Residual lifetime prediction of aero engines based on multi scale fusion prediction models

DOI:	10.11809/bqzbgcxb2023.07.039
Keywords:	residual lifetime; gated residual mechanism; static covariate encoders; LSTM neural network; multi head attention mechanism; quantile
Abstract:	For most data driven aero engine remaining life prediction methods, the relationship between its degradation process and complex input data is not broken down to accurately identify and extract key features. In this view, this paper proposes a method for predicting the residual lifetime of aero engines based on a multi scale fusion (MSF) prediction model. This method uses the static covariate coding network (SCCN) and variable selection network (VSN) to select features for the input data type, and connects the static covariates generated by SCCN to different positions of the model so as to improve the model ability to capture the temporal features of different scales and integrate the gated residual mechanism to build the basic framework of the model. It can not only improve the adaptability of the model but also ensure the efficiency of information transmission in the network. Besides, it uses quantile error as the loss function to achieve multi scale prediction, effectively improving accuracy of the prediction. The experimental analysis on the CMAPSS turbofan engine dataset shows that the prediction accuracy of FD002 and FD004 test sets reaches 91.9% and 92.4% respectively. Compared with other deep learning methods, the RMSE optimal values increase by 15.54% and 16.91% respectively, and the optimal Score values increase by 83.21% and 78.78% respectively.
Issue:	Vol. 44 No. 7 (2023)
Published:	2023-07-28
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