针对航空发动机剩余使用寿命（Remaining Useful Life，RUL）在线准确预测问题，本文提出一种基于多源传感器时序信息增强的航空发动机RUL预测方法。首先，集成自注意力机制与双向长短期记忆网络建立RUL预测网络框架，用于捕捉航空发动机多源传感器信号的长时序依赖关系及多源传感器时变性能之间的耦合关系，提取影响RUL的时序特征；在此基础上，引入残差模块解决预测网络在训练过程中潜在的梯度消失问题，提升模型训练的稳定性，同时结合多头自注意力机制从时序特征中提取和增强关键信息，实现RUL在线预测精度和稳定性的双重提升。最后，利用NASA的C-MAPSS航空发动机数据集进行对比实验，结果表明，所提出的方法可充分利用传感器时序信息，在大范围时间和空间尺度上实现精确的航空发动机剩余寿命预测和退化趋势预测，RUL预测的均方根误差（RMSE）相较于其他深度学习模型平均下降了21.74%，决定系数（R2）平均提升了15.81%，可为航空发动机健康管理系统的发展应用以及航空发动机预测性维护提供有效的技术支撑。

To address the challenge of accurately predicting the Remaining Useful Life (RUL) of aircraft engines online, this paper propos-es a novel RUL prediction method that enhances multi-source sensor temporal information. The approach first establishes a pre-diction network framework by integrating the self-attention mechanism with bidirectional long short-term memory (Bi-LSTM) networks. This framework captures the long-term temporal dependencies of multi-source sensor signals and the coupling rela-tionships between their time-varying performance, enabling the extraction of temporal features that influence RUL. To address the potential gradient vanishing issue during training, a residual module is introduced, improving model stability. Additionally, a multi-head self-attention mechanism is employed to extract and enhance key information from the temporal features, leading to dual improvements in both the accuracy and stability of RUL online prediction. Comparative experiments using NASA's C-MAPSS aircraft engine dataset demonstrate the effectiveness of the proposed method. The results show that the method leverag-es sensor temporal information to make precise RUL predictions and degradation trend forecasts across a wide range of time and spatial scales. Specifically, the root mean square error (RMSE) of RUL prediction is reduced by an average of 21.74% compared to other deep learning models, while the coefficient of determination (R2) is improved by an average of 15.81%. This approach offers valuable technical support for the development of aircraft engine health management systems and predictive maintenance strategies.