Rolling bearing defect severity assessment under varying operating conditions. (27th January 2009)

• Record Type: Journal Article
• Authors: Wang, Changting; Gao, Robert X.; Yan, Ruqiang; Malhi, Arnaz
• Is Part Of: International journal of manufacturing research. Volume 4:Number 1(2009)
• Page Start: 37
• Page End: 56
• Publication Date: 2009-01-27
• Subjects: neural networks -- feature extraction -- bearing health index -- wavelet transform -- rolling bearings -- bearing defects -- defect assessment -- defect severity; Manufacturing processes -- Periodicals; Manufacturing processes -- Automation -- Periodicals; Production engineering -- Periodicals; Factory management -- Periodicals; 670.5
• Languages: English
• ISSNs: 1750-0591

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]