Health estimation of fan based on belief-rule-base expert system in turbofan engine gas-path

Abstract

Fan is an important rotating part in turbofan engine gas-path. The health condition of fan has a great impact on the health status of the whole aero-engine. Based on belief-rule-base, a novel health estimation model is proposed for fan in turbofan engine gas-path. In this model, the health condition of fan is reflected by the observable information which can represent the system health. In the process of health estimation, the expert knowledge is used fully to improve the precision and speed of the estimation. In the initial health estimation model, some parameters given by expert may not be accurate. To obtain the accurate estimation result, an algorithm for updating the parameters is proposed based on differential evolution algorithm. In order to verify the feasibility and accuracy of the proposed model, back-propagation neural network is applied to comparison. The newly proposed model is applied to an actual test in the aero-engine test bed, which is used to testify the validity of the health estimation model. This model can also provide a reference for the health estimation of turbofan engine gas-path.