Programs
- Ph. D. in Mechanical Engineering -
- B.Tech. in Artificial Intelligence (AI) and Data Science (Medical Engineering) - Undergraduate
Publication Type : Journal Article
Thematic Areas : Center for Computational Engineering and Networking (CEN)
Publisher : Expert systems with applications
Source : Expert systems with applications, Elsevier, Volume 35, Number 3, p.1351–1366 (2008)
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Mechanical Engineering
Year : 2008
Abstract : The condition of an inaccessible gear in an operating machine can be monitored using the vibration signal of the machine measured at some convenient location and further processed to unravel the significance of these signals. This paper deals with the effectiveness of wavelet-based features for fault diagnosis using support vector machines (SVM) and proximal support vector machines (PSVM). The statistical feature vectors from Morlet wavelet coefficients are classified using J48 algorithm and the predominant features were fed as input for training and testing SVM and PSVM and their relative efficiency in classifying the faults in the bevel gear box was compared.
Cite this Research Publication : N. Saravanan, Siddabattuni, V. N. S. Kumar, and Dr. K. I. Ramachandran, “A comparative study on classification of features by SVM and PSVM extracted using Morlet wavelet for fault diagnosis of spur bevel gear box”, Expert systems with applications, vol. 35, pp. 1351–1366, 2008.