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