快速Hoyer谱图及VNCMD的变转频滚动轴承故障诊断
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TH17;TH133.3

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国家自然科学基金资助项目(51775409)


Rolling Element Bearing Defect Diagnosis Under Variable Frequency Based on Fast Hoyergram and Improved VNCMD
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    摘要:

    针对变转频情况下滚动轴承振动信号出现频谱混叠现象而无法直接提取故障特征频率的问题,提出一种基于快速Hoyer谱图及改进变分非线性调频模态分解(variational nonlinear chirp mode decomposition,简称VNCMD)的变转频轴承故障诊断方法。首先,采用快速Hoyer谱图确定轴承故障冲击所处的共振频带,对信号进行带通滤波提取轴承故障冲击成分并与低通滤波后的信号进行融合;其次,通过多分量协同转频估计方法对转频及轴承故障特征频率脊线进行估计;最后,将估计的脊线作为VNCMD的输入参数,提取转频及轴承故障冲击成分,并通过阶次分析确定轴承的故障类型。相较于集合经验模态分解(ensemble empirical mode decomposition,简称EEMD),所提方法可以获得更加精确的时频脊线,并通过信号分解得到正确的分量。仿真信号和实验信号均表明所提方法的有效性。

    Abstract:

    In the light of the difficulty of rolling element bearing defect diagnosis under variable frequency while using traditional spectrum analysis, a new method based on fast Hoyergram and improved variational nonlinear chirp mode decomposition (VNCMD) is proposed. First, fast Hoyergram is used to determine the resonance frequency band where the bearing fault impact locates. Then bandpass filtering is utilized to extract the component of rolling bearing vibration signal and the result is mixed with the signal after lowpass filtering. Secondly, the ridges of rotating frequency and bearing fault frequency are extracted based on multi-component collaborative speed estimation, and used as the input parameter of VNCMD to extract the rotation and bearing fault impact components. Finally, the type of rolling bearing fault can be determined by characteristic frequency ratio. Compared with ensemble empirical mode decomposition (EEMD), the proposed method can extract more accurate time-frequency ridges and obtain correct components through signal decomposition. Both the simulation and experimental results demonstrate the effectiveness of the proposed method.

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  • 在线发布日期: 2022-12-28
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