针对滚动轴承早期故障信息微弱，频率切片小波变换（FSWT）在强背景噪声中提取故障特征的不足，提出变分模态分解（VMD）奇异值分解（SVD）联合降噪与FSWT相结合的故障特征提取方法，首先利用VMD故障信号自适应分解为若干本征模态分量（IMF），通过峭度准则选择包含故障信息最丰富的IMF进行信号重构，其次利用SVD对重构信号进行再次降噪，提高信噪比。最后对降噪信号进行FSWT，凸显故障信号的时频分布信息提取故障特征。仿真信号和实际数据分析结果表明，该方法有效消除了噪声的影响，能够清晰提取故障信号的特征频率，实现滚动轴承故障的精准识别。
Abstract
Aiming at problems of early fault features of rolling bearings being weak and the deficiency of the frequency sliced wavelet transformation (FSWT)’s extracting fault features under strong background noise,a new fault feature extracting method using FSWT and joint de-noising of the variational mode decomposition(VMD) and the singular value decomposition (SVD) was proposed. Firstly,VMD was used to decompose adaptively a fault signal into a series of intrinsic mode functions (IMFs). Some IMFs containing richest fault information were selected to reconstruct a signal based on the kurtosis criterion. Then SVD was used to de-noise the reconstructed signal again and improve its signal-noise ratio. Finally,FSWT was done for the reconstructed signal to highlight fault features’time-frequency distribution information and extract fault features. The analysis results of simulated signals and test data showed that this method can effectively eliminate noise effects and clearly extract the fault signal’s characteristic frequency to realize the accurate recognition of rolling bearings’faults.
关键词
滚动轴承 /
变模态分解 /
奇异值分解 /
频率切片小波变换 /
故障特征提取
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Key words
rolling bearing /
VMD /
SVD /
FSWT /
fault feature extraction
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