Research On Fault Diagnosis Of Wind Turbine Gearbox Based On IFA-ELM
- https://doi.org/10.2991/iceeecs-16.2016.153How to use a DOI?
- wind turbine; fault diagnosis; extreme learning machine; firefly algorithm
In order to effectively improve the accuracy of fault diagnosis of wind turbine gearbox, a fault diagnosis model based on improved firefly algorithm for extreme learning machine is proposed in this paper. The extreme learning machine overcomes the shortcomings of traditional neural network, such as slow convergence rate and easy to fall into local minimum points, however, the weights and thresholds of the input layer and the hidden layer are generated in a random way, this can lead to excessive number of nodes in the hidden layer, resulting in over fitting in the training process. In view of this problem, the firefly algorithm with high searching speed and high efficiency is used to optimize the parameters of the extreme learning machine. However, because of the fixed step size, the firefly algorithm is easy to fall into the local optimum in the early stage and slows down in the late convergence. Therefore, the step size of the firefly algorithm is improved to make it change with the change of the objective function in the search process so as to improve the performance of the firefly algorithm. The experimental results show that compared to the standard ELM, GA-ELM, and FA-ELM networks, the improved firefly algorithm for extreme learning machine that proposed in this paper has a higher prediction accuracy.
- © 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/).
Cite this article
TY - CONF AU - Shaomin Zhang AU - Jia Wei AU - Baoyi Wang PY - 2016/12 DA - 2016/12 TI - Research On Fault Diagnosis Of Wind Turbine Gearbox Based On IFA-ELM BT - Proceedings of the 2016 4th International Conference on Electrical & Electronics Engineering and Computer Science (ICEEECS 2016) PB - Atlantis Press SP - 775 EP - 780 SN - 2352-538X UR - https://doi.org/10.2991/iceeecs-16.2016.153 DO - https://doi.org/10.2991/iceeecs-16.2016.153 ID - Zhang2016/12 ER -