用堆叠集成提升航空发动机剩余寿命预测精度。
A Deep Learning-Based Stacking Ensemble Framework for Turbofan Engine Remaining Useful Life Prediction
- 融合LSTM、CNN等四种深度模型，用XGBoost整合预测结果。
- 在FD001和FD003上RMSE分别降至9.989和8.613。
- 适合航空航天健康监测场景，抗模型偏差能力强。
本研究提出一种两级堆叠集成框架，用于航空发动机剩余使用寿命（RUL）预测，基于NASA C-MAPSS基准的FD001和FD003数据集进行评估。框架整合了四种异构深度学习基学习器：长短期记忆网络（LSTM）、卷积神经网络（CNN）、CNN-LSTM和CNN-GRU，其交叉验证预测结果由XGBoost元学习器融合，以捕捉复杂退化模式并降低单个模型偏差。实验表明，该集成方法表现优异，在FD001和FD003上的均方根误差（RMSE）分别为9.989和8.613，平均绝对误差（MAE）为7.081和5.195，决定系数（R²）达0.899和0.906。相比最优基线方法TCAT（RMSE 11.12和11.02），性能提升分别为10.2%和21.8%。特征相关性分析、残差诊断与训练收敛曲线验证了模型稳健性。结果表明，堆叠集成方法在安全关键型航空航天健康管理中具有显著有效性。
原文摘要 · Abstract (English)
This study proposes a two-level stacking ensemble framework for Remaining Useful Life (RUL) prediction of turbofan engines, evaluated on the NASA C-MAPSS benchmark using the FD001 and FD003 subsets. The framework integrates four heterogeneous deep learning base learners: Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), CNN-LSTM, and CNN-GRU, whose out-of-fold predictions are combined by an XGBoost meta-learner to capture complex degradation patterns while mitigating individual model biases. Comprehensive experiments demonstrate that the stacking ensemble achieves superior predictive performance, with Root Mean Square Error (RMSE) of 9.989 and 8.613, Mean Absolute Error (MAE) of 7.081 and 5.195, and R-squared values of 0.899 and 0.906 for FD001 and FD003, respectively. Compared to the best-reported baseline (TCAT: RMSE 11.12 and 11.02), the proposed method achieves RMSE reductions of 10.2 percent and 21.8 percent for FD001 and FD003, respectively. Feature correlation analysis, residual diagnostics, and training convergence curves validate the model's robustness. These findings underscore the efficacy of stacking ensemble methods for prognostics and health management in safety-critical aerospace applications.
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