Dynamic System Based On Analytical Redundancy Fault Diagnosis Method

With the development of the technology and the improvement of productivity, the modern control systems are becoming larger and more complicated. The possibility of fault arising in system is also increasing. In order to improve the reliability and security of the system, it is becoming urgent to building a monitoring system. The monitoring system is used to monitor the system states, detect the fault in system in time, analyze and judge the reason and characteristics of the fault, and take some required measures to avoid catastrophic accident. This research on fault detection and diagnosis for control system is becoming more and more important and calling much more attention.The main content studied in this dissertation is 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 system without disturbance, it can correctly detect and isolate the fault.Secondly, an approach to the robust fault detection filter based on unknown input observer is discussed. This method improves the robustness, which is the limitation of the method on Beard fault detection filter. To further discuss the identification of fault after the detection and isolation, the approach based on adaptive observer is presented.Finally, an approach on neural network predictor for nonlinear system is discussed. The method achieves the task of the detection and identification of fault in the nonlinear system.All the approaches discussed in this dissertation are demonstrated through corresponding simulations.