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

系统工程与电子技术 ›› 2023, Vol. 45 ›› Issue (11): 3690-3698.doi: 10.12305/j.issn.1001-506X.2023.11.38

基于多惩罚因子优化VMD的滚动轴承故障特征提取方法

李波¹^,²^,*, 胡哿郗¹^,², 石剑钧¹^,², 刘恒畅¹^,², 洪涛¹^,²

1. 电子科技大学航空航天学院, 四川成都 611731
2. 飞行器集群智能感知与协同控制四川省重点实验室, 四川成都 611731

收稿日期:2023-05-09 出版日期:2023-10-25 发布日期:2023-10-31
通讯作者: 李波
作者简介:李波(1975—), 男, 教授, 博士, 主要研究方向为装备故障预测/诊断与维护、生产调度与过程控制
胡哿郗(1999—), 男, 硕士研究生, 主要研究方向为装备故障诊断、复杂装备可靠性建模
石剑钧(1996—), 男, 助理工程师, 硕士, 主要研究方向为装备故障诊断与识别
刘恒畅(1997—), 男, 博士研究生, 主要研究方向为装备维修决策与健康评估
洪涛(1977—), 男, 副研究员, 博士, 主要研究方向为航空宇航智能制造工程、装备故障诊断
基金资助:
四川省科技厅科技计划(2023YFG0050);四川省科技厅科技计划(2023YFG0039)

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

Bo LI¹^,²^,*, Gexi HU¹^,², Jianjun SHI¹^,², Hengchang LIU¹^,², Tao HONG¹^,²

1. School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China
2. Sichuan Key Laboratory of Intelligent Sensing and Cooperative Control for Aircraft Cluster, Chengdu 611731, China

Received:2023-05-09 Online:2023-10-25 Published:2023-10-31
Contact: Bo LI

摘要/Abstract

摘要：

针对变分模态分解(variational mode decomposition, VMD)在提取滚动轴承故障特征时预先设置多惩罚因子具有不确定性的问题, 结合灰狼优化(grey wolf optimization, GWO)算法提出一种基于多惩罚因子优化VMD的滚动轴承故障特征提取方法。首先利用GWO算法实现VMD的多惩罚因子自适应优化, 再利用优化得到的参数将滚动轴承的振动信号分解为多个本征模态函数(intrinsic mode function, IMF), 最后对各个IMF分量作包络解调提取滚动轴承的故障频率特征。研究结果表明, 在优化VMD参数时, 该方法相对其他方法优化效率有了明显提高, 并且提取滚动轴承故障特征效果显著, 得到特征频率幅值为其他方法的2~4倍, 证明了该方法的有效性和优越性。

关键词: 特征提取, 变分模态分解, 本征模态函数, 滚动轴承

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

中图分类号:

TH17

李波, 胡哿郗, 石剑钧, 刘恒畅, 洪涛. 基于多惩罚因子优化VMD的滚动轴承故障特征提取方法[J]. 系统工程与电子技术, 2023, 45(11): 3690-3698.

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.

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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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基于多惩罚因子优化VMD的滚动轴承故障特征提取方法

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

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