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Wind Turbine Gearbox Condition Monitoring Based on Class of Support Vector Regression Models and Residual Analysis

机译：基于支持向量回归模型的风力涡轮机齿轮箱状态监测及剩余分析

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The intelligent condition monitoring of wind turbines reduces their downtime and increases reliability. In this manuscript, a feature selection-based methodology that essentially works on regression models is used for identifying faulty scenarios. Supervisory control and data acquisition (SCADA) data with 1009 samples from one year and one month before failure are considered. Gearbox oil and bearing temperatures are treated as target variables with all the other variables used for the prediction model. Neighborhood component analysis (NCA) as a feature selection technique is employed to select the best features and prediction performance for several machine learning regression models is assessed. The results reveal that twin support vector regression (99.91%) and decision trees (98.74%) yield the highest accuracy for gearbox oil and bearing temperatures respectively. It is observed that NCA increases the accuracy and thus reliability of the condition monitoring system. Furthermore, the residuals from the class of support vector regression (SVR) models are tested from a statistical point of view. Diebold–Mariano and Durbin–Watson tests are carried out to establish the robustness of the tested models.
机译：风力涡轮机的智能状态监测降低了它们的停机时间并提高了可靠性。在此稿件中，基于特征选择的方法，基本上用于回归模型的工作方法用于识别错误的方案。监督控制和数据采集（SCADA）在一年和一个月内进行1009个样本的数据，在未能前一个月。变速箱油和轴承温度被视为目标变量，其中包含用于预测模型的所有其他变量。邻域分量分析（NCA）作为特征选择技术被用于选择评估多种机器学习回归模型的最佳特征和预测性能。结果表明，双支持载体回归（99.91％）和决策树（98.74％）分别产生齿轮箱油和轴承温度的最高精度。观察到NCA增加了条件监测系统的准确性，从而增加了可靠性。此外，从统计观点测试了来自支持向量回归（SVR）模型的剩余物。正在进行DieBold-Mariano和Durbin-Watson测试以确定测试模型的稳健性。

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