Fault Classification in Cylinders Using Multilayer Perceptrons, Support Vector Machines and Guassian Mixture Models

Tshilidzi Marwala; Unathi Mahola; Snehashish Chakraverty

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arXiv · 0705.0197

Fault Classification in Cylinders Using Multilayer Perceptrons, Support Vector Machines and Guassian Mixture Models

Tshilidzi Marwala·Unathi Mahola·Snehashish Chakraverty

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Abstract

Gaussian mixture models (GMM) and support vector machines (SVM) are introduced to classify faults in a population of cylindrical shells. The proposed procedures are tested on a population of 20 cylindrical shells and their performance is compared to the procedure, which uses multi-layer perceptrons (MLP). The modal properties extracted from vibration data are used to train the GMM, SVM and MLP. It is observed that the GMM produces 98%, SVM produces 94% classification accuracy while the MLP produces 88% classification rates.

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Tshilidzi Marwala, Unathi Mahola, Snehashish Chakraverty. 2007-05-02. Fault Classification in Cylinders Using Multilayer Perceptrons, Support Vector Machines and Guassian Mixture Models. https://arxiv.org/abs/0705.0197

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