Bearing Fault Diagnosis of Sorting Machine Induction Based on Improved Neural Network and Evidence Theory
- Wei Chen, Qing-xuan Jia, Han-xu Sun, Si-cheng Nian
- Corresponding Author
- Wei Chen
Available Online March 2013.
- https://doi.org/10.2991/iccsee.2013.49How to use a DOI?
- fault diagnosis, roller bearing, neural network, evidence theory
- Roller bearing is an important mechanical element of sorting machine induction. It usually has defects in outer race, inner race or balls due to continuous metal-metal contacts in high-speed operating conditions. This paper presents a novel diagnosis algorithm based on improved neural network and D-S evidence theory. Firstly, fault features are extracted through vibration signal analysis. Improved neural network classifier is then constructed to finish primary recognition, which introduces momentum to increase the learning rate. In order to reduce recognition uncertainty, each single classifier is regarded as independent evidence, and they are aggregated by improved Dempster’s combination rule. Experiment results show that proposed algorithm can improve diagnosis accuracy and decrease recognition uncertainty.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Wei Chen AU - Qing-xuan Jia AU - Han-xu Sun AU - Si-cheng Nian PY - 2013/03 DA - 2013/03 TI - Bearing Fault Diagnosis of Sorting Machine Induction Based on Improved Neural Network and Evidence Theory BT - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering PB - Atlantis Press UR - https://doi.org/10.2991/iccsee.2013.49 DO - https://doi.org/10.2991/iccsee.2013.49 ID - Chen2013/03 ER -