剩余使用寿命的精准预测对于大型复杂装备的稳定可靠运行具有重要价值。为了确保预测准确性的同时提高模型的鲁棒性和泛化性，提出了一种基于双重注意力时序卷积网络和选择性粒子群优化集成的预测方法。首先，利用双重注意力时序卷积网络探索监测数据中多类别输入特征和不同时间步长之间的内部关联，从特征与时间两个维度强化退化信息；再利用选择性粒子群优化集成算法修剪不同时间尺度下的基模型，自删除表现不佳模型，自生成最优模型子集并赋予最优权重，加权输出预测结果。所提方法已在航空涡扇发动机退化数据集上开展验证，结果表明预测准确性比其他方法提高了13.9%。

Accurately predicting remaining useful life (RUL) is crucial for ensuring the stable and reliable operation of large complex equipment. To enhance prediction accuracy while improving model robustness and generalization, a method based on dual-attention temporal convolutional networks and selective particle swarm optimization ensemble is proposed. Firstly, the dual-attention temporal convolutional networks are employed to explore the internal correlations between multi-category input features and different time steps in monitoring data, enhancing degradation information from both feature and temporal dimensions. Subsequently, the selective particle swarm optimization ensemble algorithm prunes underperforming base models at various time scales, autonomously generating an optimal subset of models and assigning optimal weights for weighted output predictions. The proposed method has been validated on a dataset of aviation turbofan engine degradation, demonstrating a 13.9% improvement in prediction accuracy compared to other methods.