Affiliations: [a] College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, China
| [b] Key Laboratory of Industrial Process Control of Gansu Province, Lanzhou, China
| [c]
National Experimental Teaching Demonstration Center for Electrical and Control Engineering, Lanzhou University of Technology, Lanzhou, China
Correspondence:
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Corresponding author. Ping Yu, Lanzhou University of Technology, Lanzhou, China. E-mail: [email protected].
Abstract: In order to address the timing problem, invalid data problem and deep feature extraction problem in the current deep learning based aero-engine remaining life prediction, a remaining life prediction method based on time-series residual neural networks is proposed. This method uses a combination of temporal feature extraction layer and deep feature extraction layer to build the network model. First, the temporal feature extraction layer with multi-head structure is used to extract rich temporal features; then, the spatial attention mechanism is applied to improve the weights of important data; finally, the deep feature extraction layer is used to process the deep features of the data. To verify the effectiveness of the proposed method, experiments are conducted on the C-MAPSS dataset provided by NASA. The experimental results show that the method proposed in this paper can make accurate predictions of the remaining service life under different sub-datasets and has outstanding performance advantages in comparison with other outstanding networks.
Keywords: Time sequential resnet, temporal feature extraction layer, spatial attention module, deep feature extraction layer, remaining useful life Introduction
