中国机械工程2019，Vol.30Issue(4)：406-412,7.DOI:10.3969/j.issn.1004-132X.2019.04.005
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
Bearing Fault Diagnosis Based on K-SVD and HBW-OOMP
摘要
Abstract
A new method for fault diagnosis of bearings was presented based on K-SVD and HBWOOMP.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.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Key words
sparse representation/K singular value decomposition (K-SVD)/hierarchized block wise optimized orthogonal matching pursuit (HBW-OOMP)/block process/envelop spectrum kurtosis分类
交通工程引用本文复制引用
张文颢,李永健,张卫华..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,2019,30(4):406-412,7.基金项目
国家重点研发计划资助项目(2016YFB1200401) （2016YFB1200401）
国家科技支撑计划资助项目(2015BAG19B02) （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) （szjj2018-132）
江门市基础与理论科学研究类科技计划资助项目(2018JC01005) （2018JC01005）
