Embedding-Enhanced Graph Attention Networks for Imbalanced Industrial Fault Diagnosis

Embedding-Enhanced Graph Attention Networks for Imbalanced Industrial Fault Diagnosis

Intricate nonlinear interactions among measurements and imbalanced distribution of fault samples pose considerable challenges for accurate fault diagnosis in modern industrial processes. To address these challenges, this study proposes an embedding-enhanced graph attention network to learn efficient...

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Bibliographic Details
Main Authors: Chi Zhang, Yuliang Li, Lifeng Fan, Lixiang Shen, Ting Qin
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Attention mechanism
complex industrial processes
data imbalance
deep learning
fault diagnosis
feature extraction
Online Access:https://ieeexplore.ieee.org/document/10506705/
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https://ieeexplore.ieee.org/document/10506705/

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