基于自适应奇异值分解的行星齿轮箱故障诊断方法

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振动与冲击 ›› 2018, Vol. 37 ›› Issue (17) : 122-127.

论文

秦毅 1，2 ，张清亮 1，2，赵月 1，2

作者信息 +

Fault diagnosis method for planetary gearboxes based on adaptive SVD

QIN Yi 1, 2 ZHANG Qingliang 1, 2 ZHAO Yue 1, 2

Author information +

文章历史 +

摘要

行星齿轮箱振动信号中的故障特征通常淹没在噪声信号中，因此有必要研究如何提取这些微弱故障特征。针对传统奇异值分解特征提取方法中不能自动选择有效奇异值数目的问题，提出了一种基于自适应奇异值分解的行星齿轮箱故障诊断方法。首先根据一定的条件，通过该方法选择几个不同的有效奇异值数目并得到几组不同的重构信号；再根据这些重构信号的偏态绝对值，自动选择最佳的重构信号；最后进行包络分析，得到故障信号的包络谱。仿真和实验对比结果表明，此方法相比于传统的奇异值分解特征提取方法，能够更好地在消除噪声和提取行星齿轮箱振动信号中的微弱故障特征。

Abstract

Since fault features in planetary gearbox vibration signals are usually submerged in noise signals,it is necessary to study how to extract these weak fault features. Aiming at the problem of traditional feature extraction method based on the singular value decomposition (SVD) being not able to automatically select the amount of effective singular values,a new fault diagnosis method for planetary gearboxes based on adaptive SVD was proposed. Firstly,according to a certain condition,several different amounts of effective singular values were selected with the proposed method to get several reconstructed signals. Secondly,the optimal reconstructed signal was automatically chosen according to the skew absolute values of these reconstructed signals. At last,the envelope analysis was done for the optimal reconstructed signal to acquire the envelope spectrum of the fault signal. The contrastive results between simulation and tests showed that compared to the traditional feature extraction method based on SVD,the proposed method can better de-noise and extract weak fault features in planetary gearbox vibration signals.

导出引用

秦毅 1，2,张清亮 1，2，赵月 1，2. 基于自适应奇异值分解的行星齿轮箱故障诊断方法[J]. 振动与冲击, 2018, 37(17): 122-127

QIN Yi 1, 2 ZHANG Qingliang 1, 2 ZHAO Yue 1, 2. Fault diagnosis method for planetary gearboxes based on adaptive SVD[J]. Journal of Vibration and Shock, 2018, 37(17): 122-127

参考文献

[1] 饶振纲. 行星齿轮传动设计[M]. 化学工业出版社, 2014.
RAO Zhen-gang. Design of planetary gear transmission[M]. Chemical Industry Publisher, 2014.
[2] Lin J, Zuo M J. Gearbox fault diagnosis using adaptive wavelet filter[J]．Mechanical systems and Signal processing, 2003, 17(6):1259-1269.
[3] Ruiz-Carcel C, Hernani-Ros E, Cao V, et al. Application of Linear Prediction, Self-Adaptive Noise Cancellation, and Spectral Kurtosis in Identifying Natural Damage of Rolling Element Bearing in a Gearbox[J]. International Journal of Acoustics & Vibrations, 2012, 20(1):15-23.
[4] 李永军;马立元;王天辉;段永刚. 一种新的窄带信号去噪方法仿真分析[J]. 振动与冲击, 2012, 31(1): 140-143.
LI Yong-jun, MA Li-yuan, WANGg Tian-hui, DUAN Yong-gang. A simulation research on a new combination denoise algorithm for narrow-band signal[J]. Journal of Vibration and Shock, 2012, 31(1): 140-143.
[5] Yu J, Makis V. Wavelet analysis with time-synchronous averaging of planetary gearbox vibration data for fault detection and diagnostics[C]// IEEE International Conference on Computer Science and Automation Engineering. IEEE, 2011:417-421.
[6] 代士超, 郭瑜, 伍星. 基于同步平均与倒频谱编辑的齿轮箱滚动轴承故障特征量提取[J]. 振动与冲击, 2015, 34(21): 205-209.
DAI Shi-chao, GUO Yu, WU Xing. Rolling element bearing characteristics extraction based on edited cepstrum and time domain synchronous average[J]. Journal of Vibration and Shock, 2015, 34(21): 205-209.
[7] 陈仁祥,汤宝平,马婧华. 基于EEMD的振动信号自适应降噪方法[J]. 振动与冲击,2012, 31(15):82-86.
CHEN Ren-xiang, TANG Bao-ping, MA Jing-hua. Adaptive de-noising method based on ensemble empirical mode decomposition for vibration signal[J]. Journal of Vibration & Shock, 2012, 31(15): 82-86.
[8] 杨望灿, 张培林, 王怀光, 陈彦龙, 孙也尊. 基于EEMD的多尺度模糊熵的齿轮故障诊断[J]. 振动与冲击, 2015, 34(14): 163-167.
YANG Wang-can, ZHANG Pei-lin, WANG Huai-guang, CHEN Yan-long, SUN Ye-zun. Gear fault diagnosis based on multiscale fuzzy entropy of EEMD[J]. Journal of Vibration and Shock, 2015, 34(14): 163-167.
[9] QIN Yi, WANG Jia-xu, MAO Yong-fang. Dense framelets with two generators and their application in mechanical fault diagnosis [J]. Mechanical Systems and Signal Processing, 2013, 40(2): 483-498.
[10] 隋文涛, 张丹. 基于峭度的阈值降噪方法及其在振动信号分析中的应用[J]. 振动与冲击, 2013, 32(7): 155-158.
SUI Wen-tao, ZHANG Dan. Kurtosis-based threshold denoising method and its application in vibration signal analysis[J]. Journal of Vibration and Shock, 2013, 32(7): 155-158.
[11] Qiao Z, Pan Z. SVD principle analysis and fault diagnosis for bearings based on the correlation coefficient[J]. Measurement Science & Technology, 2015, 26(8): 085014.
[12] 陈恩利,张玺,申永军,等. 基于SVD降噪和盲信号分离的滚动轴承故障诊断[J]. 振动与冲击, 2012, 31(23): 185-190.
CHEN En-li, ZHANG Xi, SHEN Yong-jun, et al. Fault diagnosis of rolling bearings based on SVD denoising and blind signals separation[J]. Journal of Vibration and Shock, 2012, 31(23): 185-190.
[13] 王敏, 周磊, 周树道,等. 基于峰值信噪比和小波方向特性的图像奇异值去噪技术[J]. 应用光学, 2013, 34(1):85-89.
WANG Min, ZHOU Lei, ZHOU Shu-dao, et al. Image SVD denoising based on PSNR and wavelet directional feature[J]. Journal of Applied Optics, 2013, 34(1):85-89.
[14] Zhao X, Ye B. Selection of effective singular values using difference spectrum and its application to fault diagnosis of headstock[J]. Mechanical Systems and Signal Processing, 2011, 25(5):1617-1631.
[15] 何志坚, 周志雄. 基于FSWT细化时频谱SVD降噪的冲击特征分离方法[J]. 中国机械工程, 2016, 27(9):1184-1190.
HE Zhi-jian, ZHOU Zhi-xiong. Impact Feature Separation Method Based on FSWT Zoom Time-frequency Spectrum De-noised by SVD. China Mechanical Engineering, 2016, 27(09): 1184-1190.
[16] 贾永娜. 基于改进奇异谱分析法的地震数据插值[D]. 哈尔滨工业大学, 2014.
JIA Yong-na. Seismic data interpolation via improved singular spectrum analysis[D]. Harbin Institute of Technology, 2014.
[17] 李乔.矩阵论八讲[M].上海: 上海科技出版社，1988．
LI Qiao. Eight lectures on matrix theory[M]. Shanghai: Shanghai Science and Technology Press，1988．
[18] 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(7):37-48.
ZHAO Xue-zhi, YE Bang-yan, CHEN Tong-jian. Extraction Method of Faint Fault Feature Based on Wavelet-SVD Difference Spectrum[J]. Journal of Mechanical Engineering, 2012, 48(7):37-48.
[19] 冯志鹏, 褚福磊, 左明健. 行星齿轮箱振动故障诊断方法[M]. 科学出版社, 2015.
FENG Zhi-peng, ZHU Fu-lei, ZUO Ming-jian. Vibration fault diagnosis methods of planetary gear box[M]. Science Press, 2015.