Support vector machines for fault detection

Abstract

Support vector machines (SVMs), based on Vapnik's statistical learning theory is a new tool that can be used for fault detection and isolation in dynamic systems. This paper presents a new approach that combines the system identification technique and the SVM learning algorithm for fault detection and isolation in dynamic systems. A conventional heat exchanger dynamics is used to illustrate the technique.