【摘要】 针对单一加速度传感器信号难以充分反映滚动轴承健康状态的问题，提出了一种基于数据级和特征级信息融合的滚动轴承故障诊断方法.首先，根据滚动轴承故障的冲击特性，设计了相关峭度规则来指导多传感器信号的权重分配，结合加权融合方法获得高质量的数据级融合信号；随后，设计了一个特征融合卷积神经网络(FFCNN),对从融合信号中提取的一维(1D)特征和从小波时频谱中提取的二维(2D)特征进行融合，获得滚动轴承健康状态的充分表征；最后，将融合后的特征输入Softmax分类器，完成故障诊断.结果表明，所提方法在2个滚动轴承故障数据集上平均测试准确率均高于99.00%,优于其他对比方法，可用于滚动轴承的故障诊断.更多还原

【Abstract】 To address the limitation of single acceleration sensor signals in effectively reflecting the health status of rolling bearings, a rolling bearing fault diagnosis method based on the fusion of data-level and feature-level information was proposed. First, according to the impact characteristics of rolling bearing faults, correlation kurtosis rules were designed to guide the weight distribution of multi-sensor signals. These rules were then combined with a weighted fusion method to obtain high-quality data-level fusion signals. Subsequently, a feature-fusion convolutional neural network(FFCNN) that merges the one-dimensional(1D) features extracted from the fused signal with the two-dimensional(2D) features extracted from the wavelet time-frequency spectrum was designed to obtain a comprehensive representation of the health status of rolling bearings. Finally, the fused features were fed into a Softmax classifier to complete the fault diagnosis. The results show that the proposed method exhibits an average test accuracy of over 99.00% on the two rolling bearing fault datasets, outperforming other comparison methods. Thus, the method can be effectively utilized for diagnosing rolling bearing faults.更多还原