PROCESS FAULT DIAGNOSIS USING ONE-CLASS SUPPORT VECTOR MACHINES

Abstract

The process fault diagnosis problem is usually considered in classification framework. Although used widely in diagnostic applications, artificial neural networks and other nonlinear classifiers perform poorly under nonideal conditions encountered in practice, owing to the arbitrary placement of decision boundaries in empty regions of the input space and unbounded normal class region. This is particularly problematic where few and noisy data are available. In this paper, the use of one-class support vector machines for the diagnosis of process operations is proposed and their performance under practical conditions assessed. One-class classifiers are shown to be superior to and more robust than competing approaches previously proposed for diagnostic applications.