Title: Rolling bearing defect severity assessment under varying operating conditions

Authors: Changting Wang, Robert X. Gao, Ruqiang Yan, Arnaz Malhi

Addresses: Nondestructive Technologies Laboratory, GE Global Research Center, 1 Research Circle, Niskayuna, NY 12309, USA. ' Department of Mechanical Engineering, University of Connecticut, Storrs, CT 06269, USA. ' Department of Mechanical Engineering, University of Connecticut, Storrs, CT 06269, USA. ' Department of Mechanical and Industrial Engineering, University of Massachusetts, Amherst, MA 01003, USA

Abstract: This paper investigates a multilayer feed-forward neural network structure for automated bearing defect severity assessment under varying operating conditions. A bearing health index was proposed, based on the Weibull theory. Defect-related frequency features were used as inputs to the neural network. The neural network was trained to establish relationship between defect characteristic and bearing conditions, and outputs specific a health index value corresponding the defect severity. Experiments have shown that the neural network was effective in differentiating faulty bearings from a |healthy| bearing, with a 99% and 97% success rate for classifying defects in the inner and outer raceways, respectively. [Received 21 April 2008; Revised 4 August 2008; Accepted 15 August 2008]

Keywords: neural networks; feature extraction; bearing health index; wavelet transform; rolling bearings; bearing defects; defect assessment; defect severity.

DOI: 10.1504/IJMR.2009.022742

International Journal of Manufacturing Research, 2009 Vol.4 No.1, pp.37 - 56

Published online: 25 Jan 2009 *

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