanned aerial vehicles

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Yapay Yaşam ve Karmaşık Uyarlanabilir Sistemler

Bölüm

Araştırma Makalesi

Yazarlar

Ferhat Yol Bu kişi benim
Türkiye

Ayhan Altınors Bu kişi benim
Türkiye

Orhan Yaman ^*
Türkiye

Yayımlanma Tarihi

1 Temmuz 2021

Gönderilme Tarihi

10 Nisan 2021

Kabul Tarihi

24 Haziran 2021

Yayımlandığı Sayı

Yıl 2021 Cilt: 4 Sayı: 1

IZ

https://izlik.org/JA93SD95HE

IEEE

[1]F. Yol, A. Altınors, ve O. Yaman, “A Sound Based Method for Fault Classification with Support Vector Machines in UAV Motors”, DataSCI, c. 4, sy 1, ss. 5–10, Tem. 2021, [çevrimiçi]. Erişim adresi: https://izlik.org/JA93SD95HE