Bond graph Model-Based Methods for Fault Diagnosis: A Comparative Study

Abstract

Advanced methods of fault diagnosis become increasingly significant for improving the safety, reliability and efficiently of dynamic systems in various domains of industrial engineering. This paper reviews and compares between three bond graph model-based methods for fault diagnosis. These methods are causality inversion method, augmented Analytical redundancy relation method, and fault/residual sensitivity relation method. These methods are applied on a simulation model of an electrical system. This latter is used to simulate the system variables in both normal and faulty situations and to generate residuals for fault detection and isolation. The results of the case study are compared for highlighting the fault diagnosis performance of a method over another. The result show that the fault/residual sensitivity relation method has batter diagnosis performance when compared to the other methods