Fault Diagnosis of Wind Turbine Pitch Bearings Based on Online Soft-Label Meta-Learning and Gaussian Prototype Network

Lianghong Wang, Zhongzhuang Bai, Hongxiang Li, Panpan Yang, Jie Tao (), Xuemei Zou, Jinliang Zhao and Chunwei Wang
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Lianghong Wang: Yunnan Dianneng Smart Energy Co., Ltd., Kunming 650228, China
Zhongzhuang Bai: Yunnan Dianneng Smart Energy Co., Ltd., Kunming 650228, China
Hongxiang Li: Yunnan Dianneng Smart Energy Co., Ltd., Kunming 650228, China
Panpan Yang: Yunnan Dianneng Smart Energy Co., Ltd., Kunming 650228, China
Jie Tao: School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
Xuemei Zou: School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
Jinliang Zhao: Shanghai Electric Power Electronics Co., Ltd., Shanghai 201906, China
Chunwei Wang: Shanghai Electric Power Electronics Co., Ltd., Shanghai 201906, China

Energies, 2025, vol. 18, issue 16, 1-22

Abstract: Meta-learning has demonstrated significant advantages in small-sample tasks and has attracted considerable attention in wind turbine fault diagnosis. However, due to extreme operating conditions and equipment aging, the monitoring data of wind turbines often contain false alarms or missed detections. This results in inaccurate fault sample labeling. In meta-learning, these erroneous labels not only fail to help models quickly adapt to new meta-test tasks, but they also interfere with learning for new tasks, which leads to “negative transfer” phenomena. To address this, this paper proposes a novel method called Online Soft-Labeled Meta-learning with Gaussian Prototype Networks (SL-GPN). During training, the method dynamically aggregates feature similarities across multiple tasks or samples to form online soft labels. They guide model training process and effectively solve small-sample bearing fault diagnosis challenges. Experimental tests on small-sample data under various operating conditions and error labels were carried out. The results show that the proposed method improves diagnostic accuracy in small-sample environments, reduces false alarm rates, and demonstrates excellent generalization performance.

Keywords: meta-learning; soft-label; small sample; wind turbine; fault diagnosis; Gaussian prototype network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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