Among the existing bearing fault diagnosis algorithms, the diagnosis time of algorithms with high accuracy is relatively long, and the accuracy of some lightweight methods is low compared to other methods. This work proposes a bi-level binary coded fully connected classifier based on residual network 50 with bottom and deep level features (Bi-BUR) for bearing fault diagnosis, enabling both high accuracy and high-speed characteristics. The Bi-BUR has two levels of classifiers. The first level begins with a signal-to-picture method based on integer binary coding to convert the bearing fault signals into binary pictures; then, the first classification of the converted pictures is performed by the underlying feature extraction-residual network 50, which can extract both the underlying and the advanced features. The second level performs modal decomposition of the signals that are not classified successfully in the first level, performs the second classification with a fully connected classifier inserted into the underlying feature extraction module, and exports the final classification results after weighted summation of the results of each type. The Bi-BUR is simulated on the Case Western Reserve University motor, the self-priming centrifugal pump, and the axial piston hydraulic pump bearing failure datasets. All three experiments achieved more than 95% accuracy in the first level of categorization and 100% diagnosis after the second level of categorization. And the Bi-BUR is faster than other high-accuracy methods.

中文翻译：

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基于残差网络50的具有底层和深层特征的轴承故障诊断双层二进制编码全连接分类器

现有的轴承故障诊断算法中，精度较高的算法诊断时间较长，而一些轻量级方法相对于其他方法精度较低。这项工作提出了一种基于具有底层和深层特征的残差网络50（Bi-BUR）的双层二进制编码全连接分类器，用于轴承故障诊断，实现高精度和高速特性。 Bi-BUR 有两个级别的分类器。第一级首先采用基于整数二进制编码的信号转图像方法，将轴承故障信号转换为二进制图像；然后，由底层特征提取残差网络50对转换后的图片进行第一次分类，该网络可以提取底层特征和高级特征。第二级对第一级分类失败的信号进行模态分解，在底层特征提取模块中插入全连接分类器进行二次分类，将各分类结果加权求和后导出最终的分类结果。类型。 Bi-BUR 在凯斯西储大学电机、自吸离心泵和轴向柱塞液压泵轴承故障数据集上进行模拟。所有三个实验在第一级分类时均达到了95%以上的准确率，在第二级分类后的诊断率为100%。 Bi-BUR 比其他高精度方法更快。