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

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.