用深度学习自动分析传感器数据，预测战机发动机剩余寿命。
Predictive Maintenance: Deep Learning-Based Remaining Useful Life Prediction for Combat Aircraft Engines
- 基于多变量传感器数据，自动生成时间序列块进行特征提取。
- 在FD001数据集上达0.8901 R²，30周期阈值下AUC为0.9973。
- 适用于高强度作战飞行场景，适合航空维护系统研发者参考。
为提升战斗飞机发动机的战备状态并降低非计划维修成本，准确估计剩余使用寿命（RUL）至关重要。传统维护方法在动态任务剖面下表现不足。本研究开发了一种基于深度学习的预测性维护模型，可从多变量传感器数据中自主提取特征。利用NASA C-MAPSS FD001和FD004数据集，通过50步和30步滑动窗口将数据转换为序列块。该模型在自主提取时序退化特征方面优于随机森林、CNN-LSTM和BiLSTM基线模型。在FD001上，达到0.8901的决定系数（R²）、13.28的均方根误差（RMSE）和320.34的NASA风险评分；在多工况的FD004数据集上实现15.71的RMSE，验证了其泛化能力。所提维护协议在关键30周期阈值下获得0.9973的AUC，确保高可靠性。此外，还开发了一个决策支持模拟器，用于在激进作战飞行剖面下验证该协议。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。