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dc.contributor.authorUddin, Jia
dc.contributor.authorKang, Myeongsu
dc.contributor.authorDish, V. Nguyen
dc.contributor.authorKim, Jong-Myon
dc.identifier.citationUddin, J., Kang, M., Nguyen, D. V., & Kim, J. -. (2014). Reliable fault classification of induction motors using texture feature extraction and a multiclass support vector machine. Mathematical Problems in Engineering, 2014 doi:10.1155/2014/814593en_US
dc.descriptionThis article was published in the Mathematical Problems in Engineering [© 2014 Jia Uddin et al.] and the definite version is available at : The Journal's website is at:
dc.description.abstractThis paper proposes a method for the reliable fault detection and classification of induction motors using two-dimensional (2D) texture features and a multiclass support vector machine (MCSVM). The proposed model first converts time-domain vibration signals to 2D gray images, resulting in texture patterns (or repetitive patterns), and extracts these texture features by generating the dominant neighborhood structure (DNS) map. The principal component analysis (PCA) is then used for the purpose of dimensionality reduction of the high-dimensional feature vector including the extracted texture features due to the fact that the high-dimensional feature vector can degrade classification performance, and this paper configures an effective feature vector including discriminative fault features for diagnosis. Finally, the proposed approach utilizes the one-against-all (OAA) multiclass support vector machines (MCSVMs) to identify induction motor failures. In this study, the Gaussian radial basis function kernel cooperates with OAA MCSVMs to deal with nonlinear fault features. Experimental results demonstrate that the proposed approach outperforms three state-of-the-art fault diagnosis algorithms in terms of fault classification accuracy, yielding an average classification accuracy of 100% even in noisy environmentsen_US
dc.publisher© 2014 Hindawi Publishing Corporationen_US
dc.subjectInduction motorsen_US
dc.subjectPrincipal component analysisen_US
dc.subjectRadial basis function networksen_US
dc.subjectSystems analysisen_US
dc.subjectextures Classification performanceen_US
dc.titleReliable fault classification of induction motors using texture feature extraction and a multiclass support vector machineen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, BRAC University

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