Performance monitoring of heavy duty gas turbines based on Bayesian and Dempster-Shafer theory

The topic of control performance assessment techniques has drawn lots of attentions and many performance assessment indices have been proposed. These indices are focused on certain malfunctions. Fallacious decision would result in if it is based on individual indices. Therefore, fusion of different indices can improve the assessment accuracy. In this paper, Bayesian and Dempster-Shafer theory are used individually to establish decision fusion strategy and tackle the performance assessment challenge of heavy duty gas turbine. To study the uncertainty effect on these methods, they are applied to the well-known Rowen model of gas turbine. The results illustrate the effectiveness of the proposed performance assessment method of the gas turbine model on the one hand, and the superiority of the Bayesian method when the uncertainty is low and that of Dempster-Shafer theory in the presence of uncertainty, on the other hand.