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.

本文提出了一种基于对比特征学习的硬样本挖掘方法，通过学习区分表示来诊断风力发电机叶片调 pitch 系统的故障。实验表明，该方法在故障诊断准确性上具有显著优势。