Hard Sample Mining Enabled Contrastive Feature Learning for Wind Turbine Pitch System Fault Diagnosis

The efficient utilization of wind power by wind turbines relies on the ability of their pitch systems to adjust blade pitch angles in response to varying wind speeds. However, the presence of multiple fault types in the pitch system poses challenges in accurately classifying these faults. This paper proposes a novel method based on hard sample mining-enabled contrastive feature learning (HSMCFL) to address this problem. The proposed method employs cosine similarity to identify hard samples and subsequently leverages contrastive feature learning to enhance representation learning through the construction of hard sample pairs. Furthermore, a multilayer perceptron is trained using the learned discriminative representations to serve as an efficient classifier. To evaluate the effectiveness of the proposed method, two real datasets comprising wind turbine pitch system cog belt fracture data are utilized. The fault diagnosis performance of the proposed method is compared against existing methods, and the results demonstrate its superior performance. The proposed approach exhibits significant improvements in fault diagnosis accuracy, providing promising prospects for enhancing the reliability and efficiency of wind turbine pitch system fault diagnosis.