南京航空航天大学学报（英文版）2021，Vol.38Issue(5)：867-875,9.
基于主成分分析和一维卷积神经网络的航空发动机剩余寿命预测
Remaining Useful Life Prediction of Aeroengine Based on Principal Component Analysis and One?Dimensional Convolutional Neural Network
摘要
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.关键词
航空发动机/剩余使用寿命/主成分分析/一维卷积神经网络/时间序列预测/状态参数Key words
aeroengine/remaining useful life (RUL)/principal component analysis(PCA)/one-dimensional convolution neural network(1D-CNN)/time series prediction/state parameters分类
航空航天引用本文复制引用
吕德峰,胡煜雯..基于主成分分析和一维卷积神经网络的航空发动机剩余寿命预测[J].南京航空航天大学学报（英文版）,2021,38(5):867-875,9.基金项目
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). （No.20GLD008）
