自适应UPEMD-MCKD轴承故障特征提取方法

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振动与冲击 ›› 2023, Vol. 42 ›› Issue (3) : 83-91.

论文

宋宇博1，刘运航1，朱大鹏2

作者信息 +

Adaptive UPEMD-MCKD rolling bearing fault feature extraction method

SONG Yubo1, LIU Yunhang1, ZHU Dapeng2

Author information +

文章历史 +

摘要

为了准确提取强噪声背景下较微弱的轴承故障特征信息，结合均相经验模态分解（Uniform Phase Empirical Mode Decomposition, UPEMD）和最大相关峭度解卷积方法(Maximum Correlated Kurtosis Deconvolution, MCKD)的优势，提出了一种自适应UPEMD-MCKD轴承故障特征提取方法。该方法将样本熵和峭度指标相结合构建最小熵峭比，采用遗传算法对最小熵峭比的最小值进行搜索，以确定移位数、滤波器长度和周期的最佳参数组合。经均相模态分解方法预处理的含噪信号通过相关性计算选取有效分量进行信号重构，重构信号借助最佳参数组合下的MCKD算法提取故障特征。内圈故障和外圈故障的实例分析表明，所提方法借助UPEMD的噪声抑制能力和最小熵峭比的参数组合寻优评价能力，能够从故障信号中有效的提取出微弱的故障特征。

Abstract

at the difficulty of extracting weak fault signals of bearings under strong noise background, combined with the advantages of Uniform Phase Empirical Mode Decomposition and Maximum Correlated Kurtosis Deconvolution method, an adaptive UPEMD-MCKD bearing fault feature extraction method was proposed.The minimum entropy kurtosis ratio was constructed by combining sample entropy and kurtosis index, and the minimum entropy kurtosis ratio was searched by genetic algorithm to obtain the optimal parameter combination of shift number, filter length and period.The noise signals preprocessed by Uniform Phase Empirical Mode Decomposition method were screened out by correlation calculation for signal reconstruction, and fault features were extracted from reconstructed signals by MCKD algorithm under optimal parameter combination.The analysis of inner and outer ring faults shows that the proposed method can effectively extract weak fault features from fault signals by virtue of UPEMD's noise suppression ability and minimum entropy ratio parameter combination optimization evaluation ability.

导出引用

宋宇博1，刘运航1，朱大鹏2. 自适应UPEMD-MCKD轴承故障特征提取方法[J]. 振动与冲击, 2023, 42(3): 83-91

SONG Yubo1, LIU Yunhang1, ZHU Dapeng2. Adaptive UPEMD-MCKD rolling bearing fault feature extraction method[J]. Journal of Vibration and Shock, 2023, 42(3): 83-91

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