DETECTION AND IDENTIFICATION OF AXIAL FLOW COMPRESSOR INSTABILITIES

A new approach to failure detection and identification is proposed that 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. We employ a real-time recursive parameter estimation algorithm with covariance resetting that triggers the fault detection and identification routine only when potential failure modes are anticipated. A possibilistic scheme based on fuzzy set theory is applied to the identification part of the algorithm with computational efficiency. At the final stage of the algorithm, an index is computed—the degree of certainty—bas ed on Dempster-Shafer theory, which measures the reliability of the decision. The proposed algorithm has been applied successfully to the detection of rotating stall and surge instabilities in axial flow compressors.

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