Paper Title:
Recognition on Rolling Bearing’s Condition of Grinding Machine by Using a Novel Approach-Wavelet Hilbert Marginal Spectrum
  Abstract

Grinding machine condition monitoring is very important during the manufacturing process. Vibration analysis is usually used to its pattern recognition. But traditional signal analysis method limits the accuracy of recognition because of non-stationary and nonlinear characteristics. In this paper, a novel approach is presented in detail for grinding machine fault diagnosis. The method is based on the new developed Hilbert Marginal Spectrum and wavelet transform, named as wavelet-Hilbert marginal spectrum (WHMS). A rolling bearing’s pattern recognition of grinding machine is used to testify the effectiveness of this method, which can accurately detect flaw of the rolling bearing in early stage. Thus, it can be concluded that this promising method will contribute the development of grinding machine condition monitoring.

  Info
Periodical
Edited by
Dongming Guo, Tsunemoto Kuriyagawa, Jun Wang and Jun’ichi Tamaki
Pages
773-778
DOI
10.4028/www.scientific.net/KEM.329.773
Citation
H.K. Li, X.J. Ma, Q.M. Ren, J.L. Zhao, "Recognition on Rolling Bearing’s Condition of Grinding Machine by Using a Novel Approach-Wavelet Hilbert Marginal Spectrum", Key Engineering Materials, Vol. 329, pp. 773-778, 2007
Online since
January 2007
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