KDMFusion: Knowledge-Guided Dual-Path Multifeature Fusion Framework for Remaining Useful Life Prediction

Accurate remaining useful life (RUL) prediction for aircraft engines is essential for reducing maintenance costs and preventing failures. However, existing methods struggle to capture both spatial relationships and complex multifactor interactions, such as those between temperature and pressure. To address these challenges, we propose the knowledge-guided dual-path multifeature fusion (KDMFusion) framework that combines spatial-temporal feature extraction and multifeature fusion. A convolutional neural network (CNN) enhanced with domain knowledge captures spatial dependencies, while a gated recurrent unit (GRU) with a self-attention mechanism models long-term temporal relationships. By integrating spatial, temporal, and engineered features, our model offers a comprehensive representation of engine states, improving performance under varying operational conditions and noisy data. Extensive tests on the NASA C-MAPSS and N-CMAPSS datasets demonstrate the effectiveness of our method. On the C-MAPSS dataset, our approach reduces the root mean square error (RMSE) by at least 3.0% and the Score by at least 12.4%. Similarly, on the N-CMAPSS dataset, it decreases the RMSE by at least 4.8% and the Score by at least 5.0%. These results highlight the robustness and reliability of our method.