A Knowledge-based Signal-symbol Processing Approach for Change Detection

Abstract

Recently, computerized fault diagnostic systems have been applied to catch up and localize incipient failures in complex plants. The early indication of abmpt changes in the dynamic properties of aignals is a critical issue in the operation of such systems. To date, systems performing change detection functions effectively, have generally required the continuous, constructive interaction with the skilled analyst for intelligent assistance in commissioning signal processing activities. Quite recently, remarkable efforts have been made to automate the nontrivial decision-making activities of the human operator in this field. In this paper we describe a knowledge-based approach to solve the autonomous, change detection problem. The focus of the paper is on the expert skill-based decision process because this is the critical but nevertheless neglected, aspect in the realization of knowledge-based signal processing systems. In the framework of a combined expert-decision support system (ES/DSS) we aim to provide a methodology to embody expert trategies as control knowledge and signal-symbol processing skills as domain knowledge applied to the change detection task. The overall scheme and behavior of the system is briefly reviewed.