基于改进生成对抗网络与ConvLSTM的航空发动机剩余寿命预测方法  

Aeroengine residual life prediction method based on improved generative adversarial network and ConvLSTM

作　　者：陈维兴 常东润 李宗帅 Chen Weixing;Chang Dongrun;Li Zongshuai(College of Electronic Information and Automation,Civil Aviation University of China,Tianjin 300300,China)

机构地区：[1]中国民航大学电子信息与自动化学院,天津300300

出　　处：《电子测量与仪器学报》2023年第3期211-221,共11页Journal of Electronic Measurement and Instrumentation

基　　金：国家自然科学基金委员会-中国民航联合研究基金(U1933107);天津市教委自然科学科研基金(2018KJ237);中央高校基本科研业务费民航大学专项(3122020025)项目资助。

摘　　要：针对航空发动机运行周期内故障数据难以采集而造成的数据失衡等问题,提出一种基于Wasserstein距离与梯度惩罚措施的条件生成对抗网络与卷积长短时记忆网络相结合的预测模型。首先,使用WCGAN-GP模型学习预处理后的时序数据的深层分布特征;然后,利用生成器生成故障样本并与真实样本混合,作为训练集输入到基于ConvLSTM网络的预测模型中进行训练。基于C-MAPSS数据集开展验证比较,结果表明:与单一真实样本训练预测模型相比,使用混合数据时性能指标RMSE和Score平均下降了12.65%和48.95%。A prediction model based on Wasserstein conditional generative adversarial network-gradient penalty(WCGAN-GP)and convolution long and short-term memory network(ConvLSTM)is proposed to address the problem of unbalanced data caused by the difficulty of collecting fault data during the operating cycle of an aero-engine.First,a WCGAN-GP model is used to learn the deep distribution characteristics of the pre-processed time-series data;then,a generator is used to generate fault samples and mix them with real samples as a training set to input into the prediction model based on the ConvLSTM network for training.Through testing with CMAPSS data set,the results show that compared with the single real sample training prediction model,the performance indexes RMSE and score of the model using mixed data are reduced by 12.65%and 48.95%on average.

关 键 词：航空发动机 Wasserstein距离 梯度惩罚项 条件式生成对抗网络 剩余寿命预测 

分 类 号：TN0[电子电信—物理电子学] TP183[自动化与计算机技术—控制理论与控制工程]