Fault detection and diagnosis for stochastic systems via output PDFs

This paper investigates a new type of fault detection and diagnosis (FDD) problem for non-Gaussian stochastic distribution systems via the output probability density function (PDF). The PDF can be approximated by using square root B-spline expansions. In this framework, an optimal fault detection algorithm is presented by introducing the tuning parameter such that the residual is as sensitive as possible to the fault. When the fault occurs, an adaptive network parameter-updating law is designed to approximate the fault. At last, paper-making process example is given to demonstrate the efficiency of the proposed approach.