Open AccessEMD and Singular Value Difference Spectrum Based Bearing Fault Characteristics Extraction
Author(s) -
Hui Li,
Yang Qi
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1624/3/032004
Subject(s) - hilbert–huang transform , envelope (radar) , singular spectrum analysis , singular value decomposition , bearing (navigation) , vibration , noise (video) , signal (programming language) , singular value , fault (geology) , mode (computer interface) , transient (computer programming) , mathematics , principal component analysis , algorithm , white noise , acoustics , computer science , physics , artificial intelligence , statistics , telecommunications , geology , eigenvalues and eigenvectors , quantum mechanics , seismology , image (mathematics) , programming language , operating system , radar
Aiming at extracting rolling bearing defect characteristic effectively under strong background noise, a defect diagnosis technique based on empirical mode decomposition (EMD) and singular value difference spectrum is put forward. Firstly, the non-stationary original bearing vibration experimental data is decomposed into several intrinsic mode functions (IMFs) using EMD technique to eliminate the noise influence. Secondly, the effective intrinsic mode functions are selected by using correlation functions to reconstruct the bearing transient vibration signal. Thirdly, the singular value decomposition of the reconstructed signal is carried out to calculate the singular value difference spectrum (SVDS), and the principal components are selected according to the SVDS. Finally, the envelope spectrum of the reconstructed principal components data is calculated, and rolling bearing defect characteristic is picked up according to the envelope spectrum technique. The results of the experiment exhibit that the proposed technique can be effectively applied to rolling bearing defect characteristics extraction.