【摘要】 为提高航空发动机剩余使用寿命的预测精度，提出了一种基于改进粒子群算法(IPSO)优化注意力时间卷积网络(Att-TCN)的剩余使用寿命(RUL)预测方法(IPSO-Att-TCN)。首先，使用注意力机制对各变量特征进行权重分配。其次，使用改进的粒子群算法对注意力时间卷积网络进行超参数优化，利用非线性惯性权重来提升算法的全局寻优的能力。最后，以C-MAPSS的真实剩余使用寿命为参照，将IPSO-Att-TCN与BP、LSTM以及GRU的剩余使用寿命预测结果进行比较，结果表明所提出的模型能有效提高RUL预测精度。更多还原

【Abstract】 In order to improve the prediction accuracy of the remaining useful life of aeroengine, a Remaining Useful Life(RUL) prediction method based on Improved Particle Swarm Optimization(IPSO) optimized Attention Time Convolutional Network(IPSO-Att-TCN) is proposed.Firstly, the attention mechanism is used to assign weights to the features of each variable.Secondly, the improved particle swarm optimization algorithm is used to optimize the hyperparameters of the attention time convolutional network, and the nonlinear inertia weights are used to improve the global optimization ability of the algorithm.Finally, using the actual remaining service life of C-MAPSS as a reference, the remaining service life prediction results of IPSO-Att-TCN are compared with those of BP,LSTM and GRU.The results show that the proposed model can effectively improve the accuracy of RUL prediction.更多还原