文献类型： 外文期刊

第一作者： Chen Wenbai

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

作者机构：

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.

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