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Data-Augmentation Based CBAM-ResNet-GCN Method for Unbalance Fault Diagnosis of Rotating Machinery

In practical engineering scenarios, machines are seldom in a faulty operating state, so it is difficult to get enough available sample data to train the fault diagnosis model, leading to the problem of the small and unbalanced number of rotating machinery fault samples and low fault diagnosis accura...

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Principais autores: Haitao Wang, Xiyang Dai, Lichen Shi, Mingjun Li, Zelin Liu, Ruihua Wang, Xiaohua Xia
Formato: Artigo
Idioma:Inglês
Publicado em: IEEE 2024-01-01
coleção:IEEE Access
Assuntos:
Attentional mechanism
fault diagnosis
gram angle difference field
generative adversarial network
graph neural network (GCN)
rotating machinery
Acesso em linha:https://ieeexplore.ieee.org/document/10443404/
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