  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 health conditions in the
pitch system due to the long-term wear and tear poses challenges in accurately
classifying them, thus increasing the maintenance cost of wind turbines or even
damaging them. This paper proposes a novel method based on hard sample
mining-enabled supervised contrastive learning (HSMSCL) to address this
problem. The proposed method employs cosine similarity to identify hard samples
and subsequently, leverages supervised contrastive learning to learn more
discriminative representations by constructing hard sample pairs. Furthermore,
the hard sample mining framework in the proposed method also constructs hard
samples with learned representations to make the training process of the
multilayer perceptron (MLP) more challenging and make it a more effective
classifier. The proposed approach progressively improves the fault diagnosis
model by introducing hard samples in the SCL and MLP phases, thus enhancing its
performance in complex multi-class fault diagnosis tasks.
  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 performance,
providing promising prospects for enhancing the reliability and efficiency of
wind turbine pitch system fault diagnosis.
