发表机构
Bangladesh University of Engineering and Technology; Clemson University; Louisiana Tech University; Purdue University（孟加拉工程技术大学; 克莱姆森大学; 路易斯安那理工大学; 普渡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出两级堆叠集成深度学习框架，结合LSTM等四种基学习器与XGBoost元学习器，在NASA C-MAPSS数据集上实现涡扇发动机RUL预测性能优于TCAT基准，验证了其在航空航天PHM中的有效性。
AI 中文摘要
本研究提出一种用于涡扇发动机剩余使用寿命（RUL）预测的两级堆叠集成框架，在NASA C-MAPSS基准数据集的FD001和FD003子集上进行评估。该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以及CNN-GRU，其折外预测结果由XGBoost元学习器组合，以捕捉复杂退化模式同时缓解单一模型偏差。综合实验表明，该堆叠集成框架预测性能优异，针对FD001和FD003，其均方根误差（RMSE）分别为9.989和8.613，平均绝对误差（MAE）分别为7.081和5.195，决定系数分别为0.899和0.906。与已报道的最佳基准模型TCAT（RMSE分别为11.12和11.02）相比，所提方法在FD001和FD003上的RMSE分别降低了10.2%和21.8%。特征相关性分析、残差诊断及训练收敛曲线验证了模型的鲁棒性，这些发现凸显了堆叠集成方法在安全关键航空航天应用的预测与健康管理中的有效性。
英文摘要
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.
