Transactions of Nanjing University of Aeronautics & Astronautics
Attention-Based Multi-scale CNN and LSTM Model for Remaining Useful Life Estimation
- Article
- Figures
- Metrics
- Preview PDF
- Reference
- Related
- Cited by
- Materials
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.
Keywords:
Project Supported:
This work was supported by the National Key Research and Development Program of China （2023YFB4302403）， and the Research and Practical Innovation Program of NUAA （xcxjh20230735）.
Clc Number:
V240.2
Articles
Mobile website
