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

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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Bibliographic Details
Main Authors: Haitao Wang, Xiyang Dai, Lichen Shi, Mingjun Li, Zelin Liu, Ruihua Wang, Xiaohua Xia
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Attentional mechanism
fault diagnosis
gram angle difference field
generative adversarial network
graph neural network (GCN)
rotating machinery
Online Access:https://ieeexplore.ieee.org/document/10443404/

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