随机共振降噪下的齿轮微弱故障特征提取

摘要/Abstract

摘要：

针对强背景噪声下的齿轮微弱故障特征提取问题，提出了一种将级联单稳随机共振与经验模式分解(EMD)-Teager能量算子解调方法相结合的特征提取方法。首先对含噪故障信号进行随机共振输出，降噪后再进行经验模式分解，分解得到具有不同特征时间尺度的固有模态函数(IMFs)，最后通过Teager能量算子解调方法求取每个有效IMF分量的幅频信息，从而提取齿轮微弱故障特征。仿真分析和实际测试结果均表明，通过随机共振降噪后，该方法能有效检测出齿轮局部损伤故障特征频率。

关键词: 级联, 单稳随机共振, 经验模式分解, Teager能量算子

Abstract:

Aimed at the feature extraction problem of weak gear faults under strong background noise, an early feature extraction method was proposed based on cascaed monostable stochastic resonance(CMSR) system and EMD with Teager energy operator demodulating. Firstly CMSRS was employed as the preprocessing to remove noise, and then the denoised signals were decomposed into a series of intrinsic mode functions(IMFs) of different scales by EMD. Finally, Teager energy operator demodulating was applied to get amplitudes and frequencies of each effective IMF so as to extract the faint gear fault features. The simulation and application results show that the proposed method can detect the characteristic frequency of gear faults of local damage effectively after the noise reduction by CMSR.

Key words: cascaded, monostable stochastic resonance, empirical mode decomposition(EMD), Teager energy operator

中图分类号:

赵军, 崔颖, 赖欣欢, 孔明, 林敏. 随机共振降噪下的齿轮微弱故障特征提取[J]. 中国机械工程, 2014, 25(4): 539-546.

Zhao Jun, Cui Ying, Lai Xinhuan, Kong Ming, Lin Min. Weak Feature Extraction of Gear Faults Based on Stochastic Resonance Denoising[J]. China Mechanical Engineering, 2014, 25(4): 539-546.

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http://www.cmemo.org.cn/CN/Y2014/V25/I4/539

参考文献

[1]Lei Y G，Zuo M J．Gear Crack Level Identification Based on Weighted K Nearest Neighbor Classification Algorithm[J]．Mechanical Systems and Signal Processing，2009，23(5)：1535-1547.
[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]Huang N E，Shen Zheng，Long S R．The Empirical Mode Decomposition and the Hilbert Spectrum for Non-linear and Non-stationary Time Series Analysis[J]．Proceeding of Royal Society of London A，1998，454：903-995．
[4]冷永刚，王太勇，胡世广，等．级联双稳系统的随机共振特性[J]．物理学报，2005，54(3)：1118-1124．
Leng Yonggang，Wang Taiyong，Hu Shiguang，et al. Stochastic Resonance Behaviors of Bistable Systems Connected in Series[J]. Acta Physica Sinica，2005，54(3)：1118-1124．
[5]Stocks N G，Stein N D，McClintock P V E．Stochastic Resonance in Monostable Systems[J]．Phys．Rev．E，1993，26(7)：385-390．
[6]Vilar J M G，Rubf J M．Divergent Signal-noise Ratio and Stochastic Resonance in Monostable Systems[J]．Phys．Rev．Lett.，1996，77(4)：2863-2866．
[7]Grigorenko A N，Nikitin S I，Roschepkin G V．Stochastic Resonance at Higher Harmonics in Monostable Systems[J]. Phys．Rev．E，1997，56(5)：4907-4910．
[8]万频，詹宜巨，李学聪，等．一种单稳随机共振系统信噪比增益的数值研究[J]．物理学报，2011，60(4)：53-59．
Wan Pin，Zhan Yiju，Li Xuecong，et al. Numerical Research of Signal-to-noise Ratio Gain on a Monostable Stochastic Resonance[J]. Acta Phys. Sin.，2011，60(4)：53-59．
[9]程军圣，于德介，杨宇. 基于EMD的能量算子解调方法及其在机械故障诊断中的应用[J]．机械工程学报，2004，40(8)：115-118．
Cheng Junsheng，Yu Dejie，Yang Yu. Energy Operator Demodulating Approach Based on EMD and Its Application in Mechanical Fault Diagnosis[J].Journal of Mechanical Engineering，2004，40(8)：115-118.
[10]Alexandros P，Petros M．A Comparsion of the Energy Operator and Hilbert Transform Approach to Signal and Speech Demodulation[J]．Signal Processing，1994，37(1)：95-120．
[11]Alfonsi L，Gammaitoni L，Santucci S，et al．Intrawell Stochastic Resonance Versus Interwell Stochastic Resonance[J]．Phys．Rev．E，2000，62(1)：299-302．
[12]Peng Z K，Peter W T，Chu F L．An Improved Hilbert Huang Transform and Its Application in Vibration Signal Analysis[J]．Journal of Sound and Vibration，2005，286(23)：187-205．
[13]Teager H M．Some Observations on Oral Air Flow during Phonation[J]．IEEE Transactions on Signal Processing，1980，25(5)：599-601．
[14]丁康，李巍华，朱小勇．齿轮及齿轮箱故障诊断实用技术[M]．北京：机械工业出版社，2005．
[15]冷永刚，王太勇，李瑞欣，等．变尺度随机共振用于机电故障的监测诊断[J]．中国机电工程学报，2003，23(11)：111-115．
Leng Yonggang，Wang Taiyong，Li Ruixin，et al. Scale Transformation Stochastic Resonance for the Monitoring and Diagnosis of Electromotor Faults[J]. Proceedings of the CSEE，2003，23(11)：111-115.