电子测量与仪器学报

赵小强,齐祥德.基于空洞卷积与改进BKA-LSSVM的旋转机械故障诊断[J].电子测量与仪器学报,2025,39(11):161-174

基于空洞卷积与改进BKA-LSSVM的旋转机械故障诊断

Fault diagnosis of rotating machinery based on dilatedconvolution and improved BKA-LSSVM

DOI：

英文关键词:rotating machinery fault diagnosis hollow convolution black winged kite optimization algorithm least squares support vector machine

基金项目:

作者	单位
赵小强	1.兰州理工大学电气工程与信息工程学院兰州730050； 2.兰州理工大学国家级电气与控制工程实验教学中心兰州730050
齐祥德	兰州理工大学电气工程与信息工程学院兰州730050

Author	Institution
Zhao Xiaoqiang	1.School of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China; 2.National Experimental Teaching Center for Electrical and Control Engineering, Lanzhou University of Technology, Lanzhou 730050, China
Qi Xiangde	School of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China

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中文摘要:

轴承和齿轮作为机械传动系统中至关重要的部件，其故障诊断对于保证设备的安全运行具有重要意义。为有效提取旋转机械故障信号特征、解决分类器对提取特征存在较强依赖的问题，提出了一种基于空洞卷积和改进黑翅鸢优化最小二乘支持向量机（BKA-LSSVM）的故障诊断模型。首先利用同步压缩小波变换将一维振动信号转化为具有高分辨率时频表示的二维时频图像；其次构建多尺度级联的空洞卷积模块，利用膨胀率调节机制实现对故障特征的分层级、多粒度提取，有效捕捉不同尺度下的故障模式特征，并将全连接层的结果作为BKA-LSSVM分类层的输入，并通过引入非线性增长模型动态调节扰动系数，以及构建随机搜索机制对BKA进行改进；最后利用改进后的BKA对LSSVM的参数进行优化来提高模型的分类精度。在两个数据集上进行验证，实验结果表明，所提模型在样本数为10时准确率高于87%，在信噪比为-4时准确率高于95%，验证了所提模型较对比模型具有更强的抗噪能力和泛化性能。

英文摘要:

Bearings and gears are crucial components in mechanical transmission systems, and their fault diagnosis is of great significance for ensuring the safe operation of equipment. To effectively extract the features of rotating machinery fault signals and solve the problem of strong dependence of classifiers on feature extraction, this paper proposes a fault diagnosis model based on dilated convolution and improved black winged kite optimized least squares support vector machine (BKA-LSSVM). Firstly, the one-dimensional vibration signal is transformed into a two-dimensional time-frequency image with high-resolution time-frequency representation using synchronous compression wavelet transform. Secondly, a multi-scale cascaded dilated convolution module is constructed, and the dilation rate adjustment mechanism is used to achieve hierarchical and multi granularity extraction of fault features, effectively capturing fault mode features at different scales. The results of the fully connected layer are used as inputs to the BKA-LSSVM classification layer, and a nonlinear growth model is introduced to dynamically adjust the disturbance coefficient. A random search mechanism is constructed to improve the BKA. Finally, the improved BKA is used to optimize the parameters of LSSVM to improve the classification accuracy of the model. Validation was conducted on two datasets, and the experimental results showed that the proposed model had an accuracy rate of over 87% when the sample size was 10, and an accuracy rate of over 95% when the signal-to-noise ratio was -4. This validates that the proposed model has stronger noise resistance and generalization performance compared to the comparison model.

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