An application of fuzzy logic and Dempster-Shafer theory to failure detection and identification

Abstract

An approach to failure detection and identification (FDI) is proposed which 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 use 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. The FDI algorithm has been applied successfully to the detection of rotating stall and surge instabilities in axial flow compressors.<>