S. Sendhilkumar and N. Mohanasundaram and M. Senthilkumar and S. N. Sivanandam
Elman Neural Network for Diagnosis of Unbalance in a RotorBearing System
613 - 617
2016
10
3
International Journal of Mechanical and Mechatronics Engineering
https://publications.waset.org/pdf/10004647
https://publications.waset.org/vol/111
World Academy of Science, Engineering and Technology
The operational life of rotating machines has to be extended using a predictive condition maintenance tool. Among various condition monitoring techniques, vibration analysis is most widely used technique in industry. Signals are extracted for evaluating the condition of machine; further diagnostics is carried out with detected signals to extend the life of machine. With help of detected signals, further interpretations are done to predict the occurrence of defects. To study the problem of defects, a test rig with various possibilities of defects is constructed and experiments are performed considering the unbalanced condition. Further, this paper presents an approach for fault diagnosis of unbalance condition using Elman neural network and frequencydomain vibration analysis. Amplitudes with variation in acceleration are fed to Elman neural network to classify fault or nofault condition. The Elman network is trained, validated and tested with experimental readings. Results illustrate the effectiveness of Elman network in rotorbearing system.
Open Science Index 111, 2016