Wind Turbine Gearbox Failure Identification with Deep Neural Networks

The feasibility of monitoring the health of wind turbine (WT) gearboxes based on the lubricant pressure data in the Supervisory Control and Data Acquisition (SCADA) system is investigated in this paper. A deep neural network (DNN) based framework is developed to monitor conditions of WT gearboxes and identify their impending failures. Six data-mining algorithms, the k nearest neighbors (kNN), least absolute shrinkage and selection operator (Lasso), ridge regression (Ridge), support vector machines (SVM), shallow neural network (NN), as well as deep neural network (DNN), are applied to model the lubricant pressure. A comparative analysis of developed data-driven models is conducted and the DNN model is the most accurate. To prevent the overfitting of the DNN model, a dropout algorithm is applied into the DNN training process. Computational results show that the prediction error will shift before the occurrences of gearbox failures. An exponentially weighted moving average (EWMA) control chart is deployed to derive criteria for detecting the shifts. The effectiveness of the proposed monitoring approach is demonstrated by examining real cases from wind farms in China and benchmarked against the gearbox monitoring based on the oil temperature data.