학술논문
The Semisupervised Weighted Centroid Prototype Network for Fault Diagnosis of Wind Turbine Gearbox
Document Type
Periodical
Source
IEEE/ASME Transactions on Mechatronics IEEE/ASME Trans. Mechatron. Mechatronics, IEEE/ASME Transactions on. 29(2):1567-1578 Apr, 2024
Subject
Language
ISSN
1083-4435
1941-014X
1941-014X
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.