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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書誌詳細

出版年:	IEEE Access
主要な著者:	Haitao Wang, Xiyang Dai, Lichen Shi, Mingjun Li, Zelin Liu, Ruihua Wang, Xiaohua Xia
フォーマット:	論文
言語:	英語
出版事項:	IEEE 2024-01-01
主題:	Attentional mechanism fault diagnosis gram angle difference field generative adversarial network graph neural network (GCN) rotating machinery
オンライン･アクセス:	https://ieeexplore.ieee.org/document/10443404/