Fault Diagnosis of Rolling Bearing by Improved Hilbert-Huang Transform

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Fault Diagnosis of Rolling Bearing by Improved Hilbert-Huang Transform

Journal Title: 河南科技大学学报（自然科学版） - Year 2018, Vol 39, Issue 1

Abstract

An improved Hilbert-Huang transform (HHT) time-frequency analysis method was proposed based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) .A set of intrinsic mode function (IMFs) was gained by decomposing vibration signal of rolling bearing with CEEMDAN.The sensitive IMFs were screened out with the algorithm of automatic extraction of sensitive IMF.The Hilbert envelope spectrum and the HHT 2 D time-frequency spectrum of the sensitive IMFs were computed to extract the fault characteristic frequency information.The research results show that the mode aliasing can be significantly reduced by CEEMDAN algorithm, and CEEMDAN is superior to empirical mode decomposition and ensemble empirical mode decomposition.In combination with improved HHT and automatic extraction of sensitive IMF, the characteristic information of signal can be decomposed effectively.The sensitive IMFs containing fault characteristic information are screened out, and the interference of background noise and faultless IMFs are eliminated.The fault frequencies of rolling bearing are extracted effectively and the locations of fault can be diagnosed out.

Authors and Affiliations

Feng KANG, Wenchao LI, Haisong ZHAO, Ruping YANGA, Xiaokai YU

Keywords

CEEMDAN; algorithm of automatic extraction of sensitive IMF; rolling bearing; fault diagnosis;

EP ID EP464630
DOI -
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