南京航空航天大学学报（英文版）2025，Vol.42Issue(z1)：64-77,14.DOI:10.16356/j.1005-1120.2025.S.006
基于注意力机制的多尺度CNN与LSTM网络及在剩余使用寿命预测中的应用
Attention-Based Multi-scale CNN and LSTM Model for Remaining Useful Life Estimation
摘要
Abstract
Current aero-engine life prediction areas typically focus on single-scale degradation features,and the existing methods are not comprehensive enough to capture the relationship within time series data.To address this problem,we propose a novel remaining useful life(RUL)estimation method based on the attention mechanism.Our approach designs a two-layer multi-scale feature extraction module that integrates degradation features at different scales.These features are then processed in parallel by a self-attention module and a three-layer long short-term memory(LSTM)network,which together capture long-term dependencies and adaptively weigh important feature.The integration of degradation patterns from both components into the attention module enhances the model's ability to capture long-term dependencies.Visualizing the attention module's weight matrices further improves model interpretability.Experimental results on the C-MAPSS dataset demonstrate that our approach outperforms the existing state-of-the-art methods.关键词
注意力机制/卷积神经网络/长短期记忆网络/多尺度特征提取Key words
attention mechanism/convolutional neural network(CNN)/long short-term memory(LSTM)/multi-scale feature extraction分类
航空航天引用本文复制引用
段佳俊,陆中,杜志强..基于注意力机制的多尺度CNN与LSTM网络及在剩余使用寿命预测中的应用[J].南京航空航天大学学报（英文版）,2025,42(z1):64-77,14.基金项目
This work was supported by the Na-tional Key Research and Development Program of China(2023YFB4302403),and the Research and Practical Innova-tion Program of NUAA(xcxjh20230735). （2023YFB4302403）
