It is challenging to have a good fault diagnostic scheme that can distinguish between model uncertainties and occurrence of faults, which helps in reducing false alarms and missed detections. In this paper, a dynamic threshold algorithm is developed for aircraft engine sensor fault diagnosis that accommodates parametric uncertainties. Using the robustness analysis of parametric uncertain systems, we generate upper-and-lower bound trajectories for the dynamic threshold. The extent of parametric uncertainties is assumed to be such that the perturbed eigenvalues reside in a set of distinct circular regions. Dedicated observer scheme is used for engine sensor fault diagnosis design. The residuals are errors between estimated state variables from a bank of Kalman filters. With this design approach, the residual crossing the upper-and-lower bounds of the dynamic threshold indicates the occurrence of fault. Application to an aircraft gas turbine engine Component Level Model clearly illustrates the improved performance of the proposed method.

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