| Citation: | DUAN Jiajun, LU Zhong, WANG Jie. Aero-engine life prediction method based on multi-scale CNN and Transformer[J]. Journal of Aerospace Power, 2026, 41（3）：20240311 doi: 10.13224/j.cnki.jasp.20240311
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Multi-scale degradation features are seldom considered in remaining life prediction for aero-engine, and it is inefficient in dealing with time-series data with long-term dependencies. A remaining life prediction method is proposed based on multi-scale CNN and Transformer. A multi-scale feature extraction module is designed by traditional CNN, which extracts and integrates degradation features at different scales. A feature connection module is constructed to make up for the shortage that traditional feature extraction module can only extract features from the same level. The learning of long-term dependency features for time-series data is realized based on the position encoding, the multi-head self-attention mechanism and the feed-forward neural network of the Transformer. The parallel computing of the multi-head attention mechanism is utilized to reduce network runtime. Experimental validation is carried out on the C-MAPSS dataset, and the results show that our proposed method has less error compared with the network without extracting multi-scale features. Compared with RNN, LSTM and GRU, the operation time is reduced by 65.77%, 48.41%, and 45.02%, respectively.
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