Commercial aircraft engine post-repairing performance prediction based on fusion of multisource data

To solve the problem of multisource heterogeneous data fusion in commercial aircraft engine post-repairing exhaust gas temperature margin prediction, a combined method of convolutional auto-encoder and extreme gradient boost model was proposed. This method uses the proposed cross entropy increasing factor to regularize the parameter order in the multi-dimensional engine sensor parameter series observed before repairing, and then uses convolutional auto-encoder to extract features from the regularized parameter series and engine workscope data. With the combined feature composed of the extracted features and the features representing engine using time, extreme gradient boost model is trained in order to predict engine post-repairing performance and estimate the importance of influential factors. The experiment performed on the prediction of the post-repairing performance of an engine fleet proved that the proposed method achieves higher prediction precision than prediction methods supported by one-dimentional parameter series and can predict engine post-repairing exhaust gas temperature margin with an average relative error no higher than 8.3%.