摘要
针对强噪声情况滚动轴承故障特征较微弱、其故障特征较难提取问题,提出将最小熵反褶积(Minimum Entropy Deconvolution, MED)与快速谱峭度算法(Fast Spectral Kurtosis, FSK)结合用于滚动轴承微弱故障提取。用MED对强噪声滚动轴承振动信号降噪,对降噪后信号进行快速谱峭度计算,确定故障信号共振解调带通滤波器参数,结合能量算子解调包络谱提取故障特征。通过仿真与实验数据验证该方法的有效性。
Abstract
The fault feature extraction of rolling bearing has a big problem that the rolling bearing’s fault feature under strong background noise is very weak.The spectral kurtosis has been used in fault feature extraction of rolling bearing, but it’s performance is poor under strong background noise.The minimum entropy deconvolution (MED) and fast spectral kurtosis (FSK) were combined for weak fault feature extraction of rolling bearing.Firstly MED was used for rolling bearing vibration signals under strong background noise, then parameters of demodulated resonance band-pass filter were chosen by FSK of decreased signals, finally fault feature was extracted successfully through energy operator demodulation envelop spectrum. In the end the effectiveness of the proposed method was verified through simulation signal and experiment data.
关键词
最小熵反褶积 /
快速谱峭度算法 /
滚动轴承 /
共振解调 /
特征提取
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Key words
FSK /
rolling bearing /
demodulated resonance /
feature extraction
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刘志川;唐力伟;曹立军.
基于MED及FSK的滚动轴承微弱故障特征提取[J]. 振动与冲击, 2014, 33(14): 137-142
LIU Zhi-chuan;TANG Li-wei;CAO Li-jun.
Feature extraction method for rolling bearing’s weak fault based on MED and FSK[J]. Journal of Vibration and Shock, 2014, 33(14): 137-142
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脚注
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