Cost-Sensitive LightGBM-Based Online Fault Detection Method for Wind Turbine Gearboxes - Tang, Mingzhu; Zhao, Qi; Wu, Huawei; Wang, Zimin (Frontiers in Energy Research, 2021)
In practice, fault samples of wind turbine (WT) gearboxes are far smaller than normal samples during operation, and most of existing fault diagnosis methods for WT gearboxes only focusing on the improvement of classification accuracy, ignore the decrease of missed alarms and reduction of the average cost. To this end, a new framework is proposed through combining Spearman rank correlation feature extraction and cost-sensitive LightGBM algorithm for WT gearboxes fault detection. In this paper, features from wind turbines supervisory control and data acquisition (SCADA) systems are firstly extracted. Then the feature selection is employed by using the expert experience and Spearman rank correlation coefficient to analyze the correlation between the big data of WT gearboxes. Moreover, the cost-sensitive LightGBM fault detection framework is established by optimizing the misclassification cost. The false alarm rate and the miss detection rate of the WT gearbox under different working conditions are finally obtained. Experiments have demonstrated that the proposed method can significantly improve the fault detection accuracy. Meanwhile, the proposed method can consistently outperform traditional classifiers such as Adacost, cost-sensitive GBDT and cost-sensitive XGBoost in terms of low false alarm rate and miss detection rate. Owing to its high Matthews correlation coefficient scores, low average misclassification cost, cost-sensitive LightGBM (CS-LightGBM) method is preferred for imbalanced WT gearbox fault detection in practice.