针对滚动轴承微弱故障特征易受噪声干扰且退化起始点难以确定的问题,提出了一种基于自适应变分模态分解和包络谐噪比的滚动轴承早期退化检测方法。首先,采用灰狼优化算法自适应地选择变分模态分解的分解层数和二次惩罚因子,以最小平均包络熵为目标函数获得最佳参数组合。其次,通过引入有效加权稀疏峭度指数实现了有效模态分量和噪声模态分量的分离,使重构后的信号滤除了干扰而保留了故障信息。最后,计算了重构信号的包络谐噪比,利用其对周期性故障冲击的敏感性实现了滚动轴承早期退化起始点的检测。实验验证结果表明,该方法不仅解决了轴承运行初期的误报警问题,还能较早地识别出轴承退化过程的起始点,兼具鲁棒性和敏感性,为滚动轴承的早期故障诊断和剩余寿命预测提供参考依据。
Abstract
Here, aiming at problems of weak fault features of rolling bearing being easy to be disturbed by noise and its degradation starting point being difficult to determine, an early degradation detection method of rolling bearing based on adaptive variational mode decomposition (AVMD) and envelope harmonic-to-noise ratio(EHNR) was proposed. Firstly, the grey wolf optimization algorithm was used to adaptively select the number of decomposition layers and quadratic penalty factor of AVMD, and the minimum average envelope entropy was taken as the objective function to obtain the optimal parametric combination. Secondly, the effective weighted sparse kurtosis index was introduced to separate effective modal components and noise modal components, and the reconstructed signal could filter out interference and retain fault information. Finally, the envelope harmonic to noise ratio of the reconstructed signal was calculated, and its sensitivity to periodic fault impact was used to detect the early degradation starting point of rolling bearing. The experimental verification results showed that the proposed method can not only solve the problem of false alarm in the early stage of bearing operation, but also earlier identify the starting point of bearing degradation process; it has both robustness and sensitivity, and provides a reference for early fault diagnosis and residual life prediction of rolling bearing.
关键词
自适应变分模态分解(AVMD) /
包络谐噪比(EHNR) /
滚动轴承 /
早期退化
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Key words
adaptive variational mode decomposition (AVMD) /
envelope harmonic-to-noise ratio(EHNR) /
rolling bearing /
early degradation
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脚注
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