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Abstract For the purpose of fault detection and isolation (FDI), reconstruction-based contribution (RBC) analysis is carried out in a model-based way. A bank of adaptive observers are designed for a set of potential faults. From these observers, fault estimates and fault signatures are directly available, thus contribution functions are conveniently constructed to accomplish the FDI work. This integrated design of contribution analysis and adaptive observer takes advantages of both data-driven and model-based approaches, and the diagnosis performance is improved. Furthermore, quantitative isolability analysis is also studied by similarity measurement of the obtained fault signatures. Simulation study with a nonlinear unmanned aerial vehicle (UAV) model shows the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

A novel subspace tracking algorithm based on subspace distance is proposed in this paper. The proposed algorithm provides robust orthonormal vector estimation adapting to signal subspace and noise subspace. Simulation results demonstrate that the proposed algorithm is numerically stable, because it does not accumulate roundoff errors. Moreover, the proposed method performs well in subspace tracking and it has a low complexity of 3NL + O(N). [ABSTRACT FROM AUTHOR]