Fault Diagnosis of Wind Turbines’ Bearing Based on PSO-AdaBoostSVM

DOI

10.2991/eame-18.2018.53How to use a DOI?

Keywords

fault diagnosis; PSO; bearing; PSO-AdaBoostSVM

Abstract

Bearing is an important part of wind turbine. In the past few years, there have been many intelligent fault diagnosis algorithms for it. Support vector machine is one of them. But it also has many shortcomings for fault diagnosis. For example, how to select kernel and parameters makes the classifier more accurate. In this paper, in order to find the best global parameters, we choose the PSO algorithm, and we also use the adaboost algorithm to improve the classification accuracy. By comparing the experimental results of other classifiers, the proposed PSO-AdaBoostSVM algorithm is superior to SVM and AdaBoostSVM in classification accuracy. So the proposed algorithm can be used in the fault diagnosis of wind Turbines’ Bearing.

Copyright

© 2018, 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/).

Download article (PDF)

TY  - CONF
AU  - Hao Gan
AU  - Bin Jiao
PY  - 2018/06
DA  - 2018/06
TI  - Fault Diagnosis of Wind Turbines’ Bearing Based on PSO-AdaBoostSVM
BT  - Proceedings of the 2018 3rd International Conference on Electrical, Automation and Mechanical Engineering (EAME 2018)
PB  - Atlantis Press
SP  - 252
EP  - 255
SN  - 2352-5401
UR  - https://doi.org/10.2991/eame-18.2018.53
DO  - 10.2991/eame-18.2018.53
ID  - Gan2018/06
ER  -