|Table of Contents|

Fault feature extraction of rolling bearing based on ALIFand energy operator demodulation(PDF)

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2018年04期
Page:
445-
Research Field:
Publishing date:

Info

Title:
Fault feature extraction of rolling bearing based on ALIFand energy operator demodulation
Author(s):
Chen Baojia12Wang Xinbo2Zhao Chunhua12Chen Fafa12Qiu Guangyin2Tian Hongliang12
1.Hubei Key Laboratory of Hydroelectric Machinery Design and Maintenance; 2.College of Mechanical and Power Engineering,China Three Gorges University,Yichang 443002,China
Keywords:
adaptive local iterative filtering intrinsic mode function rolling bearing energy operator feature extraction empirical mode decomposition envelop spectrum
PACS:
TH17
DOI:
10.14177/j.cnki.32-1397n.2018.42.04.009
Abstract:
In order to improve the reliability of the fault feature extraction of rolling bearings,a method of fault feature extraction of rolling bearing based on adaptive local iterative filtering(ALIF)and energy operator demodulation is proposed. In this method,the fault vibration signal of bearing is first decomposed into several intrinsic mode function(IMF)components using ALIF,and the energy operator is demodulated for the component which contains the most fault information,and the envelope spectrum of the component is obtained to extract the fault characteristics of the bearing. Simulation results show that the ALIF can accurately obtain the IMF components and solve the problem of pattern aliasing caused by the empirical mode decomposition(EMD). Combining with the energy operator demodulation method,the characteristics of the envelope spectrum of the fault signal can be better highlighted and effectively extract bearing fault characteristic frequency.

References:

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Last Update: 2018-08-30