Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 35, Issue 3, Pages 1351-1366Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2007.08.026
Keywords
support vector machine; proximal support vector machines; bevel gear box; Morlet wavelet; statistical features; fault detection
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The condition of an inaccessible gear in an operating machine can be monitored using the vibration signal of the machine measured at sonic 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. (C) 2007 Elsevier Ltd. All rights reserved.
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