循环平稳相关熵轴承故障诊断方法

李辉

doi:10.13433/j.cnki.1003-8728.20220004

循环平稳相关熵轴承故障诊断方法

doi: 10.13433/j.cnki.1003-8728.20220004

李辉

天津职业技术师范大学机械工程学院, 天津 300222

基金项目:

国家自然科学基金项目 51375319

详细信息

作者简介:
李辉(1968-)，教授，硕士生导师，博士，研究方向为非平稳信号处理及机电设备故障诊断, huili68@163.com

中图分类号: TH165⁺.3;TN911.72
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- 文章访问数: 117
- HTML全文浏览量: 33
- PDF下载量: 17
- 被引次数: 0
出版历程
- 收稿日期: 2020-08-28
- 刊出日期: 2023-07-25

Bearing Fault Diagnosis Method Based on Cyclostationary Correntropy Analysis

LI Hui

School of Mechanical Engineering, Tianjin University of Technology and Education, Tianjin 300222, China

摘要

摘要: 相关熵是一种基于信息理论学习和核函数的相似性度量方法，不仅能有效刻画信号的时间和统计特征，而且包含了信号的高阶统计量，因而，相关熵是处理非高斯、非线性信号的有效方法。将相关熵与循环平稳信号处理方法结合，提出了一种基于循环平稳相关熵的轴承故障诊断方法。首先，简述了相关熵的基本概念，推导了循环平稳相关熵函数和循环平稳相关熵谱密度函数公式；其次，分析了循环平稳相关熵轴承故障诊断流程；最后，将循环平稳相关熵应用于轴承内圈、外圈局部裂纹故障振动信号的分析与处理。实验结果表明：相关熵能有效提取轴承故障振动信号中的周期成分，循环平稳相关熵函数和循环平稳相关熵谱密度函数能有效刻画轴承故障的频谱特征，便于进行故障特征提取与识别，验证了提出方法的优越性。
- 故障诊断 /
- 轴承 /
- 核函数 /
- 相关熵 /
- 循环平稳相关熵
Abstract: Correntropy is a kind of similarity measure method based on information theoretic learning and kernel function. Correntropy can not only effectively describe the time and statistical characteristics of the signal, but also include the high-order statistics of the signal, therefore, it is an effective technique to deal with non-Gaussian and nonlinear signals. Combining the correntropy with the cyclostationary signal analysis, a bearing fault diagnosis method based on cyclostationary correntropy is proposed. Firstly, the basic concept of correntropy is introduced, and the formulas of cyclostationary correntropy function and cyclostationary correntropy spectral density function are derived. Secondly, the steps of bearing fault diagnosis based on cyclostationary correntropy are put forward. Finally, the cyclostationary correntropy technique is applied to the analysis and processing of vibration signals of bearing inner race or outer race localized crack defect. The outcomes confirm that the correntropy can efficaciously extract the periodic components of bearing fault vibration signal, and the cyclostationary correntropy function and cyclostationary correntropy spectral density can effectively characterize the spectrum characteristics of bearing fault, which is convenient for fault feature extraction and recognition. The superiority of the proposed method is further verified.
- fault diagnosis /
- bearing /
- kernel function /
- correntropy /
- cyclostationary correntropy

HTML全文

图 1 轴承内圈故障振动信号及其FFT

Figure 1. Vibration signal and FFT of inner race faults in bearings

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图 2 轴承内圈故障信号的相关熵

Figure 2. Correntropy of inner race fault signals in bearings

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图 3 轴承内圈故障信号的R_x^σ(α, τ)(三维图)

Figure 3. The R_x^σ(α, τ) of inner race fault signals in bearings (3D plot)

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图 4 轴承内圈故障信号的R_x^σ(α, τ)(等高线图)

Figure 4. The R_x^σ(α, τ) of inner race fault signals in bearings (contour plot)

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图 5 轴承内圈故障信号的S_x^σ(α, f)(三维图)

Figure 5. The S_x^σ(α, f) of inner race fault signals in bearings (3D plot)

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图 6 轴承内圈故障信号的S_x^σ(α, f)(等高线图)

Figure 6. The S_x^σ(α, f) of inner race fault signals in bearings (contour plot)

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图 7 轴承内圈故障信号的DCS^σ

Figure 7. The DCS^σ of inner race fault signals in bearing

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图 8 轴承外圈故障振动信号及其FFT

Figure 8. Vibration signals and FFT of outer race faults in bearings

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图 9 轴承外圈故障振动信号相关熵

Figure 9. Correntropy of outer race fault signals in bearings

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图 10 轴承外圈故障信号的R_x^σ(α, τ)(三维图)

Figure 10. The R_x^σ(α, τ) of outer race fault signals in bearings (3D plot)

下载: 全尺寸图片幻灯片

图 11 轴承外圈故障信号的R_x^σ(α, τ)(等高线图)

Figure 11. The R_x^σ(α, τ) of outer race fault signals in bearings (contour plot)

下载: 全尺寸图片幻灯片

图 12 轴承外圈故障信号的S_x^σ(α, f)(三维图)

Figure 12. The S_x^σ(α, f) of outer race fault signals in bearings (3D plot)

下载: 全尺寸图片幻灯片

图 13 轴承外圈故障信号的S_x^σ(α, f)(等高线图)

Figure 13. S_x^σ(α, f) of outer race fault signals in bearings (contour plot)

下载: 全尺寸图片幻灯片

图 14 轴承外圈故障信号的DCS^σ

Figure 14. The DCS^σ of outer race fault signals in bearings

下载: 全尺寸图片幻灯片

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