Fault Diagnosis of Nonlinear Systems using a Hybrid-Degree Dual Cubature-based Estimation Scheme

In this paper a novel hybrid-degree dual cubature-based nonlinear filtering methodology is proposed for fault diagnosis of nonlinear systems subject to multiplicative component faults. Distinct from conventional dual estimation schemes, the nonlinear functions are approximated with cubature rules to achieve a designated and case-dependent degree of accuracy. Our methodology is motivated from two primary observations: (i) dynamic characteristics of system states and parameters generally are distinct and posses different degrees of complexities, and (ii) performance of cubature rules depend on the system dynamics and vary when approximate high-dimensional integrations are utilized. The boundedness of the estimation error covariance and stability analysis are formally investigated in presence of approximation errors due to cubature rules, uncertainties, and noise. The effectiveness of our proposed methodology is evaluated by application to a gas turbine engine for addressing the multi-mode component fault diagnosis problem within an integrated fault detection, isolation and identification framework. Case studies are provided to substantiate the superiority of the proposed methodology when compared with those of other representative filters including the Unscented Kalman Filters (UKF) and Particle Filters (PF).