A two-dimensional fault diagnosis model of induction motors using a gabor filter on segmented images

Abstract

Image segmentation has received extensive attention due to the use of high-level descriptions of image content. This paper proposes a fault diagnosis model using a Gabor filter on segmented two-dimensional (2D) gray-level images. The proposed approach first converts time domain AE signals into 2D gray-level images to exploit texture information from the converted images. 2D discrete wavelet transform (DWT) is then applied to select appropriate (vertical) texture information and reconstructed it into an image. The reconstructed image is segmented into a number of sub-images depending on the segment size and a Gabor filter is applied on each sub-image. Finally, feature vectors are extracted from the Gabor-filtered sub-images and utilized as inputs in a one-against-all multiclass support vector (OAA-MCSVM) to identify each fault in an induction motor. In this study, multiple bearing defects under various segment sizes are utilized to validate the effectiveness of the proposed method. Experimental results indicate that the proposed model outperforms conventional Gabor-filter-based 2D fault diagnosis algorithms in classification accuracy, exhibiting a 97 % average classification accuracy for 64×64 segmented images.