Fault diagnosis for non-Gaussian stochastic distribution systems using iterative learning observer

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 diagnosis of such systems is to use the measured input and the system output PDFs to obtain possible fault 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. The proposed approach relies on an iterative learning observer (ILO) for fault estimation. The fault may be constant, slow-varying or fast-varying. Convergency analysis is performed for the error dynamics raised from the fault diagnosis phase and simulated examples are given to show the effectiveness of the proposed algorithm.