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Publication Type : Journal Article
Publisher : International Journal of Control Theory and Applications.
Source : International Journal of Control Theory and Applications, Volume 9, Number 10, p.4433-4443 (2016)
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Electronics and Communication
Year : 2016
Abstract : Bearings are the pivotal components in rotating machines whose failure can result in unpredicted loss in productivity. Hence the faults on bearing need to be rectified as early as possible. In this paper four conditions of a DC motor namely good condition, defect on inner of race, defect on outer of race and defect on both inner and outer of race are obtained and subjected to classification using statistical features after a preprocessing operation for denoising. The denoising algorithm employed for preprocessing is Overlapping Group Shrinkage (OGS) and SVM is the classifier used. The accuracy in classification is found to be more when statistical features of denoised signal are fed as inputs to the classifier. Later, a vibration signal modeling system and its denoising is studied. © International Science Press.
Cite this Research Publication : A. Chandran, Dr. Neethu Mohan, and Dr. Soman K. P., “Non-convex group sparsity denoising for bearing fault diagnosis using SVM”, International Journal of Control Theory and Applications, vol. 9, pp. 4433-4443, 2016.