Study of Fault Diagnosis for Rolling Bearing Based on GA-BP Algorithm

DOI

10.2991/amcce-17.2017.137How to use a DOI?

Keywords

Genetic Algorithm; BP Neural Network; Fault Diagnosis; Rolling Bearing

Abstract

With the development of modern technology, the diagnosis technology for mechanical equipment has attached more attentions. This paper mainly presents the fault diagnosing process for rolling bearing by using the hybrid GA-BP algorithm, which combines genetic algorithm (GA) with error back-propagation algorithm (BP). The weights and thresholds of BP neural network are optimized by using genetic algorithm firstly and then assigned to the BP neural network. With the using of proposed approach, the diagnosis performance of BP neural network can be improved. In this paper, the optimal nonlinear mapping relationship between fault types and fault symptoms of rolling bearings is obtained and then realize failure recognition of rolling bearings though the hybrid GA-BP algorithm. Finally, the results of the test experiment verifies the efficiency and accuracy of the proposed GA-BP algorithm.

Copyright

© 2017, 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  - Yue Wen
AU  - Minping Jia
AU  - Cheng Luo
PY  - 2017/03
DA  - 2017/03
TI  - Study of Fault Diagnosis for Rolling Bearing Based on GA-BP Algorithm
BT  - Proceedings of the 2017 2nd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2017)
PB  - Atlantis Press
SP  - 776
EP  - 781
SN  - 2352-5401
UR  - https://doi.org/10.2991/amcce-17.2017.137
DO  - 10.2991/amcce-17.2017.137
ID  - Wen2017/03
ER  -