一种风电机组轴承健康劣化趋势预测方法

A Method for Predicting Bearing Health Degradation Trend of Wind Turbines

以风电机组轴承为研究对象,利用SCADA监测有关参数,计算这些参数与轴承温度的相关系数,对其归一化后得到各参数的影响权重,然后基于温度特征量构建轴承健康劣化度模型。应用改进的集合经验模态分解(EEMD)将具有非平稳性特性的劣化趋势分解为一系列相对平稳的分量,采用时间序列神经网络模型分别预测各类分量,叠加所有预测分量得到最终预测结果。结果表明:本文方法的预测精度较高,提高了与实际劣化度曲线的吻合程度。

Taking the bearing of a wind turbine as an object of study, using the monitored parameters by supervisory control and data acquisition （SCADA）, the correlation coefficient between monitored parame- ters and bearing temperatures was calculated and normalized to obtain the influence weight, and subsequently a model for degradation trend of bearing health was established. The degradation trend with nonsteady characteristics was decomposed by modified ensemble empirical mode decomposition （EEMD） to acquire several relatively steady components, and then each component was predicted by time series neural network, and final prediction results were formed by superposition of the predicted results of all components. Results show that the method proposed has higher prediction accuracy, which helps to improve the degree of conformity with actual degradation curves.