针对航空发动机寿命预测中存在特征信息提取不充分、子空间隔离导致预测精度受限的问题，提出一种基于多头自注意力机制（multi-head self-attention mechanism, MHSA）的双向时间卷积网络（bidirectional temporal convolutional network, BiTCN）模型。首先，在时间卷积网络（temporal convolutional network, TCN）的基础上引入双向架构BiTCN，以弥补TCN不能捕捉双向时间序列信息的缺陷，然后通过设置多个注意力头，每个头都独立地进行双向TCN处理和特征融合，将融合后的特征信息利用多头自注意力机制增强特征交互与自适应权重分配，以捕捉不同的时间序列特征。最后采用C-MAPSS数据集进行实验验证，验证结果表明该模型对航空发动机的剩余使用寿命预测精度高于经典方法。

In order to solve the problems of feature loss and limited prediction accuracy due to subspace segregation in traditional models in the field of aero-engine life prediction, a bidirectional temporal convolutional network model incorporating a multi-head self-attention mechanism was proposed. Firstly, a bidirectional architecture was introduced on the basis of temporal convolutional network to compensate for the defect that temporal convolutional network cannot capture bidirectional time series information. Subsequently, multiple attention heads were established, each independently performing bidirectional temporal convolutional network processing and feature fusion. The fused feature information was then utiliezd to enhance the feature interaction and adaptive weight assignment using the multi-head self-attention mechanism in order to capture different time series features. Finally, the C-MAPSS dataset was used for experimental validation. The validation results show that the model's remaining useful life prediction accuracy for aero-engines is higher than that of the classical methods.