基于改进谱峭度图与多维融合CNN的轴承故障诊断方法

针对轴承振动信号中存在与故障特征相关性较低成分的干扰导致故障诊断准确率降低的问题，提出了一种基于改进谱峭度图与多维融合CNN的轴承故障诊断方法。首先，为提高振动信号与故障特征的相关性，减少干扰成分，以双树复小波包变换为基础构建改进谱峭度图模型，增强多分辨率差异性故障特征表达。然后，考虑丰富特征评价维度，构建多维融合CNN模型，将原始信号与改进谱峭度图共同作为多维特征输入实现故障精准诊断。实验结果表明，该方法能够提取各类轴承振动信号中具备差异性的故障特征，在多工况下均能够准确识别轴承故障，具有较好的诊断精度。

Bearing fault diagnosis method based on improved spectral kurtosis map and multidimensional fusion CNN

Aiming at the problem that the interference of components with low correlation with fault features in the bearing vibration signal reduces the fault diagnosis accuracy, a bearing fault diagnosis method based on improved spectral kurtosis map and multi-dimensional fusion CNN is proposed. To improve the correlation between vibration signals and fault features and reduce interference components, an improved spectral kurtosis graph model was constructed based on DTCWPT to enhance the expression of multi-resolution differential fault features. Then, considering the rich feature dimension, a multi-dimensional fusion CNN model is constructed, and the original signal and the improved spectral kurtosis map are used as input together. The experimental results show that the method can extract different fault features in the vibration signals of various types of bearings, and can accurately identify bearing faults under multiple working conditions, with good diagnostic accuracy.