用双向残差修正LSTM提升发动机剩余寿命预测精度
Bi-cLSTM: Residual-Corrected Bidirectional LSTM for Aero-Engine RUL Estimation
- 双向LSTM结合自适应残差修正，迭代优化时序特征
- 在NASA C-MAPSS四组数据上均优于传统LSTM模型
- 适合复杂工况下高可靠性的设备健康状态预测
精确的剩余使用寿命（RUL）预测是航空发动机等关键系统健康管理（PHM）的核心需求。现有基于LSTM的深度学习方法在不同工况下泛化能力不足，且对多变量传感器数据中的噪声敏感。为此，本文提出一种新型双向残差修正长短期记忆网络（Bi-cLSTM），通过双向时序建模与自适应残差修正机制，迭代优化序列表征。同时引入基于工况的预处理流程，包含分段归一化、特征选择和指数平滑，以增强复杂运行环境下的鲁棒性。在NASA C-MAPSS数据集全部四个子集上的实验表明，所提Bi-cLSTM持续优于基于LSTM的基线模型，并在多工况挑战场景中达到领先性能，验证了双向时序学习与残差修正结合的有效性。
原文摘要 · Abstract (English)
Accurate Remaining Useful Life (RUL) prediction is a key requirement for effective Prognostics and Health Management (PHM) in safety-critical systems such as aero-engines. Existing deep learning approaches, particularly LSTM-based models, often struggle to generalize across varying operating conditions and are sensitive to noise in multivariate sensor data. To address these challenges, we propose a novel Bidirectional Residual Corrected LSTM (Bi-cLSTM) model for robust RUL estimation. The proposed architecture combines bidirectional temporal modeling with an adaptive residual correction mechanism to iteratively refine sequence representations. In addition, we introduce a condition-aware preprocessing pipeline incorporating regime-based normalization, feature selection, and exponential smoothing to improve robustness under complex operating environments. Extensive experiments on all four subsets of the NASA C-MAPSS dataset demonstrate that the proposed Bi-cLSTM consistently outperforms LSTM-based baselines and achieves competitive state-of-the-art performance, particularly in challenging multi-condition scenarios. These results highlight the effectiveness of combining bidirectional temporal learning with residual correction for reliable RUL prediction.
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