基于多惩罚因子优化VMD的滚动轴承故障特征提取方法

doi:10.12305/j.issn.1001-506X.2023.11.38

Abstract

Abstract:

To solve the problem of uncertainty in pre-setting the penalty factor when the variational mode decomposition (VMD) is used to extract rolling bearing fault features, a rolling bearing fault feature extraction method is propose based on VMD optimization with multiple penalty factors using the grey wolf optimization (GWO) algorithm. Firstly, the GWO algorithm is used to achieve adaptive optimization of VMD with multiple penalty factors. Then, the vibration signal of the rolling bearing is decomposed into multiple intrinsic mode functions (IMF) using the optimized parameters. Finally, the fault frequency features of the rolling bearing are extracted by envelope demodulation of each IMF component. The results show that the optimization efficiency of the proposed method is significantly improved compared with other methods when optimizing VMD parameters, and the effect of extracting rolling bearing fault features is significant. The feature frequency amplitude obtained is 2 to 4 times higher than that of other methods, proving the effectiveness and superiority of the proposed method.

Key words: feature extraction, variational mode decomposition (VMD), intrinsic mode function (IMF), rolling bearing

CLC Number:

TH17

Bo LI, Gexi HU, Jianjun SHI, Hengchang LIU, Tao HONG. Fault feature extraction method of rolling bearing based on multiple penalty factors optimization VMD[J]. Systems Engineering and Electronics, 2023, 45(11): 3690-3698.

Figures/Tables 15

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算法	迭代次数
算法	最小值	最大值	平均值	中位数
GWO-VMD	41	57	48.62	48
PO-VMD	39	82	69.82	71
GOA-VMD	51	87	72.16	74

算法	特征频率
算法	f_i	2f_i	3f_i	4f_i	5f_i
VMD	0.218	0.176	0.112	0.071	0.104
PO-VMD	0.416	0.273	0.261	0.018	0.012
GWO-VMD	0.857	0.682	0.691	0.483	0.841

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Fault feature extraction method of rolling bearing based on multiple penalty factors optimization VMD

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