The Semisupervised Weighted Centroid Prototype Network for Fault Diagnosis of Wind Turbine Gearbox
Zuqiang Su, Xiaolong Zhang, Guoyin Wang, Shuxian Wang, Maolin Luo, Xin Wang
Abstract
The success of fault diagnosis based on deep learning benefits from a large amount of labeled fault samples. However, the scarcity of labeled fault samples in fault diagnosis of wind turbine gearbox (WTG) makes it difficult to train a satisfactory diagnostic model. To address this issue, this article proposed a semisupervised weighted centroid prototype network (SSWCPN) for WTG fault diagnosis. Specifically, SSWCPN is a few-shot semisupervised learning framework, which alleviates the matter of overfitting caused by the lack of supervision information. First, to capture abundant semisupervised information to guide network training, a sample selection model based on the evolution trend of posterior probability is proposed, which could efficiently cherry-pick out the unlabeled samples of high confidence to refine prototypes. Second, a new prototype updating strategy based on a weighted centroid prototype is designed, which controls the prototype drifting issue caused by incorrect pseudolabels and the introduction of new data distribution. Finally, experiments performed on test-bench data and successful application on WTG data show that the proposed SSWCPN-based WTG fault diagnosis achieves the best fault diagnosis performance among the comparison methods.