Rolling bearing defect severity assessment under varying operating conditions
by Changting Wang, Robert X. Gao, Ruqiang Yan, Arnaz Malhi
International Journal of Manufacturing Research (IJMR), Vol. 4, No. 1, 2009

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]

Online publication date: Sun, 25-Jan-2009

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