关键词: 深度学习, 剩余使用寿命, 时间卷积神经网络, 注意力机制

Abstract: Remaining useful life (RUL) prediction is of great significance for ensuring the safe operation of modern industrial equipment and reducing maintenance costs.At present,the existing RUL models based on recurrent neural networks are complex in structure and lack an effective mechanism to extract important degradation information from multi-sensor data.The new Temporal Convolutional Attention Network (TCAN) model was proposed for RUL estimation.A Temporal Convolutional Neural (TCN) network with a simple structure was used in  TCAN to extract the degradation features from the sensor data,and then the attention mechanism was used to extract the important degradation information.The learned high-level feature representation was flattened and fed into a fully connected layer to output the predicted RUL.Compared with other methods on the C-MAPSS dataset,the experimental results showed that the TCAN could more effectively improve the accuracy of remaining life prediction.

Key words: deep learning, remaining useful life, temporal convolutional network, attention mechanism