SE-KSD优化的FRFT-VMD轴承故障诊断方法

韦明辉; 江丽霞; 涂凤秒; 姜蓬勃

doi:10.13433/j.cnki.1003-8728.20220202

SE-KSD优化的FRFT-VMD轴承故障诊断方法

doi: 10.13433/j.cnki.1003-8728.20220202

韦明辉^{1, 2,},
江丽霞^{1, 2},
涂凤秒^{1, 2},
姜蓬勃^{1, 2}

1.
西南石油大学机电工程学院，成都　610500
2.
石油天然气装备技术四川省科技资源共享服务平台，成都　610500

基金项目: 国家自然科学基金项目(51804267)与中国石油大学石油资源与勘探国家重点实验室项目(北京)(PRP/open-1610)

详细信息

作者简介:
韦明辉，副教授，硕士生导师，博士研究生，201699010089@swpu.edu.cn

中图分类号: TG156
计量
- 文章访问数: 62
- HTML全文浏览量: 16
- PDF下载量: 11
- 被引次数: 0
出版历程
- 收稿日期: 2021-11-12
- 网络出版日期: 2024-03-08
- 刊出日期: 2024-02-01

SE-KSD Optimized FRFT-VMD Bearing Fault Diagnosis Method

WEI Minghui^{1, 2
,},
JIANG Lixia^{1, 2},
TU Fengmiao^{1, 2},
JIANG Pengbo^{1, 2}

1.
School of Mechanical Engineering, Southwest Petroleum University, Chengdu 610500, China
2.
Oil and Gas Equipment Technology Sichuan Province Science and Technology Resource Sharing Service Platform, Chengdu 610500, China

摘要

摘要: 提出了一种基于样本熵（SE）和峭度均方差（KSD）优化的分数阶变分模态分解（FRFT-VMD）的故障特征提取方法，同时结合随机森林（RF）分类器对故障进行自动识别分类。针对分数阶傅里叶变换中阶次选择对于数据可分性影响较大的问题，提出利用搜寻样本熵最小值来得到分数阶最优阶次，使得数据中重叠交叉部分在分数域内能更好分离。同时利用峭度均方差准则寻找变分模态分解最优参数使变分模态分解效果更好。通过数据库和实测数据的研究结果表明，该方法提取的信号包含更多和更明显的故障特征频率，大幅度提高了滚动轴承不同状态的故障诊断准确率。
- 分数阶Fourier变换 /
- 变分模态分解 /
- 特征提取 /
- 故障诊断
Abstract: A fault feature extraction method based on sample entropy (SE) and kurtosis standard deviation (KSD) optimization of fractional variational modal decomposition (FRFT-VMD) is proposed, and a random forest (RF) classifier is combined to perform fault detection. Automatic recognition of classification. Aiming at the problem that the choice of order in the fractional Fourier transform has a greater impact on the separability of data, it is proposed to search for the minimum entropy of the sample to obtain the optimal order of the fractional order. Makes the overlapped part of the data better separated in the score domain. At the same time, the kurtosis standard deviation criterion is used to find the optimal parameters of the variational modal decomposition to make the effect of the variational modal decomposition better. The research results based on database data and measured data show that the signals extracted by this method contain more and more obvious fault characteristic frequencies, which greatly improves the fault diagnosis accuracy of rolling bearings in different states.
- fractional fourier transform /
- variational mode decomposition /
- feature extraction /
- fault diagnosis

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图 1 时频平面上分数阶傅里叶变换旋转示意图

Figure 1. Schematic diagram of fractional Fourier transform rotation on the time-frequency plane

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图 2 基于分数阶Fourier变换的变分模态分解算法流程

Figure 2. Procedures of variational modal decomposition algorithm based on fractional fourier transform

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图 3 分数阶Fourier最优阶次

Figure 3. Fractional Fourier optimal order

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图 4 惩罚因子和分解层

Figure 4. Penalty factor and decomposition layer

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图 5 分数阶VMD分解各分量

Figure 5. Fractional order VMD decomposition of each component

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图 6 不同参数的FRFT-VMD各分量包络谱图

Figure 6. Envelope spectra of FRFT-VMD components with different parameters

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图 7 实验流程图

Figure 7. Experimental flow chart

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图 8 轴承各状态下原始时域图

Figure 8. Original time domains diagram of bearings in different states

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图 9 聚类结果图

Figure 9. Clustering results

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图 10 随机森林结构图

Figure 10. Random forest structure

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图 11 分类结果图

Figure 11. Classification results

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图 12 实验平台框图

Figure 12. Experimental platform's block diagram

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图 13 实验平台图

Figure 13. Experimental platform

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图 14 实测数据随机森林结构图

Figure 14. Random forest structure of measured data

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图 15 实测数据基于SE-KSD优化的FRFT-VMD-RF分类结果

Figure 15. FRFT-VMD-RF classification results of measured data based on SE-KSD optimization

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表 1 轴承不同状态数据

Table 1. Different status data of bearings

轴承状态	故障直径/ mm	电机转速/ （r·min⁻¹）	轴承型号（驱动端）	采样频率/ Hz
正常状态	-	1797	SKF6205	12
内圈故障	0.1778	1797	SKF6205	12
滚动体故	0.1778	1797	SKF6205	12
外圈故障	0.1778	1797	SKF6205	12

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表 2 各状态轴承对应编号

Table 2. Corresponding numbers of bearings in each state

轴承状态	训练样本数	测试样本数	故障编号
正常状态	32	8	1
内圈故障	32	8	2
滚动体故障	32	8	3
外圈故障	32	8	4

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表 3 基于SE-KSD优化的FRFT-VMD-RF分类结果汇总

Table 3. Summary of FRFT-VMD-RF classification results based on SE-KSD optimization

轴承状态	缺陷大小/ mm	编号	测试样本数	正确分类数	准确率/%
正常状态	-	1	8	8	100
内圈故障	0.1778	2	8	8	100
滚动体	0.1778	3	8	8	100
外圈故障	0.1778	4	8	7	87.5
汇总	-	-	32	31	96.875

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表 4 基于VMD-RF分类结果汇总

Table 4. Summary of VMD-RF classification results

轴承状态	缺陷大小/ mm	编号	测试样本数	正确分类数	准确率/%
正常状态	-	1	8	4	50
内圈故障	0.1778	2	8	8	100
滚动体	0.1778	3	8	8	100
外圈故障	0.1778	4	8	5	62.5
汇总	-	-	32	25	78.1

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