齿轮箱断齿特征识别的S变换-SVD降噪组合方法

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摘要齿轮箱作为传动结构的核心部件，其断齿故障将直接导致设备损伤，产生巨大经济损失。为了从齿轮箱振动信号中准确识别出断齿故障，把S变换时频谱与奇异值分解（SVD）降噪相结合，通过对比分析奇异值比值谱与差分谱的差异，提出了S变换-奇异值比值谱选取阈值的降噪方法。首先将信号S变换的时频矩阵作为SVD的构造矩阵，然后采用奇异值比值谱选取置零阈值位置进行降噪，最后对降噪后的时频矩阵进行S逆变换，获得信号的时域冲击特征，并结合S变换时频图，从而识别出冲击特征频率。通过仿真信号和实验信号对所提改进方法的有效性进行验证，结果表明，该方法能够更加直观准确地识别出仿真信号的周期性冲击特征与齿轮箱行星齿轮的断齿故障特征。

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	潘高元1
	李舜酩1
	杜华蓉1
	朱彦祺1

关键词 ：齿轮箱, S变换, 断齿, 奇异值分解, 奇异值比值谱

Abstract：In order to identify the characteristic signal of a broken tooth failure in gearbox from the gearbox vibration signal accurately, a method combining the S-transform time-frequency spectrum with the singular value decomposition (SVD) denoising was proposed.By comparing and analyzing the difference between the singular value ratio spectrum and the singular value difference spectrum, a noise reduction method using the S-transform singular value ratio spectrum threshold was put forward.Firstly, the time-frequency matrix obtained by the S-transform of the signal was used as the SVD construction matrix.Then, a threshold was chosen to denoise the signal by analyzing the singular value ratio spectrum.Lastly, the inverse S-transform was performed on the denoised data matrix to obtain the time-domain impact characteristics.In the light of the S-transform time-frequency diagram, the impact characteristic frequency was identified.The effectiveness verification of the proposed method was carried out by comparing the simulation and experiment signal data.The results show that the method can identify the periodic impact characteristics of the simulation signals accurately and effectively, and can well identify the broken tooth vibration characteristics of the planetary gearbox.

Key words： gearbox S-transform singular value decomposition broken tooth singular value ratio spectrum

收稿日期: 2018-04-16 出版日期: 2019-09-15

引用本文:

潘高元1，李舜酩1，杜华蓉1，朱彦祺1. 齿轮箱断齿特征识别的S变换-SVD降噪组合方法[J]. 振动与冲击, 2019, 38(18): 256-263.
PAN Gaoyuan 1，LI Shunming 1，DU Huarong1，ZHU Yanqi1. Feature extracting method for gearbox tooth breakage under impact based on theS-transform time-frequency spectrum combined with the denoising by SVD. JOURNAL OF VIBRATION AND SHOCK, 2019, 38(18): 256-263.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2019/V38/I18/256

[1] 陈仕琦. 齿轮箱振动仿真及故障诊断研究[D].南京农业大学,2014,7-12.
CHEN Shiqi. Research on gearbox vibration simulation and fault diagnosis [D]. Nanjing Agriculture University, 2014,7-12.
[2] Jing Yuan,ZhengJia He,YanYang Zi,et al. Gearbox fault diagnosis of rolling mills using multiwavelet sliding window neighboring coefficient denoising and optimal blind deconvolution[J]. Science in China Series E: Technological Sciences,2009,52(10).
[3] Stockwell R G, Mansinha L, Lowe R P. Localization of the complex spectrum: the S transform[J]. IEEE Transactions on Signal Processing, 2002, 44(4):998-1001.
[4] Ling Lu,Wei Xu,Sanzheng Qiao. A fast SVD for multilevel block Hankel matrices with minimal memory storage[J]. Numerical Algorithms,2015,69(4).
[5] 赵学智,叶邦彦,林颖.奇异值分解对轴承振动信号中调幅特征信息的提取[J].北京理工大学学报,2011,31(05):572-577.
ZHAO Xuezhi, YE Bangyan, LIN Ying. Amplitude Modulation Feature Extraction of Bearing VibrationSignal Using Singular Value Decomposition [J]. Journal of Beijing Institute of Technology, 2011, 31(05): 572-577.
[6] 赵学智,叶邦彦,陈统坚.奇异值差分谱理论及其在车床主轴箱故障诊断中的应用[J].机械工程学报,2010,46(01):100-108.
ZHAO Xuezhi, YE Bangyan, CHEN Tongjian. Difference spectrum theory of singular value and its application to the fault diagnosis of headstock of lathe [J]. Journal of Mechanical Engineering, 2010, 46(1): 100-108.
[7] 朱怡,蒋思源.广义S变换时频谱SVD降噪的滚动轴承故障冲击特征提取方法[J].轴承,2016(11):53-57.
ZHU Yi， JIANG Siyuan. Rolling Bearing Default Impact Feature Extracting Method Based on Generalized S Transform Time-Frequency Spectrum Denoised by SVD [J]. Bearing, 2016(11): 53-57.
[8] 郭远晶,魏燕定,周晓军,等.基于S变换谱阈值去噪的冲击特征提取方法[J].振动与冲击,2014,33(21):44-50.
GUO Yuan-jing，WEI Yan-ding，ZHOU Xiao-jun,et al. An impact feature extracting method based on S-transformation spectrum threshold denoising [J]. Journal of Vibration and Shock, 2014, 33(21): 44-50.
[9] 朱海琴.齿轮断齿时的时频域特征和实例分析[J].河北冶金,2013(08):79-80+83.
ZHU Haiqing. Time and frequency domain features of and living example analysis for gear broken on gear wheel [J]. Hebei Metallurgy, 2013(08): 79-80+83.
[10] 赵学智,叶邦彦,陈统坚.基于小波—奇异值分解差分谱的弱故障特征提取方法[J].机械工程学报,2012,48(07):37-48.
ZHAO Xuezhi, YE Bangyan, CHEN Tongjian. Extraction method of faint fault feature based on wavelet-SVD difference spectrum [J]. Journal of Mechanical Engineering, 2012, 48(7): 37-48.
[11] 郭远晶,魏燕定,周晓军,等.S变换时频谱SVD降噪的冲击特征提取方法[J].振动工程学报,2014,27(04):621-628.
GUO Yuan-jing, WEI Yan-ding, ZHOU Xiao-jun,et al. Impact feature extracting method based on S transform time-frequency spectra denoised by SVD [J]. Journal of Vibration Engineering, 2014, 27(04): 621-628.
[12] 郭远晶,魏燕定,周晓军,等.S变换用于滚动轴承故障信号冲击特征提取[J].振动.测试与诊断,2014,34(05):818-822+972.
GUO Yuanjing, WEI Yanding, ZHOU Xiaojun,et al. Impact Feature Extraction From Rolling Bearing Fault Signal by S Transform [J]. Journal of Vibration, Measurement & Diagnosis, 2014, 34(05): 818-822+972.
[13] Kanjilal P P, Palit S, Saha G. Fetal ECG extraction from single-channel maternal ECG using singular value decomposition[J]. Biomedical Engineering, IEEE Transactions on, 1997, 44(1):51-59.
[14] Kanjilal P P, Saha G. Fetal ECG extraction from single channel maternal ECG using SVD and SVR spectrum[C]// Engineering in Medicine and Biology Society, 1995. IEEE, Conference. IEEE, 2002:187-188 vol.1.