Artificial neural network for bearing defect detection based on acoustic emission
Document Type
Article
Publication Date
1-1-2010
Abstract
Neural networks have been widely used for many applications. One of the applications is forecasting. Many studies have proven that neural networks can provide good accuracy on forecasting future data with over than 80% accuracy. In this study, neural network is used to predict bearing defects. Two learning tasks, function approximation and pattern recognition, were used for detection and monitoring of defects in ball bearing. Given five categories of bearing defect, the neural networks have successfully proven the ability to distinguish one defect over the other with high accuracy. Acoustic emission (AE) was used as a measurement in this study. AE is defined as transient waves generated from a rapid release of strain energy by deformation or damage or on the surface of a material (1-3). The AE waves can provide information about bearing condition. Maximum amplitude and AE counts were used as the basis for detection.
Keywords
Predict, Ball bearing defects, Neural network
Publication Title
The International Journal of Advanced Manufacturing Technology
Volume
50
Issue
1-4
Publisher
Springer Verlag (Germany)
Publisher Location
236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND