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Fault Classification Using Multi-Layer
Perceptrons and Support Vector Machines
Tshilidzi Marwalaa, Snehashish Chakraverty and
Unathi Mahola
School of Electrical and Information Engineering
University of the Witwatersrand Private Bag x 3 Wits 2050 South Africa
e-mail: t.marwala@ee.wits.ac.za
Central Building Research Institute
Roorkee-247 667, U.A. India
e-mail: sne_chak@yahoo.com
This
paper introduces support vector machines (SVM) to classify faults in a
population of cylindrical shells. The proposed procedure is
tested on a population of 20 cylindrical shells and its performance is
compared to the procedure, which uses multi-layer
perceptrons (MLP). The modal properties extracted from vibration
data are used to train the SVM and MLP. It is observed that the SVM
produces 94% classification accuracy while the MLP produces 88%
classification rates.
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