基于双树复小波包自适应Teager能量谱的滚动轴承早期故障诊断

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振动与冲击 ›› 2017, Vol. 36 ›› Issue (10) : 84-92.

论文

任学平,王朝阁，张玉皓，王建国

作者信息 +

Early Fault Diagnosis of Rolling Bearing Based on Dual-tree Complex Wavelet Packet Transform Adaptive Teager Energy Spectrum

REN Xue-ping, WANG Chao-ge, ZHANG Yu-hao, WANG Jian-guo

Author information +

文章历史 +

摘要

针对滚动轴承早期故障特征信息难以识别以及带通滤波器参数设置依赖使用者经验等造成共振带不能有效确定并自适应提取的问题，提出了频带幅值熵的概念。在此基础上，将双树复小波包变换和Teager能量谱结合，提出了基于双树复小波包变换自适应Teager能量谱的早期故障诊断方法。该方法首先利用双树复小波包将采集到的振动信号进行分解，并计算各子带的频带幅值熵。然后将熵值按升序排列后依次作为阈值，提取频带幅值熵大于或等于阈值的子带，依据峭度指标确定最佳熵阈值和双树复小波包最佳分解层数，从而自适应并有效地提取出共振带。最后对共振带进行Teager能量谱分析，即可从中准确地识别出轴承的故障特征频率。通过信号仿真与工程实验数据分析验证了该方法的有效性与优越性。

Abstract

Aiming at the early fault feature information of rolling bearings is difficult to identify, and the parameter setting of band-pass filter depends on the user experience, which makes the resonance frequency band can’t be effectively determined and extracted, the concept of amplitude entropy of frequency band is proposed. On this basis, the dual-tree complex wavelet packet transform and Teager energy spectrum was combined, a rolling bearing early fault feature extraction method is proposed based on dual-tree complex wavelet packet transform adaptive Teager energy spectrum. Firstly, original fault signals were decomposed into several different frequency components through wavelet packet transform, and the frequency amplitude entropy of each sub-band was calculated. Then the entropy were arranged in ascending order and in turn as a threshold to extract the entropy value greater than the threshold value of the sub bands. Based on kurtosis index to determine the optimal threshold and the best dual tree complex wavelet packet decomposition levels, thus, the resonance band was extracted adaptively and effectively. Finally, the Teager energy spectrum analysis of the resonance band was performed to identify the frequency of the bearing fault. Through the signal simulation and engineering experiment data analysis it verifies the effectiveness of the proposed method.

导出引用

任学平,王朝阁，张玉皓，王建国. 基于双树复小波包自适应Teager能量谱的滚动轴承早期故障诊断[J]. 振动与冲击, 2017, 36(10): 84-92

REN Xue-ping, WANG Chao-ge, ZHANG Yu-hao, WANG Jian-guo. Early Fault Diagnosis of Rolling Bearing Based on Dual-tree Complex Wavelet Packet Transform Adaptive Teager Energy Spectrum[J]. Journal of Vibration and Shock, 2017, 36(10): 84-92

参考文献

[1] Wang Yi , Xu Guang-hua , Liang Lin, et al. Detection of weak transient signals based on wavelet packet transform and manifold learning for rolling element bearing fault
     diagnosis[J]. Mechanical Systems and Signal Processing,2015,54: 259-276.
[2] 何正嘉，袁静，訾艳阳.机械故障诊断的内积变换原理与应用[M].北京：科学出版社，2011.
     HE Zheng-jia, YUAN Jing, ZI Yan-yang. Inner product transform principle and application in mechanical fault diagnosis [M]. Beijing: Science Press, 2011.
[3] 崔玲丽，康晨辉，张建宇，等.基于时延相关及小波包系数熵阈值的增强型共振解调方法[J].机械工程学报，    2010,46（20）：53-57.
     CUI Ling-li, KANG Chen-hui, ZHANG Jian-yu, et al. Enhanced resonance demodulation based on the delayed correlation and entropy threshold of wavelet
packet coefficients[J]. Chinese Journal of Mechanical Engineering, 2010,46（20）：53-57.
[4] 田福庆，罗荣，贾兰俊，等.机械故障非平稳特征提取方法及其应用[M].北京：国防工业出版社，2014.
     TIAN Fu-gui, LUO Rong, JIA Lan-jun, et al. Method and application of non stationary feature extraction of mechanical failure[M]. Beijing: National Defense Industry Press, 2014
[5] 张进，冯志鹏，褚福磊.滚动轴承故障特征的时间-小波能量谱提取方法[J].机械工程学报，2011,47(17):44-49.
ZHANG Jin , FENG Zhi-peng ,CHU Fu-lei . Extraction of rolling bearing fault feature based on time-wavelet energy spectrum [J]. Chinese Journal of Mechanical Engineering,
2011,47(17):44-49.
[6] 王天金，冯志鹏，郝如江，等.基于Teager能量算子的滚动轴承故障诊断研究[J].振动与冲击,2012，31(2):1-5.
     WANG Tian-jin，FENG Zhi-peng，HAO Ru-jiang，et al.Fault diagnosis of rolling element bearings based on Teager energy operator[J]. Journal of Vibration and Shock,
2012,31(2):1-5.
[7] Cheng Jun-sheng, Yu De-jie, Yang Yu. The application of energy operator demodulation approach based on EMD in machinery fault diagnosis[J].Mechanical Systems and
     Signal Processing, 2007, 47 (6):1011-1020.
[8] 周智，朱永生，张优云，等.基于EEMD和共振解调的滚动轴承自适应故障诊断[J].振动与冲击，2013,32（2）：76-80.
    Zhou Zhi, Zhu Yong-sheng, Zhang You-yun, et al.Adaptive fault diagnosis of rolling bearings based on EEMD and demodulated resonance[J].Journal of Vibration and
     Shock,2013,32(2)：76-80.
[9] Bayram, Ivan Wesnick. On the dual-tree complex wavelet packet transform and M-Band transforms[J].IEEE  Transactions on Signal processing,2008,56(6):2298-2310.
[10] Wang Yan-xue, He Zheng-jia, Zi Yan-yang. Enhancement of signal denoising and multiple fault signatures detecting in rotating machinery using dual-ture complex wavelet
transform[J].Mechanical system and signal processing,2010,24(1):119-137.
[11]Selesnick I W, Baraniuk R G, Kingsbury N G.The dual-tree complex wavelet transform [J].IEEE Digital Signal Processing Magazine, 2005, 22(6):123-151.
[12] 李辉，郑海起，唐力伟.基于双树复小波包峭度图的轴承故障诊断研究[J].振动与冲击，2010,31(10)：13-18.
     LI Hui, ZHENG Hai-qi, TANG Li-wei. Bearing fault diagnosis based on kurtogram of dual-tree complex wavelet packet transform[J]. Journal of Vibration and
Shock, 2010,31(10)：13-18.
[13] 胥永刚，孟志鹏，陆明.基于双树复小波包变换的滚动轴承故障诊断[J].农业工程学报，2013,29(10):49-56.
     XU Yong-gang, MENG Zhi-peng, LU Ming. Fault diagnosis of rolling bearing based on dual-tree complex wavelet packet transform[J]. Transactions of the Chinese Society of Agricultural Engineering, 2013, 29(10):49－56.
[14] Kingsbury N. Design of Q-shift complex wavelets for Image processing using frequency domain energy minimisation //Proceedings of the IEEE Conference on
Image Processing, Barcelona, 2003: 1(1):1013-1016.
[15] 王小玲，陈进.丛飞云.基于时频的频带熵方法在滚动轴承故障识别中的应用[J].振动与冲击，2011,31（18）：29-33.
     WANG Xiao-ling, CHEN Jin, CONG Fei-yun. Application of spectral band entropy (SBE) method in rolling bearing fault diagnosis based on time-frequency analysis[J]. Journal of Vibration and Shock,2011,31(18):29-33.
[16] 段晨东，郭研.基于提升小波包变换的滚动轴承包络分析诊断方法[J].农业机械学报，2007,39（5）：192-196.
     DUAN Chen-dong , Guo Yan. An envelop analysis approach for ball bearing based on lifting wavelet packet transform[J]. Transactions of the Chinese Society for
    Agricultural Machinery, 2007,39(5):192-196.
[17] Xiang Yong. A further study of the kurtosis-based method for bearing diagnostics[J].Mechanical Systems and Signal Processing,2007,21(1):593-595.
[18] Antoni J, Bonnardot F, Raad A, et al. Cyclostationary modeling of rotating machine vibration signals [J]. Mechanical Systems and Signal Processing, 2004, 18(16):
     1285-1314.
[19] Sawalhi N, Randall R B. Vibration response of spalled roll-ing element bearings: Observations, simulations and signal Processing techniques to track the spall size[J].Mechanical
     Systems and Signal Processing , 2011, 25:846-870.