# Fault Classification In Cylinders Using Multilayer Perceptrons, Support Vector Machines And Guassian Mixture Models

Tshilidzi Marwalaa, Unathi Maholaa **and Snehashish Chakraverty**b a
*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 b**Central Building Research Institute** 
Roorkee-247 667, U.A. India e-mail: sne_chak@yahoo.com 

## 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. 

## 1. Introduction

Vibration data have been used with varying degrees of success to classify damage in structures [1]. In the fault classification process there are various stages involved and these are: data extraction, data processing, data analysis and fault classification. Data extraction process involves the choice of data to be extracted and the method of extraction. Data that have been used for fault classification include strains concentration in structures and vibration data where strain gauges and accelerometers are used respectively [1]. In this paper vibration data processed using modal analysis are used for fault classification. In the data processing stage the measured vibration data need to be processed. This is mainly due to the fact that the measured vibration data, which are in the time domain, are difficult to use in raw form. Thus far the time-domain vibration data may be transformed to the modal analysis, frequency domain analysis and time-frequency domain [2,3]. In this paper the time-domain vibration data set is transformed into the modal domain where it is represented as natural frequencies and mode shapes. The data processed need to be analysed and the general trend has been to automate the analysis process and thus automate the fault classification process. To achieve this goal intelligent pattern recognition process needs to be employed and methods such as neural networks have been widely applied [1]. There are many types of neural networks that have been employed and these include multi-layer perceptron (MLP), radial basis function (RBF) and Bayesian neural networks [4,5]. Recently, new pattern recognition methods called support vector machines (SVMs) and Gaussian mixture models (GMMs) have been proposed and found to be particularly suited to classification problems [6]. SVMs have been found to outperform neural networks [7]. One of the examples where the fault classification process summarized at the beginning of this paper has been implemented is fault classification in a population of nominally identical cylindrical 