Robust Fault Detection Method Of The Dynamic System

With the development of the technology and the improvement of productivity, the modern control systems are becoming larger and more complicated. The demand for high reliability and safety in modern industry increase rapidly, Fault detection and diagnosis(FDD) algorithms and their applications have been attracted much more attention over the past thirty years. In this thesis, fault detection and diagnosis strategies for linear and nonlinear systems with modeling uncertainties are investigated.The main contents studied in this thesis are as follow:Firstly, the latest development of fault detection and diagnosis is briefly introduced. The basic fault diagnosis approach based on Beard fault detection filter is discussed in the beginning. When it is used in the diagnosis of the linear system without disturbance, it can detect and isolate the fault accurately. Secondly, an approach to the robust fault detection filter for the linear system with unknown input is studied. This method improves the robustness, which is the limitation of Beard fault detection filter. Finally, In order to identify the value of fault after the detection, a nonlinear online neural network approximator is used to provide an estimation of the fault, the problem of robustness, sensitivity and stability conditions are rigorously investigated.All the approaches discussed in this thesis are demonstrated to be affective through corresponding simulations.