Bond Graph Approach for Plant Fault Detection and Isolation: Application to Intelligent Autonomous Vehicle

The present paper deals with bond graph model-based for structural component fault detection and isolation. The structural conditions of fault detectability and isolability are obtained directly from the bond graph using the properties of the bicausality and the causal path. It is shown that the monitorability analysis using bond graph is automatically deduced using this unified tool, with respect to the detectability and isolability conditions. A real mechatronic system application of intelligent autonomous vehicle is given to show the efficiency and the simplicity analysis of the proposed approach. This paper was motivated by the problem of integrated design of a fault diagnosis system by considering both, system instrumentation and the set of specifications regarding faults. Existing methods dealing with such problems are based mainly on the existing system instrumentation. In this paper, a fault diagnosis system study and analysis is proposed. This is done by using a unified graphical tool such as Bond Graph tool which is used for system modeling, structural analysis and fault diagnosis conclusions. Therefore, system modeling, fault monitorability analysis, and fault indicator generation are all performed by using the same graphical tool. In addition, the proposed method may be exploited for monitorability analysis before industrial design, i.e., ability to detect and isolate faults with given instrumentation architecture and how to make faulty components monitorable by adding new sensors. To show the effectiveness of the proposed method, an application on real mechatronic system is considered.