文献类型： 外文期刊

作者： Chen Wenbai ¹ ; Liu Chang ² ; Chen Weizhao ¹ ; Liu Huixiang ¹ ; Chen Qili ¹ ; Wu Peiliang ³ ;

作者机构： 1.Beijing Informat Sci & Technol Univ, Sch Automat, Beijing 100101, Peoples R China

2.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

3.Yanshan Univ, Sch Informat Sci & Technol, Qinhuangdao 066004, Hebei, Peoples R China

期刊名称：COMPLEXITY （ 影响因子：2.121； 五年影响因子：2.213 ）

ISSN： 1076-2787

年卷期： 2021 年 2021 卷

页码：

收录情况： SCI

摘要： We present a prediction framework to estimate the remaining useful life (RUL) of equipment based on the generative adversarial imputation net (GAIN) and multiscale deep convolutional neural network and long short-term memory (MSDCNN-LSTM). The method we proposed addresses the problem of missing data caused by sensor failures in engineering applications. First, a binary matrix is used to adjust the proportion of "0" to simulate the number of missing data in the engineering environment. Then, the GAIN model is used to impute the missing data and approximate the true sample distribution. Finally, the MSDCNN-LSTM model is used for RUL prediction. Experiments are carried out on the commercial modular aero-propulsion system simulation (C-MAPSS) dataset to validate the proposed method. The prediction results show that the proposed method outperforms other methods when packet loss occurs, showing significant improvements in the root mean square error (RMSE) and the score function value.