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    Acoustic emission sensor network based fault diagnosis of induction motors using a gabor filter and multiclass support vector machines
    (© 2016 Old City Publishing, Inc., 2016) Islam, Md Rashedul; Uddin, Jia; Kim, Jongmyon
    Reliable and efficient fault diagnosis of induction motors is an important issue in industrial environments. This paper proposes a method for reliable fault diagnosis of induction motors using signal processing of acoustic emission (AE) data, including Gabor filtering and the use of multiclass support vector machines (MCSVMs), where a ZigBee based wireless sensor network (WSN) model is used for efficiently transmitting AE signals to a diagnosis server. In the proposed fault diagnosis approach, the induction motor’s different state signals are acquired through proper placement of AE sensors. The AE data are sent to a server through the wireless sensor network and decomposed using discrete wavelet transformation (DWT). An appropriate band is then selected using the maximum energy ratio, and a one-dimensional (1D) Gabor filter with various frequencies and orientation angles is applied to reduce abnormalities and extract various statistical parameters for generating features. In addition, principal component analysis (PCA) is applied to the extracted features to select the most dominant feature dimensions. Finally, one-against-one multiclass support vector machines (OAA-MCSVMs) are used to classify multiple fault types of an induction motor, where each SVM individually trains with its own features to increase the fault classification accuracy of the induction motor. In experiments, the proposed approach achieved an average classification accuracy of 99.80%, outperforming conventional fault diagnosis models.
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    A two-dimensional fault diagnosis model of induction motors using a gabor filter on segmented images
    (© 2016 Science and Engineering Research Support Society, 2016) Uddin, Jia; Islam, Mr. Rashedul; Kim, Jong-Myon; Kim, Cheol-Hong
    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.