Bearing Fault Diagnosis Based on IFA-ELM

DOI

10.2991/aiie-16.2016.103How to use a DOI?

Keywords

firefly algorithm, extreme learning machine, pattern recognition, fault diagnosis

Abstract

Extreme learning machine (ELM) is a simple and effective feedforward neural network. It can be used in pattern recognition. But its classification ability is not good enough. In order to solve this problem, this paper proposed an improved firefly algorithm and used it in the parameters selection of ELM. After establishing the IFA-ELM model, we use UCI standard data set to verify its classification ability. Finally, the model is used in bearing fault diagnosis and obtains a good result.

Copyright

© 2016, 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  - Qunxian Chen
AU  - Zekun Zhou
PY  - 2016/11
DA  - 2016/11
TI  - Bearing Fault Diagnosis Based on IFA-ELM
BT  - Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)
PB  - Atlantis Press
SP  - 449
EP  - 452
SN  - 1951-6851
UR  - https://doi.org/10.2991/aiie-16.2016.103
DO  - 10.2991/aiie-16.2016.103
ID  - Chen2016/11
ER  -