Modeling and Simulation of Health Degradation Trend for Wind Turbine Bearing

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

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.

Key words: wind turbine bearing, degradation trend prediction, least squares surface, EEMD, time series neural network

CLC Number:

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.

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