双向残差校正LSTM：用于航空发动机剩余使用寿命预测的残差校正双向LSTM
Bi-cLSTM: Residual-Corrected Bidirectional LSTM for Aero-Engine RUL Estimation
AI总结：
Bi-cLSTM通过结合双向时间建模与自适应残差校正机制，提升航空发动机剩余使用寿命预测的鲁棒性和准确性。
摘要：
准确的剩余使用寿命（RUL）预测是安全关键系统（如航空发动机）中有效故障诊断与健康管理（PHM）的关键要求。现有的深度学习方法，特别是基于LSTM的模型，往往在不同运行条件下难以泛化，并且对多变量传感器数据中的噪声敏感。为了解决这些挑战，我们提出了一种新的双向残差校正LSTM（Bi-cLSTM）模型，用于稳健的RUL估计。所提出的架构结合了双向时间建模和自适应残差校正机制，以迭代方式细化序列表示。此外，我们引入了一种条件感知的预处理流水线，包括基于状态的归一化、特征选择和指数平滑，以提高在复杂运行环境下的鲁棒性。在NASA C-MAPSS数据集的四个子集上进行了广泛的实验，结果表明，所提出的Bi-cLSTM在所有四个子集上均优于基于LSTM的基线模型，并在具有挑战性的多条件场景中实现了具有竞争力的最先进性能。这些结果突显了将双向时间学习与残差校正相结合在可靠RUL预测中的有效性。
英文摘要：
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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