风力机主轴承故障监测方法

Fault Monitoring Method of Wind Turbine Main Bearing

根据风力机主轴承发生故障时引起其温度异常波动的特点,提出一种基于温度模型的故障监测方法。首先,分别建立正常运行状态下主轴承温度的多元线性回归预测模型、灰色预测模型、支持向量机回归预测模型及其组合预测模型;其次,在最佳预测模型基础上引入滑动窗口方法,研究主轴承温度预测残差统计特性;最后,通过对比温度残差均值或标准差的置信区间与设定临界值判断主轴承是否发生故障。研究结果表明,主轴承温度组合预测模型预测效果最佳,其判定系数分别较多元线性回归模型、灰色预测模型及支持向量机回归模型提高0.049 3,0.002 7和0.000 2,通过滑动窗口方法所求解的温度预测残差统计特性可及时反映出主轴承运行状态,这对风力机主轴承故障状态的高效监测及制定科学健康的维护策略提供依据。

Aiming to the problem that high cost and error of traditional fault monitoring method,a fault monitoring method is proposed based on the temperature model for fault monitoring of the wind turbine main bearings.The multiple linear regression model,grey model,support vector machine regression model and their combination forecasting model of main bearing temperature under normal operation are established respectively. Based on the combined prediction model,the sliding window method is introduced to analyse the change of temperature residual mean and standard deviation in the faulty condition of the main bearing. The failure of the main bearing is judged by comparing the confidence interval of the mean value or standard deviation of temperature residual with the set critical value. The results indicate that,the determination coefficient of the main bearing temperature combination forecasting model value is 0.049 3,0.002 7 and 0.000 2 higher than that of the multivariate linear regression model,the grey prediction model and the support vector machine regression model,the combination forecasting model by introducing a sliding window method can reflect the abnormal situation of main bearing temperature in time,which provides high-precision monitoring of the main bearing fault state and formulating scientific and healthy maintenance strategy.