为了解决传统轴承剩余使用寿命（remaining useful life, RUL）预测方法依赖先验知识过多、轴承退化数据复杂且特征数量众多导致预测误差较大、精度不高的难题，提出了一种结合主成分分析法（principal component analysis, PCA）、深度残差收缩网络（deep residual semantic network, DRSN）和时间卷积网络（temporal convolutional network, TCN）的轴承剩余寿命预测方法。首先，通过PCA对提取的轴承信号特征进行降维筛选，保留包含退化信息较多的特征作为新的数据特征，减少数据复杂度。其次，利用TCN网络捕捉寿命数据之间的时序相关性，有效建模轴承退化过程。最后，通过DRSN网络的软阈值函数和注意力模块，消除信号中的干扰噪声，增强有用特征通道，提高预测精度。采用XJTU数据集验证了该方法在轴承寿命预测中的有效性。实验结果表明经过对PCA降维后的数据预测与未降维预测的相比，MSE值下降了84.6%。DRSN-TCN模型与DRSN模型相比MAE降低了72.7%。与TCN模型相比MSE值降低了83.5%。说明PCA降维和DRSN-TCN模型结合的方法的有效性。

In order to solve the problems that the traditional bearing remaining service life (RUL) prediction method relies too much prior knowledge, the bearing degradation data is complex and the number of features is large, resulting in large prediction errors and low accuracy, a method combining principal component analysis (PCA), deep residual shrinkage network (DRSN) and time convolutional network (TCN) for predicting bearing residual life was proposed. Firstly, the extracted bearing signal features were reduced by PCA, and the features containing more degraded information were retained as new data features to reduce data complexity. Secondly, the TCN network was used to capture the temporal correlation between the life data and effectively model the bearing degradation process. Finally, through the soft threshold function and attention module of DRSN network, the interference noise in the signal was eliminated, the useful feature channel was enhanced, and the prediction accuracy was improved. XJTU data set was used to verify the validity of this method in bearing life prediction. The experimental results show that the MSE value decreased by 84.6% after PCA dimensionality reduction compared with the non-dimensionality reduction. The MAE of DRSN-TCN model was reduced by 72.7% compared with DRSN model. Compared with TCN model, MSE value decreased by 83.5%. The effectiveness of the combined method of PCA reduction and DRSN-TCN model was illustrated.