This paper presents a signal decomposition and feature extraction technique for the health diagnosis of rotary machines, based on the empirical mode decomposition. Vibration signal measured from a defective rolling bearing is decomposed into a number of intrinsic mode functions (IMFs), with each IMF corresponding to a specific range of frequency components contained within the vibration signal. Two criteria, the energy measure and correlation measure, are investigated to determine the most representative IMF for extracting defect-induced characteristic features out of vibration signals. The envelope spectrum of the selected IMF is investigated as an indicator for both the existence and the specific location of structural defects within the bearing. Theoretical foundation of the technique is introduced, and its performance is experimentally verified.
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e-mail: ryan@ecs.umass.edu
e-mail: gao@ecs.umass.edu
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April 2008
Research Papers
Rotary Machine Health Diagnosis Based on Empirical Mode Decomposition
Ruqiang Yan,
Ruqiang Yan
Member ASME
Department of Mechanical and Industrial Engineering,
e-mail: ryan@ecs.umass.edu
University of Massachusetts
, Amherst, MA 01003
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Robert X. Gao
Robert X. Gao
Fellow ASME
Department of Mechanical and Industrial Engineering,
e-mail: gao@ecs.umass.edu
University of Massachusetts
, Amherst, MA 01003
Search for other works by this author on:
Ruqiang Yan
Member ASME
Department of Mechanical and Industrial Engineering,
University of Massachusetts
, Amherst, MA 01003e-mail: ryan@ecs.umass.edu
Robert X. Gao
Fellow ASME
Department of Mechanical and Industrial Engineering,
University of Massachusetts
, Amherst, MA 01003e-mail: gao@ecs.umass.edu
J. Vib. Acoust. Apr 2008, 130(2): 021007 (12 pages)
Published Online: February 4, 2008
Article history
Received:
December 14, 2006
Published:
February 4, 2008
Citation
Yan, R., and Gao, R. X. (February 4, 2008). "Rotary Machine Health Diagnosis Based on Empirical Mode Decomposition." ASME. J. Vib. Acoust. April 2008; 130(2): 021007. https://doi.org/10.1115/1.2827360
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