发表机构
Pusan National University（釜山国立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对滚动轴承剩余寿命预测，提出因子分解轴卷积GRU与动态自适应池化方法，增强方向性特征提取，在公开数据集上优于现有方法。
AI 中文摘要
卷积神经网络（CNN）广泛用于从振动信号的时频表示（TFRs）中预测滚动轴承的剩余使用寿命（RUL）。然而，在退化过程中，时频表示中的特征结构主要沿频率轴或时间轴对齐，这使得传统CNN的各向同性卷积核难以捕获方向性结构。此外，全局平均池化（GAP）沿各轴进行平均，可能模糊显著激活的位置和集中度。本研究引入了一种因子分解轴卷积门控循环单元（GRU），采用多尺度各向异性卷积和双轴卷积块注意力模块来增强方向性特征并突出显著的时频区域。动态自适应池化（DAP）自适应地聚合从提取的特征图中获得的时频轴信息，而GRU捕获潜在表示中的时间动态，蒙特卡洛dropout则实现预测不确定性估计。在两个公开轴承数据集上的实验表明，所提出的模型在不同运行条件下优于现有的RUL预测方法。消融实验表明，因子分解轴设计比各向同性卷积核实现了更低的平均误差。DAP在一个数据集上取得了明显改进，而在另一个数据集上与GAP相当，凸显了各向异性特征提取和自适应特征聚合对于基于TFR的RUL预测的重要性。
英文摘要
Convolutional neural networks (CNN) are widely used to predict the remaining useful life (RUL) of rolling bearings from time-frequency representations (TFRs) of vibration signals. However, during degradation, characteristic structures in TFRs align predominantly along the frequency or time axis, making it challenging for conventional CNN isotropic kernels to capture directional structure. Furthermore, global average pooling (GAP) averages across axes, potentially obscuring the locations and concentrations of salient activations. This study introduces a factorized-axis convolutional gated recurrent unit (GRU) that employs multiscale anisotropic convolution and dual-axis convolution block attention module to enhance directional features and highlight salient time-frequency regions. Dynamic adaptive pooling (DAP) adaptively aggregates the time-frequency-axis information from the extracted feature maps, whereas a GRU captures temporal dynamics in the latent representations and Monte Carlo dropout enables predictive uncertainty estimation. Experiments on two public bearing datasets demonstrate that the proposed model outperforms existing RUL prediction methods across operating conditions. Ablation experiments demonstrate that the factorized axis-wise design achieves lower mean errors than convolutional isotropic kernels. DAP yields clear improvements on one dataset while matching GAP on the other, highlighting the importance of anisotropic feature extraction and adaptive feature aggregation for TFR-based RUL prediction.
