Title: Texture analysis based feature extraction using Gabor filter and SVD for reliable fault diagnosis of an induction motor
Authors: Rashedul Islam; Jia Uddin; Jong-Myon Kim
Addresses: Department of Electrical, Electronic and Computer Engineering, University of Ulsan, De 93 Daehak-ro, Nam-gu, Ulsan 680749, Korea ' Department of Computer Science and Engineering, BRAC University, 66 Mohakhali, Dhaka 1212, Bangladesh ' Department of Electrical, Electronic and Computer Engineering, University of Ulsan, De 93 Daehak-ro, Nam-gu, Ulsan 680749, Korea
Abstract: This paper presents a texture analysis based feature extraction method using a Gabor filter and singular value decomposition (SVD) for reliable fault diagnosis of an induction motor. This method first converts one-dimensional (1D) vibration signal to a two-dimensional (2D) grey-level texture image for each fault signal. Then, the 2D Gabor filter with optimal frequency and orientation values is used to extract a filtered image with distinctive texture information, and SVD is utilised to decompose the Gabor filtered image and select finer singular values of SVD as discriminative features for multi-fault diagnosis. Finally, one-against-all multiclass support vector machines (OAA-MCSVMs) are used as classifiers. In this study, multiple induction motor faults with different noisy conditions are used to validate the proposed fault diagnosis methodology. The experimental results indicate that the proposed method achieves an average classification accuracy of 99.86% and outperforms conventional fault diagnosis algorithms in the fault classification accuracy.
Keywords: induction motor; texture analysis; discriminative features; Gabor filter; singular valued decomposition; SVD.
International Journal of Information Technology and Management, 2018 Vol.17 No.1/2, pp.20 - 32
Available online: 18 Jan 2018 *Full-text access for editors Access for subscribers Free access Comment on this article