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Wavelet-Based Multi-Fractal Spectrum for Machine Defect Identification

[+] Author Affiliations
Ruqiang Yan, Robert X. Gao

University of Massachusetts at Amherst, Amherst, MA

Paper No. IMECE2007-41984, pp. 673-679; 7 pages
  • ASME 2007 International Mechanical Engineering Congress and Exposition
  • Volume 10: Mechanics of Solids and Structures, Parts A and B
  • Seattle, Washington, USA, November 11–15, 2007
  • Conference Sponsors: ASME
  • ISBN: 0-7918-4304-1 | eISBN: 0-7918-3812-9
  • Copyright © 2007 by ASME


This paper presents a technique based on the wavelet-based multi-fractal singularity spectrum for rotary machine defect identification. Specifically, vibration signals measured by accelerometers are decomposed into a series of scales, with each scale corresponding to a sub-frequency band, by means of the continuous wavelet transform (CWT). The multi-fractal spectrum is then calculated from the wavelet coefficient modulus-maxima lines. Comparing to other signal processing techniques, the inherently flexible time-frequency resolution property of the wavelet transform characterizes the scaling properties of the multi-fractal spectrum, thus is more effective in singularity identification. Experimental studies on rolling bearings and a gearbox have shown that the presented technique provides an effective tool for defect identification.

Copyright © 2007 by ASME



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