Note to Users

FAULT DIAGNOSIS OF HYBRID SYSTEMS WITH APPLICATIONS TO GAS TURBINE ENGINES Rasul Mohammadi, Ph.D. Concordia University, 2009 Stringent reliability and maintainability requirements for modern complex systems demand the development of systematic methods for fault detection and isolation. Many of such complex systems can be modeled as hybrid automata. In this thesis, a novel framework for fault diagnosis of hybrid automata is presented. Generally, in a hybrid system, two types of sensors may be available, namely: continuous sensors supplying continuous-time readings (i.e., real numbers) and threshold sensitive (discrete) sensors supplying discrete outputs (e.g., level high and pressure low). It is assumed that a bank of residual generators (detection filters) designed based on the continuous model of the plant is available. In the proposed framework, each residual generator is modeled by a Discrete-Event System (DES). Then, these DES models are integrated with the DES model of the hybrid system to build an Extended DES model. A "hybrid" diagnoser is then constructed based on the extended DES model. The "hybrid" diagnoser effectively combines the readings of discrete sensors and the information supplied by residual generators (which is based on continuous sensors) to determine the health status of the hybrid system.