基于自相关降噪和ELMD的轴承故障诊断方法
发布时间：2018-12-10 14:27
【摘要】：为了提取在故障轴承振动信号中被强噪声淹没的微弱冲击特征信号,提出一种基于总体局部均值分解和自相关降噪的轴承故障诊断方法。首先,应用自相关函数对轴承故障信号进行降噪;其次,对降噪后的信号进行ELMD分解,并得到一系列的乘积分量;最后,利用共振解调技术对各个PF分量进行包络分析,进而发现轴承故障频率。试验结果表明:将自相关降噪和ELMD分解方法结合用于实测轴承故障特征提取中,不仅可以降低信噪比,而且可以有效地提取轴承故障的特征频率。
[Abstract]:In order to extract the weak impulse characteristic signal which is submerged by strong noise in the vibration signal of the bearing, a fault diagnosis method based on the decomposition of local mean and autocorrelation noise reduction is proposed. First, the bearing fault signal is de-noised by autocorrelation function, secondly, the signal after denoising is decomposed by ELMD, and a series of product components are obtained. Finally, the resonant demodulation technique is used to analyze the envelope of each PF component, and the bearing fault frequency is found. The experimental results show that the combination of autocorrelation noise reduction and ELMD decomposition can not only reduce the signal-to-noise ratio but also extract the characteristic frequency of bearing fault effectively.
【作者单位】： 内蒙古科技大学机械工程学院;
【基金】：国家自然科学基金资助项目(51565046) 内蒙古自然科学基金资助项目(2015MS0512) 内蒙古高等学校科学研究资助项目(NJZY146)
【分类号】：TH133.3
[Abstract]:In order to extract the weak impulse characteristic signal which is submerged by strong noise in the vibration signal of the bearing, a fault diagnosis method based on the decomposition of local mean and autocorrelation noise reduction is proposed. First, the bearing fault signal is de-noised by autocorrelation function, secondly, the signal after denoising is decomposed by ELMD, and a series of product components are obtained. Finally, the resonant demodulation technique is used to analyze the envelope of each PF component, and the bearing fault frequency is found. The experimental results show that the combination of autocorrelation noise reduction and ELMD decomposition can not only reduce the signal-to-noise ratio but also extract the characteristic frequency of bearing fault effectively.
【作者单位】： 内蒙古科技大学机械工程学院;
【基金】：国家自然科学基金资助项目(51565046) 内蒙古自然科学基金资助项目(2015MS0512) 内蒙古高等学校科学研究资助项目(NJZY146)
【分类号】：TH133.3
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