Improved bond graph model for fault diagnosis under uncertain conditions

Abstract

The effectiveness of model-based diagnosis strongly depends on the model’s authenticity and is impacted by various uncertainties. Measurement uncertainty is governed by the probability method, and parameter uncertainty can be handled by the linear fractional transformation, whereas structure uncertainty and errors are rarely considered. In this paper, an improved bond graph (BG) model is proposed, which adopts subsystems to substitute modelling errors, namely parameter uncertainty and structure uncertainty. A multi-dimensional Fibonacci optimization algorithm is developed to identify the parameters of subsystems to obtain the subsystem-based diagnostic hybrid BG (SDHBG) model. Fault diagnosis is realized by comparing the residuals and thresholds derived from the SDHBG. Experiments are conducted to validate the key concepts of the proposed methods. Subsequently, the results suggest its effectiveness.