Bearing Fault Diagnosis Based on K-SVD and HBW-OOMP

China Mechanical Engineering

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Bearing Fault Diagnosis Based on K-SVD and HBW-OOMP

ZHANG Wenhao1;LI Yongjian2，3;ZHANG Weihua1

1.State Key Laboratory of Traction Power，Southwest Jiaotong University，Chengdu，610031
2.School of Rail Transportation, Wuyi University, Jiangmen, Guangdong, 529020
3.Sichuan Provincial Key Laboratory of Automotive Measurement, Control and Safety, Xihua University, Chengdu, 610039

Online:2019-02-25 Published:2019-02-26

基于K-奇异值分解和层次化分块正交匹配算法的滚动轴承故障诊断

张文颢1;李永健2,3;张卫华1

1.西南交通大学牵引动力国家重点实验室，成都，610031
2.五邑大学轨道交通学院，江门，529020
3.西华大学汽车测控与安全四川省重点实验室，成都，610039

基金资助:
国家重点研发计划资助项目（2016YFB1200401）；
国家科技支撑计划资助项目（ 2015BAG19B02）;
汽车测控与安全四川省重点实验室开放课题资助项目（szjj2018-132）;
江门市基础与理论科学研究类科技计划资助项目（2018JC01005）

Abstract

Abstract: A new method for fault diagnosis of bearings was presented based on K-SVD and HBW-OOMP. Firstly,K-SVD dictionary training algorithm was utilized to construct a redundant dictionary containing impulsive components and the disadvantages of less adaptability of fixed structure dictionary were overcome. Then, HBW-OOMP algorithm was employed in selecting the best atom and solving the sparse coefficients. The signals were decomposed adaptively with the maximum principle of the envelop spectrum kurtosis. The fault features were extracted by the proposed method from simulated and experimental signals respectively. The results show that the method may achieve the extraction of impulsive components from strong noise, which demonstrates the effectiveness and practicability.

Key words: sparse representation, K singular value decomposition(K-SVD), hierarchized block wise optimized orthogonal matching pursuit (HBW-OOMP), block process, envelop spectrum kurtosis

摘要： 利用层次化分块正交匹配算法（HBW-OOMP）的高稀疏性和运算速度快等优点，提出了一种基于K-奇异值分解（K-SVD）字典和HBW-OOMP算法的故障轴承诊断方法。首先利用K-SVD自学习训练方法得到包含冲击成分的冗余字典，克服了固定结构字典适应性不强的缺点。然后采用基于分块思想的HBW-OOMP算法进行原子的选取和稀疏系数的求解，以重构信号包络谱峭度最大为终止条件，自适应确定分解次数。最后应用所提方法对仿真信号和故障轴承实验信号进行故障特征提取，结果表明该方法能够有效提取强背景噪声下故障特征成分，具有一定的应用前景。

关键词: 稀疏表示, K-奇异值分解, 层次化分块正交匹配, 块处理, 包络谱峭度

CLC Number:

U270.7

ZHANG Wenhao1;LI Yongjian2，3;ZHANG Weihua1. Bearing Fault Diagnosis Based on K-SVD and HBW-OOMP[J]. China Mechanical Engineering.

张文颢1;李永健2,3;张卫华1. 基于K-奇异值分解和层次化分块正交匹配算法的滚动轴承故障诊断[J]. 中国机械工程.

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Bearing Fault Diagnosis Based on K-SVD and HBW-OOMP

基于K-奇异值分解和层次化分块正交匹配算法的滚动轴承故障诊断

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