Fuel savings model after aero-engine washing based on convolutional neural network prediction

Abstract

In order to accurately calculate the fuel savings after aero-engine washing, therefore the engine cleaning process flow can be arranged more scientifically and reasonably. In this paper, a derivation method of fuel savings model after engine washing based on delta fuel flow (DFF) degradation baseline and convolutional neural network prediction technology is proposed. Firstly, the fuel savings model of cruise phase is established based on gas path parameters related to the engine fuel consumption, and the QAR data’s trend on entire flight is mined, and the regularity of the gas path parameters in the cruise phase is extended to the entire flight, thereby the fuel savings model after engine washing of whole Flight is derived. In the model derivation process, the unwashed engine’s DFF degradation baseline after the wash point is predicted. According to the characteristics of DFF discrete time series, a discrete input process neural network prediction model based on discrete data convolution operation is established. The convolution operation of discrete data is used to replace the integral operation of continuous function, which effectively avoids the loss of fitting precision caused by the smooth processing of discrete time series data before the input of the process neural network model, and improves the prediction accuracy of discrete time series data. A denoising method combining Singular Value Decomposition (SVD) and Empirical Mode Decomposition (EMD) is used to denoise the DFF before and after engine washing, and the trend analysis method is used to solve the intersection of the unwashed state engine’s DFF degradation baseline and washed state engine’s DFF degradation baseline, thereby the washing effect cycle numbers is predicted, and in which the total fuel savings model of whole fleet after engine washing is derived by accumulating every flight fuel savings. The post-washed engine fuel saving calculation of an airline is used as a verification experiment. The results show that post-washed engine fuel saving prediction model presented in the paper is the closest to the actual fuel savings model.