Algorithm for Detection of Change Point in On-Line Monitoring Data

Abstract

The detection of abrupt changes in observed/estimated physical processes is an issue of concern in many applications. An algorithm for single change point detection in Gaussian white noise processes is presented in this paper. The algorithm progressively includes and analyzes the observed data points, and hence is suitable for detection of change point in on-line monitoring data. Two example problems are presented, wherein Monte Carlo simulation technique is used, to illustrate the proposed algorithm. The performance of the algorithm with respect to changes in shift amplitude, intensity of the signal, and rate of sampling are studied, and the observed trends indicate that the proposed algorithm possesses the desirable properties of change detection algorithms.