Neural network design and the complexity of learning
Neural network design and the complexity of learning
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Methodologies of Using Neural Network and Fuzzy Logic Technologies for Motor Incipient Fault Detecti
Methodologies of Using Neural Network and Fuzzy Logic Technologies for Motor Incipient Fault Detecti
Neural Networks and Natural Intelligence
Neural Networks and Natural Intelligence
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
Special issue on integrated and hybrid intelligent systems in product design and development
International Journal of Knowledge-based and Intelligent Engineering Systems - Integrated and hybrid intelligent systems in product design and development
On-line monitoring of boring tools for control of boring operations
Robotics and Computer-Integrated Manufacturing
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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 × 20 × 1 CPN should be used to distinguish normal bearings from defective bearings. Furthermore, a 6 × 24 × 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.