基于多尺度特征与注意力机制的轴承寿命预测

针对以往剩余使用寿命（RUL）预测方法对轴承退化信息挖掘不充分、忽视不同特征贡献度差异，影响预测准确性的问题，提出基于多尺度特征与注意力机制的轴承RUL预测方法. 在多个尺度下计算轴承原始振动信号的若干时域和频域特征，作为输入特征集. 将多尺度特征集输入到网络中，以注意力模块为不同特征自适应地分配最佳权重，以卷积神经网络（CNN）模块进行深层特征提取与多尺度特征融合，通过前馈神经网络（FNN）模块映射得到RUL预测值. 通过公开的轴承数据集进行实验验证，与其他RUL预测方法相比，所提方法的预测性能更优越.

Bearing life prediction based on multi-scale features and attention mechanism

A bearing RUL prediction method based on multi-scale features and attention mechanism was proposed aiming at the problem that the previous remaining useful life (RUL) prediction methods were insufficient in mining bearing degradation information and ignored the difference in the contribution of different features, which affected the prediction accuracy. Several time-domain and frequency-domain features of the original bearing vibration signal at multiple scales were calculated as the input feature set. The multi-scale feature set was input into the network, and the attention module was used to adaptively assign the best weights to different features. Then the convolutional neural network (CNN) module was used for deep feature extraction and multi-scale feature fusion. The RUL prediction value was obtained through the feedforward neural network (FNN) module mapping. The proposed method was applied to the public bearing datasets for comparative studies. Results showed the superior prediction performance of the proposed method.