Fault diagnosis in dynamic systems: theory and application
Fault diagnosis in dynamic systems: theory and application
Automatica (Journal of IFAC)
Bounded Dynamic Stochastic Systems: Modelling and Control
Bounded Dynamic Stochastic Systems: Modelling and Control
On-board Component Fault Detection and Isolation Using the Statistical Local Approach
Automatica (Journal of IFAC)
Optimal stochastic fault detection filter
Automatica (Journal of IFAC)
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In this paper, the fault detection (FD) problem is studied for a class of NARMAX models with non-Gaussian disturbances and faults, as well as a time delay. Since generally (extended) Kalman filtering approaches are insufficient to characterize the non-Gaussian variables, entropy is adopted to describe the uncertainty of the error system. After a filter is constructed to generate the detected error, the FD problem is reduced to an entropy optimization problem. The design objective is to maximize the entropies of the stochastic detection errors when the faults occur, and to minimize the entropies of the stochastic estimation errors resulting from other stochastic noises. To improve the FD performance, a multi-step-ahead predictive nonlinear cumulative cost function is adopted rather than the instantaneous performance index. Following the formulation of the probability density function of the stochastic error in terms of those of both of the disturbances and the faults via a constructed mapping, new recursive approaches are established to calculate the entropies of the detection errors. Renyi's entropy has also been used to simplify the cost function. Finally, simulations are given to demonstrate the effectiveness of the proposed control algorithm.