针对环境噪声下滚动轴承早期故障信号微弱难以检测的问题，提出了基于网格搜索法优化最大相关峭度反卷积（Maximum correlated Kurtosis deconvolution, MCKD）滚动轴承早期故障诊断方法。并针对MCKD方法受滤波器阶数和周期影响的问题，提出了利用网格搜索法优化最大相关峭度反卷积参数。首先，早期微弱故障信号集成经验模态分解后，采用相关系数以及峭度准则重构原信号；然后，以Shannon熵作为目标函数采用网格搜索法搜寻最优滤波器阶数以及周期，采用自适应MCKD方法对重构信号中故障脉冲冲击成分进行加强，最后通过包络谱、包络功率谱提取微弱故障特征。实验表明，该方法能够对早期微弱故障中冲击成分进行自适应增强，有效检测出被噪声淹没的微弱故障，实现滚动轴承故障的精确诊断。
Abstract
A rolling bearing early fault diagnosis method based on Maximum Correlated Kurtosis Deconvolution (MCKD) Optimized by Grid Search Algorithm was proposed, aiming at the environmental noise of rolling bearing early fault signals of weak difficult test problems. The MCKD method Optimized by Grid Search Algorithm was proposed, aiming at the problem that the effect of the MCKD method was subject to order of the filter and the period. Firstly, ensemble empirical mode decompose was used to decompose the early week fault signal, and the signal reconstruction was selected by the correlation coefficient and the kurtosis criterion. Then, Grid Search which select Shannon entropy as its objective function was used to search for the influencing parameters of MCKD algorithm: the order of the filter and the period. And then the impulses component in the signal reconstruction was enhanced by the adaptive MCKD. At last, the week fault characteristics was extracted by the spectrum envelope and the Hilbert power spectrum. The result shows that this method can not only enhance the impulses component in the early fault signal, but also efficiently extract the week fault information and realize the precise fault diagnosis of rolling bearing.
关键词
最大相关峭度反卷积 /
网格搜索法 /
早期故障诊断 /
滚动轴承
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Key words
MCKD /
grid search /
early fault diagnosis /
rolling bearing
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参考文献
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