基于双调Q小波变换的瞬态成分提取及轴承故障诊断应用研究

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振动与冲击 ›› 2015, Vol. 34 ›› Issue (10) : 34-39.

论文

项巍巍，蔡改改，樊薇，黄伟国，朱忠奎

作者信息 +

The research of transient feature extraction based on double-TQWT and the application in bearing fault feature extraction

XIANG Wei-wei，CAI Gai-gai，FAN Wei，HUANG Wei-guo，ZHU Zhong-kui

Author information +

文章历史 +

摘要

因轴承的剥落、裂纹等局部故障易致运行时振动信号中出现瞬态成分，而轴承故障振动信号为非平稳信号，含高、低振荡成分，传统的线性信号处理方法及基于频率的分解方法均存在一定局限性。对此，研究基于信号振荡特征而非频率特征的双调Q小波变换，设定不同Q因子小波将轴承故障信号非线性分解成低、高振荡及噪声成分，轴承故障瞬态成分对应低振荡成分，提取低振荡成分即能实现轴承故障瞬态成分提取。通过轴承故障状态下瞬态成分检测表明，该方法能有效提取轴承故障瞬态成分。经与均值滤波、小波阈值及经验模态分解(EMD)等方法比较，验证该方法的优越性。

Abstract

Localized faults in rotating machinery parts tend to result in shocks and thus arouse transient impulse responses in the vibration signal. In order to realize bearing fault diagnosis under strong noise condition, it is crucial to extract fault feature from vibration signal. Yet, vibration signal is non-stationary signal, which consists of high and low resonance components, traditional linear methods and signal decomposition methods based on frequency all have some disadvantages. To overcome these disadvantages, a nonlinear signal analysis method named double tunable Q-factor wavelet transform (double-TQWT) is proposed，which is based on signal resonance characteristics rather than the frequency. By using the double-TQWT, the vibration signal is decomposed into high and low resonance components based on the different resonance characteristics. The transient feature has a low Q-factor and can be decomposed into low resonance component. Applications in extracting fault feature for bearing fault signals under strong noise condition, results show the new method outperforms the average filtering method，the wavelet threshold algorithm, and the EMD, further confirm the validity and superiority of this method for transient feature extraction.

导出引用

项巍巍，蔡改改，樊薇，黄伟国，朱忠奎. 基于双调Q小波变换的瞬态成分提取及轴承故障诊断应用研究[J]. 振动与冲击, 2015, 34(10): 34-39

XIANG Wei-wei，CAI Gai-gai，FAN Wei，HUANG Wei-guo，ZHU Zhong-kui . The research of transient feature extraction based on double-TQWT and the application in bearing fault feature extraction[J]. Journal of Vibration and Shock, 2015, 34(10): 34-39

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