Diagnosis of systems using linear hybrid automata models
Thiziri Boumezirene, Latéfa Ghomri · 2019
The Fault diagnosis leads to answer to two functions: default detection and isolation-localization. Default detection informs us whether or not a default has occurred in the system. If this default has occurred, then the isolation and localization function aims to identify the component that caused the default. The diagnostic problem has been widely studied for autonomous and timed discrete event systems. In this paper, we consider a subclass of hybrid dynamic systems: That is the subclass of systems that we model using linear hybrid automata. For This class of hybrid systems, we propose diagnostic approach based on the offline synthesis of a diagnoser. Very few works have been devoted to solve this problem. We explain the approach and conditions of construction of the diagnoser. Then, we add a new condition allowing us to detect defaults more efficiently.