Transactions of Nanjing University of Aeronautics & Astronautics
Remaining Useful Life Prediction of Aeroengine Based on Principal Component Analysis and One-Dimensional Convolutional Neural Network
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Abstract:
In order to directly construct the mapping between multiple state parameters and remaining useful life （RUL）， and reduce the interference of random error on prediction accuracy， a RUL prediction model of aeroengine based on principal component analysis （PCA） and one-dimensional convolution neural network （1D-CNN） is proposed in this paper. Firstly， multiple state parameters corresponding to massive cycles of aeroengine are collected and brought into PCA for dimensionality reduction， and principal components are extracted for further time series prediction. Secondly， the 1D-CNN model is constructed to directly study the mapping between principal components and RUL. Multiple convolution and pooling operations are applied for deep feature extraction， and the end-to-end RUL prediction of aeroengine can be realized. Experimental results show that the most effective principal component from the multiple state parameters can be obtained by PCA， and the long time series of multiple state parameters can be directly mapped to RUL by 1D-CNN， so as to improve the efficiency and accuracy of RUL prediction. Compared with other traditional models， the proposed method also has lower prediction error and better robustness.
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This work was supported by Jiangsu Social Science Foundation （No.20GLD008） and Science， Technology Projects of Jiangsu Provincial Department of Communications （No.2020Y14） and Joint Fund for Civil Aviation Research （No.U1933202）.
Clc Number:
V267.3
CHINA AERO-SCIENCES
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