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

DOI

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.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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 (ICCSEE 2013)
PB  - Atlantis Press
SP  - 185
EP  - 188
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccsee.2013.49
DO  - 10.2991/iccsee.2013.49
ID  - Chen2013/03
ER  -