Fault diagnosis method of rolling bearings based on LMD and singular value difference spectrum - Details

Author：

Ma, C.-Y. (Ma, C.-Y..) | Liu, X. (Liu, X..) (Scholars：刘晓) | Duan, J.-M. (Duan, J.-M..) (Scholars：段建民)

Indexed by：

Scopus PKU CSCD

Abstract：

Aiming　at　the　nonlinear　and　non-stationary　vibration　signal　of　the　rolling　bearing,　a　method　based　on　local　mean　decomposition　(LMD)　and　singular　value　difference　spectrum　is　proposed.　First,　the　original　vibration　signal　was　decomposed　into　several　product　functions　(PFs)　by　LMD,　Hankel　matrix　is　constructed　by　the　product　function　that　contains　the　fault　information,　and　the　singular　value　difference　spectrum　can　be　obtained　after　singular　value　decomposition.　Then,　the　maximum　catastrophe　point　is　used　to　identify　the　number　of　singular　value　reconstruction　components;　therefore,　the　original　component　is　reconstructed　and　the　noise　is　restrained.　Finally,　the　reconstructed　signal　is　demodulated　by　Hilbert　transformation　to　extract　the　fault　feature.　Results　of　experiment　and　engineering　signals　analysis　show　that　the　method　combined　LMD　and　singular　value　difference　spectrum　can　accurately　extract　the　fault　feature　of　rolling　bearing　for　diagnosis.

Keyword：

Fault diagnosis; Local mean decomposition (LMD); Singular value difference spectrum

Author Community：

[ 1 ] [Ma, C.-Y.]Key Laboratory of Advanced Manufacturing Technology, College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing 100124, China
[ 2 ] [Ma, C.-Y.]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China
[ 3 ] [Liu, X.]Key Laboratory of Advanced Manufacturing Technology, College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing 100124, China
[ 4 ] [Duan, J.-M.]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China

Reprint Author's Address：

[Ma, C.-Y.]Key Laboratory of Advanced Manufacturing Technology, College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing 100124, China

Email：

machaoyong@bjut.edu.cn

Show more details

Related Keywords：

Source ：

Journal of Beijing University of Technology

ISSN： 0254-0037

Year： 2014

Issue： 2

Volume： 40

Page： 182-188

Cited Count：