Fast and adaptive local mean decomposition method and its application in rolling bearing fault diagnosis - 文章详情页

作者：

Zhang, Kun (Zhang, Kun.) | Ma, Chao-Yong (Ma, Chao-Yong.) | Xu, Yong-Gang (Xu, Yong-Gang.) | Zhang, Jian-Yu (Zhang, Jian-Yu.) | Fu, Sheng (Fu, Sheng.)

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EI CSCD

摘要：

Local　Mean　Decomposition　(LMD)　has　been　widely　used　in　signal　processing　and　fault　diagnosis　of　mechanical　equipment,　but　there　are　still　some　issues　that　need　to　be　improved,　such　as　endpoint　effects,　mode　mixing,　and　so　on.　This　study　proposes　a　novel　method　called　Fast　and　Adaptive　Local　Mean　Decomposition(FALMD),　which　can　decompose　the　signal　into　some　product　function　components(PF)and　a　residual　component,　just　like　LMD.　This　method　changes　the　termination　criterion,　and　the　speed　of　operation　is　accelerated;　order　statistics　filters　are　used　to　weaken　the　endpoint　effect.　Using　this　method,　the　simulation　and　experimental　signal　can　be　decomposed　quickly,　effectively　and　accurately.　This　method　can　be　applied　to　the　fault　diagnosis　of　bearing.　©　2020,　Nanjing　Univ.　of　Aeronautics　an　Astronautics.　All　right　reserved.

关键词：

Failure analysis Fault detection Roller bearings Signal processing

作者机构：

[ 1 ] [Zhang, Kun]Key Laboratory of Advanced Manufacturing Technology, The College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing; 100124, China
[ 2 ] [Ma, Chao-Yong]Key Laboratory of Advanced Manufacturing Technology, The College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing; 100124, China
[ 3 ] [Xu, Yong-Gang]Key Laboratory of Advanced Manufacturing Technology, The College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing; 100124, China
[ 4 ] [Zhang, Jian-Yu]Key Laboratory of Advanced Manufacturing Technology, The College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing; 100124, China
[ 5 ] [Fu, Sheng]Key Laboratory of Advanced Manufacturing Technology, The College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing; 100124, China

通讯作者信息：

[ma, chao-yong]key laboratory of advanced manufacturing technology, the college of mechanical engineering and applied electronics technology, beijing university of technology, beijing; 100124, china

电子邮件地址：

machaoyong@bjut.edu.cn

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