Managing ignorance and uncertainty in system fault detection and identification

Abstract

A new approach to failure detection and identification (FDI) is proposed in order to address restructurable control systems. The methodology combines an analytic estimation method and an intelligent identification scheme in such a way that sensitivity to true failure modes is enhanced, while the possibility of false alarms is reduced. The authors employ a real-time recursive parameter estimation algorithm with covariance resetting, which triggers the FDI routine only when potential failure modes are anticipated. A possibilistic scheme based on fuzzy set theory is applied to the identification part of the FDI algorithm with computational efficiency. At the final stage of the algorithm, an index is computed-the degree of certainty-based on Dempster-Shafer theory, which measures the reliability of the decision. Simple simulation results demonstrate the effectiveness of the algorithm in managing uncertainty and ignorance.<>