Fault detection and diagnosis for non-Gaussian singular stochastic distribution systems

Abstract

Fault detection and diagnosis (FDD) for singular stochastic distribution control (SDC) systems via the output probability density functions(PDFs) have been discussed. The PDFs can be approximated via square-root B-spline expansion,and expansions to represent the dynamics weighting systems between the system input and output PDFs. an novel fault detection and diagnosis algorithm is presented using the parameter-updating. Finally, the simulation result is included to show that satisfactory robustness and closed-loop performance can be achieved.