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

Abstract

Advanced methods of fault diagnosis become increasinglysignificant for improving the safety, reliability and efficiently of dynamic systems in various domains of industrial engineering. This paper reviews and comparesthree bond graph model-based methods for fault diagnosis. These methods are causality inversion method, augmented Analytical redundancy relation method, and fault estimationmethod. Thesemethods are applied toa simulation model of an electricalsystem. This latteris used to simulate the system variables in both normal and faulty situationsand to generate residuals for fault detection and isolation. The results of the case study are compared for highlighting the fault diagnosisperformanceand capability of a method over another.The result showsthat the faultestimationmethodhas a better diagnosis performance when compared to the other methods