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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...

Whakaahuatanga katoa

I tiakina i:
Ngā kaituhi matua: Haitao Wang, Xiyang Dai, Lichen Shi, Mingjun Li, Zelin Liu, Ruihua Wang, Xiaohua Xia
Hōputu: Artigo
Reo:Inglês
I whakaputaina: IEEE 2024-01-01
Rangatū:IEEE Access
Ngā marau:
Attentional mechanism
fault diagnosis
gram angle difference field
generative adversarial network
graph neural network (GCN)
rotating machinery
Urunga tuihono:https://ieeexplore.ieee.org/document/10443404/
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