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Author:

Gao, Li-Xin (Gao, Li-Xin.) | Wu, Li-Juan (Wu, Li-Juan.) | Zhang, Jian-Yu (Zhang, Jian-Yu.)

Indexed by:

EI Scopus PKU CSCD

Abstract:

The gear partial fault's typical characteristic is the modulated sideband. The traditional demodulation technology can distinguish the gears' later period fault characteristic, but it is actually helpless to the incipient fault. In order to withdraw the fault information of the incipient data, and carry on the foreknowledge effectively to the fault diagnosis of the large-scale electromechanical device, this paper centered on a ruptured tooth fault which occured in the near past in a grum gear of a high speed rolling mill, adopted a method based on Empirical mode decomposition technology and combined with the Hilbert envelope demodulation analysis, and it obtained the result which withdrew the early crack breakdown information clearly. The results of the analysis on mechanical early vibration signal of the fault showed that the method based on EMD decomposition technology and envelope demodulation analysis can effectively extract the vibration signal characteristics of the mechanical fault, which is beneficial to finding the hidden dangers ahead.

Keyword:

Rolling mills Demodulation Vibration analysis Failure (mechanical) Failure analysis Fault detection Optical variables measurement Mathematical transformations Electromechanical devices

Author Community:

  • [ 1 ] [Gao, Li-Xin]Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing 100124, China
  • [ 2 ] [Wu, Li-Juan]Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing 100124, China
  • [ 3 ] [Zhang, Jian-Yu]Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing 100124, China

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Source :

Journal of Beijing University of Technology

ISSN: 0254-0037

Year: 2009

Issue: 7

Volume: 35

Page: 876-881

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 10

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