预测性维护：基于深度学习的战斗机发动机剩余使用寿命预测
Predictive Maintenance: Deep Learning-Based Remaining Useful Life Prediction for Combat Aircraft Engines
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中文总结 AI 辅助
本研究针对战斗机发动机维护需求，开发基于深度学习的预测性维护模型，利用NASA相关数据集验证其性能，还配套决策支持模拟器，提升战备状态并降低维护成本。
中文摘要 AI 辅助
为提升战斗机发动机的作战战备状态并降低非计划维护成本，准确估计剩余使用寿命（RUL）至关重要。传统维护方式在动态任务剖面下常显不足。本研究开发了一种基于深度学习的预测性维护模型，该模型可从多变量传感器数据中自主提取特征。使用NASA C-MAPSS FD001和FD004数据集，分别通过50步和30步滑动窗口将数据转换为序列块。针对RF、CNN-LSTM和BiLSTM基线，验证了该模型在自主提取时间退化特征方面的架构优势。在FD001上，其决定系数（R2）达0.8901，均方根误差（RMSE）为13.28，NASA风险评分为320.34；在多工况FD004数据集上展现出泛化能力，RMSE为15.71。所提出的维护方案在关键的30个循环阈值下达到0.9973的ROC曲线下面积（AUC），确保了高可靠性。此外，还开发了决策支持模拟器，以在高强度作战飞行剖面下验证该方案。
英文摘要
To improve the operational readiness of combat aircraft engines and reduce unplanned maintenance costs, accurately estimating the remaining useful life (RUL) is critical. Traditional maintenance often proves insufficient under dynamic mission profiles. In this study, a deep learning-based predictive maintenance model capable of autonomously extracting features from multivariate sensor data was developed. Using the NASA C-MAPSS FD001 and FD004 datasets, data were converted into sequential blocks via 50- and 30-step sliding windows, respectively. The model's architectural superiority in autonomously extracting temporal degradation features was validated against RF, CNN-LSTM, and BiLSTM baselines. On FD001, it achieved an R-squared (R2) of 0.8901, a 13.28 RMSE, and a 320.34 NASA risk score, demonstrating generalizability on the multi-regime FD004 dataset with a 15.71 RMSE. The proposed maintenance protocol achieved a 0.9973 AUC at the critical 30-cycle threshold, ensuring high reliability. Additionally, a decision-support simulator has been developed to validate this protocol under aggressive combat flight profiles.
