Application of AttentionConvLSTM Model for Remaining Useful Life Prediction
CHENG Cheng1，ZHANG Beike1，GAO Dong1，XU Xin2
1（College of Information Science and Technology，Beijing University of Chemical Technology，Beijing 100029，China）2（Beijing Digital Process Technology Co.Ltd.，Beijing 100029，China）
Abstract：The application of predictive maintenance technology can greatly reduce the operation and maintenance costs of enterprises，and the remaining useful life （RUL） prediction of equipment is one of the key technologies of predictive maintenance.Aiming at the problem that the traditional RUL prediction algorithm is difficult to extract the hidden features of the time series data and the distribution of feature weights is unreasonable，this paper proposes a Convolution LongShort Term Memory （ConvLSTM） prediction model based on the Attention Mechanism.This model makes full use of the advantages of LSTM networks to process and predict longterm time series，and introduce the attention mechanism to significantly increase the weight of feature factors，which greatly optimizes the spacetime feature extraction capability of the model.In order to verify the prediction effect of the model，this paper uses the CMAPSS data set provided by NASA as an experiment，which take root mean square error （Root Mean Squared Error，RMSE） and the score of the data set as evaluation indicators.The prediction results are compared with other RUL prediction algorithms，which proves that the model has better prediction accuracy.
程成,张贝克,高东,许欣. 注意力ConvLSTM模型在RUL预测中的应用[J]. 小型微型计算机系统, 2021, 42(2): 443-448.
CHENG Cheng,ZHANG Beike,GAO Dong,XU Xin. Application of AttentionConvLSTM Model for Remaining Useful Life Prediction. Journal of Chinese Computer Systems, 2021, 42(2): 443-448.
2021, Vol. 42 
