基于阶频谱相关组合切片的滚动轴承故障特征提取

PDF(1236 KB)

振动与冲击 ›› 2018, Vol. 37 ›› Issue (20) : 45-49.

论文

夏均忠，汪治安，陈成法，于明奇，吕麒鹏

作者信息 +

A study on fault feature extraction of rolling bearings based on combined slice of order-frequency spectral correlation

XIA Junzhong,WANG Zhian,CHEN Chengfa,YU Mingqi,L Qipeng

Author information +

文章历史 +

摘要

变转速工况下，滚动轴承故障振动信号出现复杂的频率、幅值和相位调制等现象，基于角度/时间循环平稳（AT-CS）的阶频谱相关可以有效提取其故障特征，但是该方法存在计算量大的缺点，为此，应用组合切片思想，提出基于阶频谱相关组合切片的轴承故障特征提取方法。根据滚动轴承结构参数计算其理论故障特征阶次，并由此确定组合切片中心，再由设定的切片宽度得到组合切片区间并计算信号的阶频谱相关组合切片，通过切片之间的能量分布提取滚动轴承故障特征。通过仿真信号和实验数据验证了该方法的有效性，相比于阶频谱相关，阶频谱相关组合切片能更清晰准确地表征轴承故障特征，并具有较高的计算效率。

Abstract

In variable speed condition, complicated frequency, amplitude and phase modulation would appear in the vibration signals of fault bearings.And the fault feature can be effectively extracted based on the order-frequency correlation of the angle/time cyclostationary (AT-CS), but the method has limitation due to its large computation.In this paper, a method of bearing fault feature extraction was proposed based on the combination of slice spectrum by applying the ideal of combined slices to estimate the order-frequency correlation.According to the structural parameters of rolling bearings, the theoretical fault characteristic orders were calculated, and the combined slice center was obtained from the characteristic orders.The combined slice intervals were obtained after a proper choice of the slice width.And then combined slice of order-frequency of the signal was calculated.The fault feature of the rolling bearing was extracted through the energy distribution between the slices.The validity of the method was verified by the simulation signal and the experimental data，and it was concluded that compared with the order-frequency correlation, the combined slice of order-frequency spectral correlation can indicate the bearing failure characteristics more clearly and accurately and have a higher computational efficiency.

导出引用

夏均忠，汪治安，陈成法，于明奇，吕麒鹏. 基于阶频谱相关组合切片的滚动轴承故障特征提取[J]. 振动与冲击, 2018, 37(20): 45-49

XIA Junzhong,WANG Zhian,CHEN Chengfa,YU Mingqi,L Qipeng. A study on fault feature extraction of rolling bearings based on combined slice of order-frequency spectral correlation[J]. Journal of Vibration and Shock, 2018, 37(20): 45-49

参考文献

[1] 夏均忠，赵磊，白云川，等. 基于Teager能量算子和ZFFT的滚动轴承故障特征提取[J]. 振动与冲击，2017, 36(11): 106-110.
XIA Jun-zhong，LIU Yuan-hong，BAI Yun-chuan，et al. Fault feature extraction of rolling element bearings based on Teager energy operator and ZFFT[J]. Journal of Vibration and Shock, 2017, 36(11): 106-110.
[2] Randall R B，Antoni J. Rolling element bearing diagnostics—a tutorial[J]. Mechanical Systems and Signal Processing, 2011, 25(2): 485-520.
[3] Randall R B，Antoni J，Chobsaard S. The relationship between spectral correlation and envelope analysis for cyclostationary machine signals application to ball bearing diagnostics[J]. Mechanical Systems and Signal Processing, 2001, 15(5): 945-962.
[4] Raad A, Antoni J, Sidahmed M. Indicators of cyclostationarity: Theory and application to gear fault monitoring[J]. Mechanical Systems and Signal Processing, 2008, 22(3): 574-587.
[5] Girondin V, Pekpe K M, Morel H, et al. Bearings fault detection in helicopters using frequency readjustment and cyclostationary analysis[J]. Mechanical Systems and Signal Processing, 2013, 38(2): 499-514.
[6] Antoni J，Xin G，Hamzaoui N. Fast computation of the spectral correlation[J]. Mechanical Systems and Signal Processing, 2017, 92: 248-277.
[7] 林京，赵明. 变转速下的机械设备动态信号分析方法的回顾与展望[J]. 中国科学：技术科学，2015, 45(7): 669-686.
LIN Jing，ZHAO Ming. Dynamic signal analysis for speed-varying machinery: A review[J]. Science China Technological Sciences, 2015, 45(7): 669-686.
[8] Abboud D, Antoni J, Eltabach M, et al. Angle∖Time cyclostationarity for the analysis of rolling element bearing vibrations[J]. Measurement, 2015, 75:29-39.
[9] Sophie Baudin，Dider Remond，Antoni J，et al. Non-intrusive rattle noise detection in non-stationary conditions by an angle/time cyclostationary approach[J]. Journal of Sound and Vibration, 2016, 366(366): 501-513.
[10] Abboud D, Baudin S, Antoni J, et al. The spectral analysis of cyclo-non-stationary signals[J]. Mechanical Systems and Signal Processing, 2015, 75(5): 280-300.
[11] Abboud D, Antoni J. Order-frequency analysis of machine signals[J]. Mechanical Systems and Signal Processing, 2016, 87: 229-258.
[12] Zhou Y, Chen J, Dong G M, et al. Application of the horizontal slice of cyclic bispectrum in rolling element bearings diagnosis[J]. Mechanical Systems and Signal Processing, 2012, 26(1): 229-243.
[13] 毕果, 陈进. 组合切片分析在滚动轴承故障诊断中的应用研究[J]. 机械科学与技术, 2009, 28(2): 182-185.
BI Guo，CHEN Jin. The application of combination slice analysis of spectral correlation density in rolling element bearing diagnosis[J]. Mechanical Science and Technology for Aerospace Engineering, 2009, 28(2): 182-185.
[14] 明阳，陈进. 基于谱相关密度切片分析和SVM的滚动轴承故障诊断[J]. 振动与冲击, 2010, 29(1): 196-199+247.
MING Yang，CHEN Jin. Rolling spectral element bearing fault correlation density and support based on slice vector machine[J]. Journal of Vibration and Shock, 2010, 29(1): 196-199+247.
[15] 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): 114-118.
WANG Hong-chao，CHEN Jin，DONG Guang-min. Fault diagnosis of rolling bearings based on slice energy spectral correlation density[J]. Journal of Vibration and Shock, 2015, 34(3): 114-118.
[16] 程卫东，赵德尊，刘东东. 基于故障特征系数模板的变转速滚动轴承故障诊断[J]. 振动与冲击, 2017, 36(7): 123-129.
CHENG Wei-dong，ZHAO De-zun，LIU Dong-dong. Rolling element bearing fault diagnosis based on fault characteristic coefficient template under time-varying rotating speed[J]. Journal of Vibration and Shock, 2017, 36(7): 123-129.
[17] Mishra C，Samantaray A K，Chakraborty G，et al. Rolling element bearing defect diagnosis under variable speed operation through angle synchronous averaging of wavelet de-noised estimate[J]. Mechanical Systems and Signal Processing, 2016, 72-73: 206-222.