Lightweight Knowledge Distillation-Based Transfer Learning Framework for Rolling Bearing Fault Diagnosis

Lightweight Knowledge Distillation-Based Transfer Learning Framework for Rolling Bearing Fault Diagnosis

Compared to fault diagnosis across operating conditions, the differences in data distribution between devices are more pronounced and better aligned with practical application needs. However, current research on transfer learning inadequately addresses fault diagnosis issues across devices. To bette...

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Bibliographic Details
Main Authors: Ruijia Lu, Shuzhi Liu, Zisu Gong, Chengcheng Xu, Zonghe Ma, Yiqi Zhong, Baojian Li
Format: Article
Language:English
Published: MDPI AG 2024-03-01
Series:Sensors
Subjects:
lightweight
knowledge distillation
variational-scale residual networks
multi-kernel domain adaptation approach
Online Access:https://www.mdpi.com/1424-8220/24/6/1758

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https://www.mdpi.com/1424-8220/24/6/1758

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