Fault Diagnosis for Wind Turbine Generators Using Normal Behavior Model Based on Multi-Task Learning

Wind turbines (WTs) usually work in harsh environments and complex operating conditions, it leads to operating data of WTs with non-stationary, randomness and outlier. These imperfect data will inevitably impact the fault diagnosis of WTs. In this work, a fault diagnosis method for WT generators using normal behavior model (NBM) based on multi-task learning (MTL) is proposed. The imperfect operating data come from WTs are preprocessed and divided into different conditions. Each divided condition is constructed as a task, and a special deep neural network (DNN) is employed to train each task. Different tasks share some parameters through
$L_{2}$
regularization at the bottom layer, and are designed independently at the upper layer. Multiple tasks learn at the same time and promote each other’s performance. We employ actual operational data to test the validity for NBM of WTs. The comparative results indicate that the proposed method overcomes the deficiency of imperfect data and is suitable for fault diagnosis under complex operating conditions. Note to Practitioners—Traditional fault diagnosis methods do not divide imperfect data under complex operating conditions, resulting in poor generalization and robustness in practical engineering applications. This work proposes a data-driven fault diagnosis method, which regards the imperfect data under each operating condition as a task and employs MTL to improve the performance of each task. Experiments results show that the proposed method is feasible, but the method has not been tested on other types of wind turbines. Next, we will extend this method to other mechanical components under complex operating conditions. At the same time, we also consider embedding the proposed algorithm into SCADA system as an Active X control object, which can automatically analyze the imperfect data.