Modeling and Simulation of Health Degradation Trend for Wind Turbine Bearing

Authors

Xinghui Dong, 1. School of Energy Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China; ;
Xiaoshuang Ma, 1. School of Energy Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China; ;
Youxing Cheng, 1. School of Energy Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China; ;
Wang Shuai, 2. School of Electric Engineering, Henan Polytechnic University, Jiaozuo 454000, China;

Abstract

Abstract: By taking a wind turbine bearing as research object, the model of bearing temperature health’s degradation trend is established through using least squares surface fitting and the monitored parameters from Supervisory Control And Data Acquisition (SCADA). Bearings’ degradation trend with unsteady characteristics is decomposed by modified Ensemble Empirical Mode Decomposition(EEMD) to obtain several relatively steady components. Components are predicted respectively by time series neural network and the predicted results of all the components are added to obtain final prediction result. Comprehensive simulations and comparisons show that the proposed method can predict the health degradation trend of wind turbine bearings with higher accuracy.

Recommended Citation

Dong, Xinghui; Ma, Xiaoshuang; Cheng, Youxing; and Shuai, Wang (2019) "Modeling and Simulation of Health Degradation Trend for Wind Turbine Bearing," Journal of System Simulation: Vol. 31: Iss. 1, Article 20.
DOI: 10.16182/j.issn1004731x.joss.17-0067
Available at: https://dc-china-simulation.researchcommons.org/journal/vol31/iss1/20

First Page

151

Revised Date

2017-08-21

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.17-0067

Last Page

158

CLC

TM315;TP202+.1

Recommended Citation

Dong Xinghui, Ma Xiaoshuang, Cheng Youxing, Wang Shuai. Modeling and Simulation of Health Degradation Trend for Wind Turbine Bearing[J]. Journal of System Simulation, 2019, 31(1): 151-158.

DOI

10.16182/j.issn1004731x.joss.17-0067