Wind Turbine Gearbox Temperature Monitor Based On CITDMFPA-WNN

DOI

10.2991/iceeecs-16.2016.159How to use a DOI?

Keywords

gearbox; temperature monitoring; flower pollination algorithm; wavelet neural network

Abstract

In the gearbox temperature monitoring, in order to improve the predictive accuracy of gearbox temperature, we presents a model that improved flower pollination algorithm optimizes wavelet neural network (CITDMFPA-WNN) used in gearbox temperature prediction. Use the model to predict the temperature of the gearbox, and then get the gearbox condition by means of the analysis of residual temperature, so as to achieve the goal of gearbox online temperature monitoring. By introducing chaos sequence and t distribution variation, flower pollination algorithm (FPA) has better optimization ability. Improved FPA is used to optimize the uncertain parameters of wavelet neural network that improves the training speed and precision of wavelet neural network. Experiments show that the forecast precision of CITDMFPA-WNN is better than that of IPSO-WNN and WNN, and we can find the abnormal temperature of gearbox and achieve the goal of real-time online monitoring.

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  - Baoyi Wang
AU  - Wuchao Liu
AU  - Shaomin Zhang
PY  - 2016/12
DA  - 2016/12
TI  - Wind Turbine Gearbox Temperature Monitor Based On CITDMFPA-WNN
BT  - Proceedings of the 2016 4th International Conference on Electrical & Electronics Engineering and Computer Science (ICEEECS 2016)
PB  - Atlantis Press
SP  - 812
EP  - 817
SN  - 2352-538X
UR  - https://doi.org/10.2991/iceeecs-16.2016.159
DO  - 10.2991/iceeecs-16.2016.159
ID  - Wang2016/12
ER  -