Intelligent diagnosis of gearbox in data heterogeneous environments based on federated supervised contrastive learning framework

Intelligent diagnosis of gearbox in data heterogeneous environments based on federated supervised contrastive learning framework

Abstract To address the model training bottleneck caused by the coupling of data silos and heterogeneity in intelligent fault diagnosis, this study proposes a Federated Supervised Contrastive Learning (FSCL) framework. Traditional methods face dual challenges: on one hand, the scarcity of fault samp...

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Bibliographic Details
Main Authors: Ruoyang Bai, Hongwei Wang, Wenlei Sun, Li He, Yuxin Shi, Qingang Xu, Yunhang Wang, Xicong Chen
Format: Article
Language:English
Published: Nature Portfolio 2025-04-01
Series:Scientific Reports
Subjects:
Fault Diagnosis
Federated Learning
Data Heterogeneity
Contrastive Learning
Online Access:https://doi.org/10.1038/s41598-025-98806-2
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