Fault detection and diagnosis for stochastic distribution systems using a rational square-root approximation model

Stochastic distribution control (SDC) systems are a group of systems where the outputs considered are the measured probability density functions (PDFs) of the system output whilst subjected, to a normal crisp input. The purpose of the fault detection and diagnosis of such systems is to use the measured input and the system output PDFs to obtain possible faults information of the system. In this paper the rational square-root B-spline model is used to represent the dynamics between the output PDF and the input, where comparisons of such a model with respect to other approximation models are discussed first. This is then followed by the novel design of a nonlinear adaptive observer-based fault diagnosis. algorithm so as to diagnose the fault in the dynamic part of such systems. Convergency analysis is performed for the error dynamics raised from the fault detection and diagnosis phase and a simulated example is given to illustrate the efficiency of the proposed algorithm.