Artificial neural network for bearing defect detection based on acoustic emission

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