Rolling Bearing Fault Diagnosis Based on SVD and Improved EMD
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摘要: 针对经验模态分解(EMD)存在的模态混叠问题,提出一种奇异值分解(SVD)和改进经验模态分解相结合的信号分析新方法。该方法首先对故障信号进行SVD降噪,以消除随机干扰,再根据信号特征加入高频谐波信号并进行EMD进行分解,有效地减少模态混叠现象,最后对EMD分解得到的高频本征模态分量(IMF)进行代数运算得到故障冲击成分,经Hilbert包络分析,提取出故障特征信息。仿真信号分析了这种方法的实施过程,并将该方法成功运用于滚动轴承内圈和外圈故障的诊断中。实验结果证明:该方法能够有效地提取滚动轴承故障特征信息,实现故障诊断。Abstract: Considering the mode mixing in empirical mode decomposition,a novel method that combines singular value decomposition(SVD) with improved empirical mode decomposition(EMD) is proposed. The first step of this method is to reduce the random noise in fault signal by the SVD,and then the high frequency harmonic is added by the characteristic of original signal before EMD. The preprocessed signal is decomposed by EMD to restrain the mode mixing effectively. Finally,envelope demodulation is performed for the intrinsic mode function(IMF) of shock signal and as a result,the fault feature was extracted successfully. The implementation process was analyzed by simulated signal and this method has been successfully applied in inner race and outer race of rolling bearing fault diagnosis. The results show that this method can extract the fault information of rolling bearing effectively and realize the fault diagnosis.
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Key words:
- computer simulation /
- demodulation /
- efficiency /
- eigenvalues and eigenfunctions /
- failure analysis
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