Rolling Bearing Fault Diagnosis with Parameters Optimized VMD and SVM
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摘要: 为了便于选取变分模态分解(VMD)参数、综合考虑轴承故障信号周期冲击性、循环平稳性, 各分量与原信号相关性及不同故障诊断的问题, 构建了一种天牛须搜索算法(BAS)优化VMD及加权合成峭度提取最优本征模态函数(IMF), 并结合布谷鸟算法优化支持向量机(CS-SVM)的轴承故障诊断方法。先以平均包络熵为BAS的适应度函数优化VMD参数, 接着对信号进行VMD分解。然后以加权合成峭度最大优选IMF, 对所选IMF提取故障特征并组成特征向量。最后, 将其输入CS-SVM中进行故障分类。运用仿真信号和实际轴承数据验证所提方法的可行性。Abstract: In order to properly select the parameters of variational mode decomposition (VMD), and to overall consider the periodic impulsiveness and cyclostationary of bearing fault signal, the correlation between each component and the original signal, and the diagnosis problem of different faults, a new bearing fault diagnosis method using beetle antennae search (BAS) optimized VMD and weighted ensemble kurtosis for extracting the optimal intrinsic mode function (IMF) combining with the cuckoo search algorithm optimized support vector machine (CS-SVM) is constructed in this study. Firstly, BAS is used to optimize VMD parameters with the mean envelope entropy as the fitness function, and optimized VMD is used to decompose signal. Then, IMF is optimized by the maximum value of weighted ensemble kurtosis, and extract its fault features to form feature vectors. Finally, they are imported into the CS-SVM for fault classification. Simulation signal and actual bearing data are used to verify the proposed method.
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表 2 IMFs加权合成峭度值
IMFi EKCI IMFi EKCI IMF1 0.942 1 IMF5 0.930 8 IMF2 1.085 8 IMF6 0.877 5 IMF3 1.277 4 IMF7 0.749 6 IMF4 1.257 2 
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表 3 轴承7种故障状态样本
状态 数据标签 损伤直径/mm 训练集 测试集 正常 1 - 12 18 内圈故障1 2 0.18 12 18 内圈故障2 3 0.36 12 18 内圈故障3 4 0.53 12 18 滚动体故障 5 0.18 12 18 外圈故障1 6 0.18 12 18 外圈故障2 7 0.53 12 18 总和 - - 84 126
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表 4 不同分类算法比较
分类器 Tm/s C g 识别率/% CS-SVM 25.142 91.163 1.071 99.2 PSO-SVM 29.123 0.492 8 211.313 98.4 AFSA-SVM 49.941 29.957 164.771 98.4
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