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

• 作 者: 吕德峰 胡煜雯
• 机 构: 南京航空航天大学民航学院,南京 211106,中国;江苏省药品监督管理局审核查验中心,南京 210019,中国
• 出 处: 南京航空航天大学学报（英文版）
• 发表日期: 2021年5期
• 关键词: aeroengine remaining useful life (RUL) principal component analysis(PCA) one-dim

为了直接构造多个状态参数与剩余使用寿命之间的映射关系,减少随机误差对预测精度的干扰,提出了一种基于主成分分析(Principal component analysis,PCA)和一维卷积神经网络(One?dimensional convolution neu?ral network,1D?CNN)的航空发动机剩余寿命预测模型.首先,采集航空发动机多个循环对应的多种状态参数,将其带入到主成分分析模型中降维,提取主成分用于进一步的时间序列预测.其次,建立1D?CNN模型,直接研究主成分与剩余使用寿命之间的映射关系.采用多重卷积和池化运算进行深度特征提取,实现航空发动机端到端剩余寿命预测.实验结果表明,PCA可以从多状态参数中提取最有效的主成分,而1D?CNN可以将多状态参数的长时间序列直接映射到剩余使用寿命,从而提高剩余使用寿命预测的效率和准确性.与其他传统模型相比,该方法具有较低的预测误差和较好的鲁棒性.“,”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.