Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering

Bearing Fault Diagnosis of Sorting Machine Induction Based on Improved Neural Network and Evidence Theory

Authors
Wei Chen, Qing-xuan Jia, Han-xu Sun, Si-cheng Nian
Corresponding Author
Wei Chen
Available Online March 2013.
DOI
https://doi.org/10.2991/iccsee.2013.49How to use a DOI?
Keywords
fault diagnosis, roller bearing, neural network, evidence theory
Abstract
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.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering
Part of series
Advances in Intelligent Systems Research
Publication Date
March 2013
ISBN
978-90-78677-61-1
ISSN
1951-6851
DOI
https://doi.org/10.2991/iccsee.2013.49How to use a DOI?
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
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccsee.2013.49
DO  - https://doi.org/10.2991/iccsee.2013.49
ID  - Chen2013/03
ER  -