|Table of Contents|

[1] Mao Yifan, Xu Feiyun,. A fault feature extraction method of gearbox based on compounddictionary noise reduction and optimized Fourier decomposition [J]. Journal of Southeast University (English Edition), 2021, 37 (1): 22-32. [doi:10.3969/j.issn.1003-7985.2021.01.004]
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A fault feature extraction method of gearbox based on compounddictionary noise reduction and optimized Fourier decomposition()
基于复合字典降噪和优化傅里叶分解的 齿轮箱故障特征提取方法
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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
37
Issue:
2021 1
Page:
22-32
Research Field:
Mechanical Engineering
Publishing date:
2021-03-20

Info

Title:
A fault feature extraction method of gearbox based on compounddictionary noise reduction and optimized Fourier decomposition
基于复合字典降噪和优化傅里叶分解的 齿轮箱故障特征提取方法
Author(s):
Mao Yifan Xu Feiyun
School of Mechanical Engineering, Southeast University, Nanjing 211189, China
毛一帆 许飞云
东南大学机械工程学院, 南京 211189
Keywords:
Fourier decomposition compound dictionary mode mixing gearbox fault feature extraction
傅里叶分解 复合字典 模态混叠 齿轮箱故障 特征提取
PACS:
TH17
DOI:
10.3969/j.issn.1003-7985.2021.01.004
Abstract:
Aimed at the problem that Fourier decomposition method(FDM)is sensitive to noise and existing mode mixing cannot accurately extract gearbox fault features, a gear fault feature extraction method combining compound dictionary noise reduction and optimized FDM(OFDM)is proposed. Firstly, the characteristics of the gear signals are used to construct a compound dictionary, and the orthogonal matching pursuit algorithm(OMP)is combined to reduce the noise of the vibration signal. Secondly, in order to overcome the mode mixing phenomenon occuring during the decomposition of FDM, a method of frequency band division based on the extremum of the spectrum is proposed to optimize the decomposition quality. Then, the OFDM is used to decompose the signal into several analytic Fourier intrinsic band functions(AFIBFs). Finally, the AFIBF with the largest correlation coefficient is selected for Hilbert envelope spectrum analysis. The fault feature frequencies of the vibration signal can be accurately extracted. The proposed method is validated through analyzing the gearbox fault simulation signal and the real vibration signals collected from an experimental gearbox.
针对傅里叶分解对噪声敏感且存在模态混叠导致无法准确提取齿轮箱故障特征的问题, 提出了一种复合字典降噪与优化傅里叶分解相结合的齿轮箱故障特征提取方法.首先, 根据齿轮箱信号特点构造复合字典, 结合正交匹配追踪算法降低振动信号中的噪声;其次, 针对傅里叶分解过程中的模态混叠现象, 提出了利用频谱的极值点划分频带的方法对其进行优化, 提高分解质量;再次, 使用优化的傅里叶分解将信号分解为若干个傅里叶本征模态分量;最后, 选择与降噪后信号相关系数最大的傅里叶本征模态分量进行包络谱分析.该方法可以准确提取振动信号的故障特征频率.通过对齿轮箱故障仿真信号和实验齿轮箱振动信号进行分析, 验证了该方法的有效性.

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Memo

Memo:
Biographies: Mao Yifan(1993—), male, graduate; Xu Feiyun(corresponding author), male, doctor, professor, fyxu@seu.edu.cn.
Foundation items: The National Natural Science Foundation of China(No. 51975117), the Key Research & Development Program of Jiangsu Province(No. BE2019086).
Citation: Mao Yifan, Xu Feiyun. A fault feature extraction method of gearbox based on compound dictionary noise reduction and optimized Fourier decomposition[J].Journal of Southeast University(English Edition), 2021, 37(1):22-32.DOI:10.3969/j.issn.1003-7985.2021.01.004.
Last Update: 2021-03-20