特征提取在滚动轴承故障诊断中起着至关重要的作用，然而实测的振动信号本质上是复杂的、非平稳的，同时故障轴承的脉冲特征常常淹没于噪声中。为了有效提取强噪声背景下的滚动轴承故障信息，提出一种基于总变差去噪(Total variation denoising，TVD)和快速谱相关（Fast spectral correlation，Fast-SC）相结合即TVD-Fast SC故障特征提取方法。首先，利用总变差去噪方法对振动信号进行消噪，提高信号的信噪比；然后，对去噪后的信号进行快速谱相关分析，准确地识别出轴承的故障特征频率。仿真和实验结果表明，该方法可以有效地提取出滚动轴承的微弱故障特征信息，分析效果优于直接快速谱相关方法和小波阈值去噪与快速谱相关结合的方法，为滚动轴承微弱故障特征提取提供一种有效的方法。
Abstract
Feature extraction plays a crucial role in rolling bearing fault diagnosis. However, vibration signals measured are complex and non-stationary inherently, and pulse features of faulty bearings are often submerged in noise. Here, in order to effectively extract the fault information of rolling bearings under strong background noise, a fault feature extraction method based on combination of total variation de-noising and fast spectral correlation (TVD-FSC) was proposed. Firstly, the total variation de-noising method was used to de-noise vibration signals and improve their signal-to-noise ratios. Then, the fast spectral correlation analysis was performed for de-noised signals to correctly identify fault feature frequencies of a bearing. Simulation and test results showed that the proposed method can be used to effectively extract the weak fault feature information of rolling bearings, and its analysis results are better than those using the direct fast spectral correlation method and the one combining the wavelet threshold de-noising with the fast spectral correlation, respectively; it provides an effective way for extracting weak fault features of rolling bearings.
关键词
快速谱相关 /
总变差去噪 /
故障诊断 /
特征提取
{{custom_keyword}} /
Key words
fast spectral correlation /
total variation denoising /
fault diagnosis /
feature extraction
{{custom_keyword}} /
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] 毕果，陈进. 基于谱相关的齿轮振动监测技术研究[J].振动与冲击，2009，28(07)：17-21+209-210.
BI Guo, CHEN Jin. Condition monitoring technology for gear vibration based on spectral correlation [J]. Journal of Vibration and Shock，2009，28(07)：17-21+209-210.
[2] 毕果，陈进，周福昌，等. 调幅信号谱相关密度分析中白噪声影响的研究[J].振动与冲击，2006(02)：75-78+185-186.
BI Guo, CHEN jin, ZHOU Fuchang, et al. Influence of the noise on spectral correlation density analysis of AM signal [J]. Journal of Vibration and Shock，2006，(02)：75-78+185-186.
[3] 柳亦兵，辛卫东，李宏，等. 滚动轴承故障的谱相关特征分析[J].中国机械工程，2013,24(03)：351-355.
LIU Yibing, XIN Weidong, LI Hong, et al. Spectral correlation feature analysis for rolling bearing faults [J]. China Academic Journal Electronic Publishing House, 2013,24(03)：351-355.
[4] 王宏超，向国权，郭志强，等. 基于改进时频谱分析方法的滚动轴承复合故障诊断[J].航空动力学报，2017,32(07):1698-1703.
WANG Hongchao, XIANG Guoquan, GUOZhiqiang, et al. Fault diagnosis of rolling bearing’ compound faults based on improved time-frequency spectrum analysis method [J].Journal of Aerospace Power, 2017,32(07):1698-1703.
[5] Javorskyj I, Leskow J, Kravets I, Isayev I, Gajecka E. Linear filtration methods for statistical analysis of periodically correlated random processes - part I: Coherent and component methods and their generalization [J]. Signal Processing,2012,92 (7):1559–1566.
[6] Antoni J. Cyclic spectral analysis in practice[J]. Mechanical Systems and Signal Processi-ng,2007, 21(2):597-630.
[7] 刘远宏，刘健敏，冯辅周,等. 基于2阶循环谱和SVM的汽车传动轴故障诊断[J].噪声与振动控制，2014,34(1):160-163.
LIU Yuanhong, LIU Jianmin, FENG Fuzhou. et al. Fault diagnosis of an automobile’s transmission shaft based on second order cyclic spectrum and support vector machine [J].Noise and Vibration Control, 2014,34(1):160-163.
[8] 王宏超，陈进，董广明. 基于谱相关密度组合切片能量的滚动轴承故障诊断研究[J].振动与冲击，2015,34(3):114-117.
WANG Hongchao, CHEN Jin, DONG Guangming. Fault diagnosis of rolling bearings based on slice energy spectral correlation density [J]. Journal of Vibration and Shock,2015,34(3):114-117.
[9] Antoni J, Hanson D, Detection of surface ships from interception of cyclostationary signature with the cyclic m
[10] odulation coherence [J].IEEE Journal of Oceanic Engineering,2012,37(3):478–493.
[11] Antoni J, GE Xin, Hamzaoui N, Fast computation of the spectral correlation [J]. Mechanical Systems and Signal Processing ,2017,92:248-277.
[12] ZHANG Suofeng, WANG Yanxue, HE Shuilong, et al. Bearing fault diagnosis based on variational made decomposition and total variation denoising [J]. Measurement Science and Technology.27(7) 075101.
[13] 隋文涛，张丹. 总变差降噪方法在轴承故障诊断中的应用[J].振动、测试与诊断，2014,34(6):1033-1037.
SUI Wentao, ZHANG Dan. Total Variation Denoising Method and Its Application in Fault Diagnosis of Bearings [J]. Journal of Vibration, Measurement & Diagnosis,2014,34(6):1033-1037.
[14] Rudin L I, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms [J]. Physica. Section D:Nonlinear Phenomena,1992,60(1-4):259-268.
[15] Selesnick I. Total variation denoising (an MM algorithm).2017.1.23. NYU Polytechnic School of Engineering Lecture Notes.(http://eeweb.poly.edu/iselesni/lecture_notes/TVDmm/TVDmm.pdf)
[16] 周福昌. 基于循环平稳信号处理的滚动轴承故障诊断方法研究[D]. 上海交通大学, 2006.
ZHOU Fuchang. Research on the fault diagnosis method of rolling element bearing based on cyclostationary signal process [D]. Shanghai Jiao Tong University,2006.
[17] 王维，张英堂，任国全.小波阈值降噪算法中最优分解层 数的自适应确定及仿真[J].仪器仪表学报,2009,30(3):526-530.
WANG Wei, ZHANG Yingtang, REN Guoquan. Adaptive selection and simulation of decomposition level in threshold denoising algorithm based on wavelet transform[J]. Chinese Journal of Scientific Instrument, 2009,30(3):526-530.
[18] 邵忍平,曹精明,李永龙.基于EMD小波阈值去噪和时频分析的齿轮故障模式识别与诊断[J].振动与冲击,2012,31(08):96-101+106.
SHAO Renping, CAO Jingming, LI Yonglong. Gear fault pattern identification and diagnosis using Time-Frequency analysis and wavelet threshold denoising based on EMD [J]. Journal of Vibration and Shock, 2012,31(08):96-101+106.
[19] 王蓓,张根耀,李智,王静.基于新阈值函数的小波阈值去噪算法[J].计算机应用,2014,34(05):1499-1502.
WANG Bei, ZHANG Genyao, LI Zhi, et al. Wavelet threshold denoising algorithm based on new threshold function [J]. Journal of Computer Applications, 2014,34(05):1499-1502.
[20] 张晓宁,孙丽君.一种改进的小波阈值信号去噪方法[J].电子科技,2012,25(11):15-17+24.
ZHANG Xiaoning, SUN Lijun. An improved method for wavelet thresholding signal denoising[J]. Electronic Science and Technology, 2012,25(11):15-17+24.
{{custom_fnGroup.title_cn}}
脚注
{{custom_fn.content}}
PDF(1351 KB)
