基于CSES和MED的滚动轴承微弱故障特征提取
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TH165+.3;TH133.3

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国家重点研发计划资助项目(2017YFC0804400,2017YFC0804407)


Weak Fault Extraction of Rolling Element Bearings Based on CSES and MED
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    摘要:

    针对高噪声条件下,联合平方包络谱(combined squared envelope spectrum, 简称CSES)方法容易受频带内噪声和其他频带特征的干扰,导致对滚动轴承微弱故障特征提取困难的问题,提出了一种结合CSES和最小熵解卷积(minimum entropy deconvolution, 简称MED)的滚动轴承微弱故障特征提取方法。首先,使用谱峭度选择不同频带-滤波后的信号;其次,对所选信号进行MED滤波,增强频带内的故障特征;最后,依据CSES原理,计算上一步滤波后信号的平方包络频谱并进行归一化,将其合并得到故障特征明显的强化包络谱。仿真与试验结果表明,该方法能够有效提取滚动轴承的微弱故障特征。

    Abstract:

    Combined squared envelope spectrum(CSES) is susceptible to the interference from in-band noise and other band characteristics in high back-ground noise, which leads to the difficulty in extracting the weak fault features of rolling element bearings. A new method based on CSES and minimum entropy deconvolution (MED) is proposed to overcome this problem. Firstly, the proposed method uses spectral kurtosis to select the filtered signals from different frequency bands. Secondly, these selected signals are further filtered using MED for enhancing the fault feature in the frequency band. According to the principle of CSES, the squared envelope spectrum is computed for the signals filtered in previous step. These spectrums are normalized within 0 and 1, then they are combined to obtain an enhanced envelope spectrum with obvious fault characteristics. The simulation and experimental results show that the proposed method can effectively extract the weak fault features of rolling element bearings.

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  • 在线发布日期: 2021-08-25
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