注意力ConvLSTM模型在RUL预测中的应用

预测性维护的应用能够极大地降低企业运维成本,而设备剩余使用寿命(Remaining Useful Life,RUL)预测是预测性维护的关键技术之一.针对传统RUL预测算法难以提取时序数据的潜藏特征以及特征权重分配不合理的问题,本文提出一种基于注意力机制(Attention Mechanism)的卷积长短时记忆(Conv...

Application of Attention-ConvLSTM Model for Remaining Useful Life Prediction

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 Long-Short 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 long-term time series,and introduce the attention mechanism to significantly increase the weight of feature factors,which greatly optimizes the space-time 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.