A remaining useful life prediction method for airborne fuel pump after maintenance

Abstract

Remaining useful life prediction is the core of condition-based maintenance under the technology framework of prognostic and health management. But the remaining useful life of airborne fuel pump after maintenance is difficult to predict because of the multi-stage noise and small data size. A new method is proposed to solve the remaining useful life prediction of repaired fuel pump. Firstly, an alternative smooth transition auto-regression model logistic smooth transition auto-regression or exponential smooth transition auto-regression is proposed to reduce the multi-stage noise. Secondly, random effect Wiener process is utilized to model the de-noised degradation data, and the posterior parameters of remaining useful life prediction after maintenance are derived by the Bayesian method based on the parameters before maintenance. Finally, the method proposed above is compared with the methods which neglect the multi-stage noise and information before maintenance, comparative results show that the proposed method can improve the remaining useful life prediction accuracy significantly.

Performance degradation prediction of aeroengine based on attention model and support vector regression

“…The commonly used regression analysis models based on statistical analysis include logical regression, multiple linear regression (MLR), and polynomial regression. 9-11 With the increase of data volume and data complexity, machine learning methods are widely used in regression analysis, such as radial basis function neural networks (RBF), random forest regression, decision tree regression, and AdaBoost regression. 12-15 Because the performance degradation is mainly obtained by random distribution fitting based on engineering experience, those regression analysis models which take sample label error as loss function are not optimal for performance degradation prediction of the aeroengine.…”