Bond Graph Model Based and Fuzzy Logic For Robust FDI of Mechatronic Systems

Abstract

Fault diagnosis is crucial for ensuring the safe operation of complex engineering systems and avoiding to execute an unsafe behaviour. This paper deals with robust decision making (RDM) for fault detection of an electromechanical system by combining the advantages of Bond Graph (BG) modelling and Fuzzy logic reasoning. The proposed fault diagnosis method is implemented in two stages. In the first stage, the residuals are deduced from the BG model allowing to build a Fault Signature Matrix (FSM) according to the sensitivity of residuals to different parameters. In the second stage, the result of FSM and the robust residual thresholds are used by the fuzzy reasoning mechanism in order to evaluate a degree of detectability for each set of components. Finally, in order to make robust decision according to the detected fault component, an analysis is done between the output variables of the fuzzy system and components having the same signature in the FSM. The performance of the proposed fault diagnosis methodology is demonstrated through experimental data of an omnidirectional robot.