| Citation: | CHEN Baojia, GUO Kaimin, CHEN Fafa, et al. Prediction of remaining useful life of aero-engine based on residual NLSTM neural network and attention mechanism[J]. Journal of Aerospace Power, 2023, 38（5）：1176-1184 doi: 10.13224/j.cnki.jasp.20210728
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The remaining useful life prediction method based on attention mechanism and residual nested long-short-term memory （NLSTM） neural network was employed to address the shortcomings of traditional long-short-term memory （LSTM） neural network in the recognition and extraction of multi-dimensional data features. Two NLSTM neural network layers were used to replace the main structure of the residual block, and the shortcut connection of the one-dimensional convolutional network in the residual block was retained, which can fully extract the temporal feature and use the jump layer to transfer useful data in the network layer. The method also added the attention mechanism to construct multilayer network, and the important information influencing the result of prediction can be chosen to improve the prediction accuracy by the attention mechanism. The proposed method was verified by experiments in the aero-engines degradation dataset. The results showed that the method can effectively establish the relationship between the monitoring data and the engines health. The prediction error was reduced by 10.8% compared with the method without residual structure and reduced 18.9% compared with the method without attention mechanism, which improved the prediction accuracy effectively.
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