| Citation: | WANG Xiaopeng, WANG Lei, HAN Xiaowei, et al. Dual-channel remaining useful life prediction method based on dilated convolution and regression features[J]. Journal of Aerospace Power, 2025, 40（9）：20240043 doi: 10.13224/j.cnki.jasp.20240043
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A two-channel aero-engine remaining useful life (RUL) prediction model was developed. The first channel utilized dilation convolution and incorporated the attention mechanism, which decreased the model parameters and enhanced the representation of crucial feature information of RUL. The second channel employed average regression features to denoise and smooth the original signal, reducing the influence of noise on RUL prediction. Ultimately, the features from both channels were combined and fed into the fully connected network to generate the ultimate RUL prediction results. In order to verify the effectiveness of the model, experiments were conducted on the C-MAPSS (commercial modular aero propulsion system simulation) dataset published by National Aeronautics and Space Administration. It was shown that the root-mean-square error of this method was reduced by 32.1%, 7.8%, and 6.3% compared with the CNN-LSTM (convolutional neural network & long short-term memory network), AdaBN-DCNN (adaptive batch normalization-deep convolutional neural network) and RCNN-ABi-LSTM (region-based convolutional neural network & attention bi-directional long short-term memory network) models, respectively. Additionally, the prediction accuracy of the two-channel model was significantly enhanced.
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