基于字典学习的轴承早期故障稀疏特征提取

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振动与冲击 ›› 2016, Vol. 35 ›› Issue (6) : 181-186.

论文

基于字典学习的轴承早期故障稀疏特征提取

余发军1,2 ，周凤星1，严保康1

作者信息 +

Bearing initial fault feature extraction via sparse representation based on dictionary learning

YU Fa-jun1,2，ZHOU Feng-xing1，YAN Bao-kang1

Author information +

文章历史 +

摘要

针对低速重载机械滚动轴承早期故障的振动信号中故障特征冲击成分微弱易被噪声覆盖难以识别，而利用稀疏表示方法提取冲击成分时因轴承工况非平稳性，准确匹配冲击成分字典难以构造问题，提出基于字典学习的轴承早期故障稀疏特征提取方法。利用改进型K-SVD字典学习算法构造自适应字典；采用正交匹配追踪算法(Orthogonal Matching Pursuit,OMP)对振动信号进行稀疏分解，计算每次迭代逼近信号的峭度值，找出最大峭度值对应的逼近信号；重构特征成分并进行包络谱分析，获得故障类型。仿真及轴承振动数据测试结果表明，所提方法能更好匹配早期故障特征成分、满足轴承实时故障监测需求。

Abstract

As initial fault occurs in rolling bearing of low-speed and heavy-duty machinery, the impulse component, reflecting the fault feature in vibration signal, is difficult to extract for it is relatively weak and easily corrupted by strong background noise. The authors attempt to extract the impulse component from vibration signal with sparse representation method. However, it is difficult to construct the accurate dictionary which matches the impulse component since operating conditions of bearing is not stable. Hence, a method of extracting the initial fault feature, which is based on dictionary learning, is proposed here. Firstly, an adaptive dictionary is obtained by the developed K-SVD dictionary learning algorithm. Then, Orthogonal Matching Pursuit (OMP) algorithm is utilized for sparse decomposition of the vibration signal, and all kurtosis values of approximation signal of iterations are calculated .Finally, the corresponding approximation signal of maximal kurtosis value will be reconstructed and analyzed with envelope spectrum to diagnose the fault type. The test results of simulate data and bearing vibration signal demonstrate that the proposed method, which can extract the feature component more accurately than other methods, meets the demand of real-time bearing condition monitor.

导出引用

余发军1,2,周凤星1，严保康1. 基于字典学习的轴承早期故障稀疏特征提取[J]. 振动与冲击, 2016, 35(6): 181-186

YU Fa-jun1,2，ZHOU Feng-xing1，YAN Bao-kang1. Bearing initial fault feature extraction via sparse representation based on dictionary learning[J]. Journal of Vibration and Shock, 2016, 35(6): 181-186

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