Remaining Useful Life Prediction via Improved CNN, GRU and Residual Attention Mechanism With Soft Thresholding
Lijie Zhang, Bin Wang, Xiaoming Yuan, Pengfei Liang
Abstract
Accurate prediction of the remaining useful life (RUL) of mechanical equipment has brought benefits of the company’s reasonable maintenance. However, in real industrial applications, owing to the change of working conditions and the interference of environment noise, it is of great difficulty to extract useful features from the collected signals, making it quite challenging to achieve high-precision RUL prediction of mechanical equipment. In order to overcome these issues, a new high-precision prediction method based on improved convolutional neural network (ICNN), residual attention mechanism with soft thresholding and gated recurrent unit (GRU) is proposed in this paper. To begin with, this method solves the limitations of manual feature extraction and makes the extracted feature representation more obvious by integrating one-dimensional depth separable convolution neural network and two-dimensional transpose convolution neural network. Then, soft thresholding and residual connection is inserted into the attention mechanism to help improve the prediction performance of RUL in noisy environment, making the prediction model adaptively set different thresholds for each sample according to its conditions and characteristics. Finally, the effectiveness of the proposed approach is verified by simulating turbofan engine and IEEE phm2010 data set, indicating that our proposed method has better performance than other approaches of literatures in prediction accuracy and time cost.