The bearing fault diagnosis of rotating machinery by using Hilbert-Huang transform

Author

Wu, Tian-Yau ; Wang, Chun-Chieh ; Chung, Yu-Liang

Author_Institution

Dept. of Mech. Eng., Nat. Central Univ., Taoyuan, Taiwan

fYear

2011

fDate

15-17 April 2011

Firstpage

6238

Lastpage

6241

Abstract

Based on improve the drawbacks of Ensemble Empirical Mode Decomposition (EEMD), such as mode mixing and end effect problem, post-processing of EEMD which was improved with HHT approach to solve the problem in this paper. Once the Intrinsic Mode Functions (IMFs) are obtained from the decomposition process, the crucial step is to extract the fault features from the information-contained IMFs. The amplitude modulation (AM) phenomenon can be discovered in the IMFs with fault information. In this paper, we not only classify the types of bearing fault but also identify the level of the fault.

Keywords

Hilbert transforms; amplitude modulation; fault diagnosis; feature extraction; machine bearings; Hilbert-Huang transform; amplitude modulation phenomenon; bearing fault diagnosis; end effect problem; ensemble empirical mode decomposition; fault feature extract; intrinsic mode functions; mode mixing; rotating machinery; Fault diagnosis; Feature extraction; Rolling bearings; Shafts; Vibrations; White noise; Amplitude modulation; Bearing fault; Hilbert-Huang Transform;

fLanguage

English

Publisher

ieee

Conference_Titel

Electric Information and Control Engineering (ICEICE), 2011 International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-8036-4

Type

conf

DOI

10.1109/ICEICE.2011.5777908

Filename

5777908