Monitoring and diagnosis of roller bearing conditions using neural networks and soft computing

Roller Bearings have extended use throughout the industry and their proper operation is paramount in insuring quality products. Therefore, an on-line monitoring and diagnostic system is needed to detect faulty bearings. In this work, by applying the feature selection technique to the data obtained from vibration signals, six indices were selected. Artificial neural networks and soft computing were used for nonlinear pattern recognition. An attempt was made to distinguish between normal and defective bearings. Furthermore, classification of roller bearing conditions into six different categories was conducted for the diagnostic purpose. Back propagation neural networks (BPN's), counterpropagation neural networks (CPN's), and adaptive neuro-fuzzy inference systems (ANFIS) were used for on-line monitoring and diagnosis of roller bearing conditions. All of them were able to recognize normal bearings from defective bearings with 100% success rate. In classifying the defect types, BPN obtained a success rate of 20% to 100%; CPN obtained a success rate of 31.7% to 100% while ANFIS achieved a success rate of 5% to 48%. CPN have the best performance among the three intelligent techniques. In order to monitor roller bearing conditions, a 1 x 20 x 1 CPN should be used to distinguish normal bearings from defective bearings. Furthermore, a 6 x 24 x 1 CPN can be used to diagnose the roller bearing conditions into six categories. In this manner, monitoring and diagnosis of roller bearings can be performed successfully.