基于参数优化 <b>VMD</b> 和样本熵的滚动轴承故障诊断

刘建昌; 权贺; 于霞; 何侃; 李镇华

doi:10.16383/j.aas.c190345

基于参数优化 VMD 和样本熵的滚动轴承故障诊断

doi: 10.16383/j.aas.c190345

刘建昌^1,,
权贺^1,,
于霞^1,,
何侃^2,,
李镇华^1,

1.
东北大学信息科学与工程学院沈阳 110819
2.
华为技术有限公司杭州研究所杭州 310007

基金项目: 国家自然科学基金(61773106)资助

详细信息

作者简介:
刘建昌：东北大学信息科学与工程学院教授. 主要研究方向为控制理论与控制工程, 故障诊断. E-mail: liujianchang@ise.neu.edu.cn

权贺：东北大学信息科学与工程学院硕士研究生. 2017年获得哈尔滨理工大学自动化学院学士学位. 主要研究方向为电机的故障诊断. 本文通信作者. E-mail: quanhee@163.com

于霞：东北大学信息科学与工程学院讲师. 主要研究方向为复杂系统建模与控制, 传感器故障监测与诊断. E-mail: yuxia@ise.neu.edu.cn

何侃：华为技术有限公司软件开发工程师. 2019年获得东北大学硕士学位. 主要研究方向为电机的故障诊断. E-mail: hekan940112@gmail.com

李镇华：东北大学信息科学与工程学院硕士研究生. 2017年获得哈尔滨理工大学自动化学院学士学位. 主要研究方向为切换系统的控制设计, 时滞系统. E-mail: lizhenhuagd@163.com

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出版历程
- 收稿日期: 2019-05-08
- 录用日期: 2019-07-30
- 网络出版日期: 2022-03-02
- 刊出日期: 2022-03-25

Rolling Bearing Fault Diagnosis Based on Parameter Optimization VMD and Sample Entropy

LIU Jian-Chang^1
,,
QUAN He^1
,,
YU Xia^1
,,
HE Kan^2
,,
LI Zhen-Hua^1
,

1.
College of Information Science and Engineering, Northeastern University, Shenyang 110819
2.
Hangzhou Research Institute, Huawei Technology Co., Ltd., Hangzhou 310007

Funds: Supported by National Natural Science Foundation of China (61773106)

More Information

Author Bio:
LIU Jian-Chang　Professor at the College of Information Science and Engineering, Northeastern University. His research interest covers control theory and control engineering, and fault diagnosis

QUAN He　Master student at the College of Information Science and Engineering, Northeastern University. He received his bachelor degree from Harbin University of Science and Technology in 2017. His main research interest is motor fault diagnosis. Corresponding author of this paper

YU Xia　Lecturer at the School of Information Science and Engineering, Northeastern University. Her research interest covers complex system modeling and control, sensor fault monitoring and diagnosis

HE Kan　Software development engineer at Huawei Technology Co., Ltd.. He received his master degree from Northeastern University in 2019. His main research interest is motor fault diagnosis

LI Zhen-Hua　Master student at the College of Information Science and Engineering, Northeastern University. He received his bachelor degree from Harbin University of Science and Technology in 2017. His research interest covers control design of switched systems and time-delay systems

摘要

摘要: 针对滚动轴承故障特征提取不丰富而导致的诊断识别率低的情况, 提出了基于参数优化变分模态分解(Variational mode decomposition, VMD)和样本熵的特征提取方法, 采用支持向量机(Support vector machine, SVM)进行故障识别. VMD方法的分解效果受限于分解个数和惩罚因子的选取, 本文分析了这两个影响参数选取的不规律性, 采用遗传变异粒子群算法进行参数优化, 利用参数优化的VMD方法处理故障信号. 样本熵在衡量滚动轴承振动信号的复杂度时, 得到的熵值并不总是和信号的复杂度相关, 故结合滚动轴承的故障机理, 提出基于滚动轴承故障机理的样本熵, 此样本熵衡量振动信号的复杂度与机理分析的结果一致. 仿真实验表明, 利用本文提出的特征提取方法, 滚动轴承的故障诊断准确率有明显的提高.
- 变分模态分解 /
- 参数优化 /
- 遗传变异粒子群 /
- 样本熵 /
- 故障诊断
Abstract: In this paper, aiming at the low diagnostic recognition rate caused by insufficient fault feature extraction of rolling bearings, a feature extraction method based on parameter optimization variational mode decomposition (VMD) and sample entropy is proposed. Support vector machine (SVM) is used for fault identification. The decomposition effect of VMD method is limited by the number of decomposition and the selection of penalty factor. This paper analyses the irregularity of the two influencing parameters, uses genetic mutation particle swarm optimization to optimize the parameters, and uses parameter optimization VMD method to process fault signals. Sample entropy is not always related to the complexity of the signal when it is used to measure the complexity of the vibration signal of rolling bearings. Therefore, according to the fault mechanism of rolling bearings, a sample entropy based on the fault mechanism of rolling bearings is proposed, which is consistent with the result of mechanism analysis. The simulation results show that the fault diagnosis accuracy of rolling bearings is obviously improved by using the feature extraction method proposed in this paper.
- Varational mode decomposition (VMD) /
- parameter optimization /
- particle swarm algorithm based on genetic variation /
- sample entropy /
- fault diagnosis

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图 1 仿真信号的频谱

Fig. 1 The frequency spectrum of simulated signal

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图 2 中心频率随分解个数 $ K $ 的变化

Fig. 2 Evolution of central frequency with the number of decomposition $ K $

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图 3 中心频率随惩罚因子 $ \alpha $ 的变化

Fig. 3 Evolution of central frequency with punishment factor $ \alpha $

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图 4 基于遗传变异粒子群算法的参数优化流程

Fig. 4 Parameter optimization process based on genetic mulation particle swarm optimization

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图 5 4种状态的振动信号

Fig. 5 Vibration signal in four conditions

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图 6 样本熵的变化曲线

Fig. 6 Evolution of sample entropy

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图 7 测试样本的分类结果

Fig. 7 Classification of test samples

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表 1 样本熵值

Table 1 Sample entropy

工况类型	熵值 1	熵值 2
正常状态	0.9717	0.9696
内圈故障	1.5976	1.7200
滚动体故障	1.6937	1.6883
外圈故障	0.7099	0.8600

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表 2 遗传变异粒子群算法的参数值

Table 2 The values of parameters in the particle swarm algorithm based on the genetic variation

D	m'	c₁	c₂	n_max	maxAge	$\omega_{\max}$	$\omega_{\min}$	q
2	20	2	2	40	2	0.9	0.4	0.5

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表 3 最佳参数组合$\left[ {{K_0},{\alpha_0}} \right]$

Table 3 Optimum combination of parameters

轴承状态	$\left[ {{K_0},{\alpha _0}} \right]$
正常状态	$\left[ {7,2\,082} \right]$
内圈故障	$\left[ {8,277} \right]$
滚动体故障	$\left[ {3,2\,400} \right]$
外圈故障	$\left[ {5,1\,957} \right]$

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表 4 其他处理方法的正确率 (%)

Table 4 Correctness of other processing methods (%)

处理方法	内圈	滚动体	外圈	正常	平均
EMD + SampEn	82.5	82.5	100	100	91.25
LMD + SampEn	90	90	100	100	95
DTCWT + SampEn	97.5	100	97.5	100	98.75
VMD + SampEn	100	90	100	100	97.5

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表 5 测试样本的分类正确率 (%)

Table 5 Classification accuracy of test samples (%)

NO	负载 (kW)	样本类型	VMD + SampEn	本文方法
1	0	${F_0}$	85	97.5
2	0.75	${F_1}$	70	95
3	1.5	${F_2}$	95	97.5
4	2.25	${F_3}$	95	100
5	0	${F_0}$	82.5	97.5
6	0.75	${F_1}$	72.5	92.5
7	1.5	${F_2}$	95	97.5
8	2.25	${F_3}$	95	100
平均正确率	—	—	86.5	97.1875

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表 6 IMF 分量样本熵的均值

Table 6 The mean of IMF sample entropy

负载大小 (kW)	IMF 1	IMF 2	IMF 3	IMF 4
0	0.651	0.668	0.307	0.275
0.75	0.707	0.312	0.208	0.189
1.5	0.546	0.286	0.164	0.168
2.25	0.542	0.229	0.197	0.193

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表 7 IMF 分量传统样本熵的均值

Table 7 The mean of IMF traditional sample entropy

负载大小 (kW)	IMF 1	IMF 2	IMF 3	IMF 4
0	0.633	0.572	0.448	0.325
0.75	0.631	0.588	0.417	0.309
1.5	0.633	0.566	0.376	0.251
2.25	0.627	0.556	0.341	0.332

下载: 导出CSV

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