Feature extraction of rolling bearing's early weak fault based on EEMD and tunable Q-factor wavelet transform
Feature extraction of rolling bearing's early weak fault based on EEMD and tunable Q-factor wavelet transform
复制标题
DOI：
10.1016/j.ymssp.2014.04.006
复制
发表时间：
2014-10-03
影响因子：
8.4
通讯作者：
Dong, Guangming
中科院分区：
文献类型：
--
作者：
Wang, Hongchao;Chen, Jin;Dong, Guangming
当滚动轴承出现早期微弱故障时，传统的故障诊断方法如快速傅立叶变换（FFT）和包络解调等难以提取故障特征。可调Q因子小波变换（TQWT）是对传统单Q因子小波变换的改进，非常适合于分离滚动轴承故障时低Q因子的瞬态冲击分量和高Q因子的持续振荡分量。然而，直接利用TQWT难以很好地提取滚动轴承早期微弱故障特征。包围式经验模态分解（EEMD）是经验模态分解（EMD）的改进，它既具有EMD自适应的优点，又克服了EMD的模态混叠问题。对滚动轴承早期微弱故障的原始信号进行EEMD分解，得到若干个本征模态函数。然后选取峰度指标值最大的IMF进行TQWT处理。最后，将包络解调法应用于低Q值瞬态冲击分量的提取，取得了满意的效果。(C)2014爱思唯尔有限公司版权所有。
When early weak fault emerges in rolling bearing the fault feature is too weak to extract using the traditional fault diagnosis methods such as Fast Fourier Transform (FFT) and envelope demodulation. The tunable Q-factor wavelet transform (TQWT) is the improvement of traditional one single Q-factor wavelet transform, and it is very fit for separating the low Q-factor transient impact component from the high Q-factor sustained oscillation components when fault emerges in rolling bearing. However, it is hard to extract the rolling bearing' early weak fault feature perfectly using the TQWT directly. Ensemble empirical mode decomposition (EEMD) is the improvement of empirical mode decomposition (EMD) which not only has the virtue of self-adaptability of EMD but also overcomes the mode mixing problem of EMD. The original signal of rolling bearing' early weak fault is decomposed by EEMD and several intrinsic mode functions (IMFs) are obtained. Then the IMF with biggest kurtosis index value is selected and handled by the TQWT subsequently. At last, the envelope demodulation method is applied on the low Q-factor transient impact component and satisfactory extraction result is obtained. (C) 2014 Elsevier Ltd. All rights reserved.