Enhancing Gas Turbine Fault Diagnosis Using a Multi-Scale Dilated Graph Variational Autoencoder Model

This paper proposes a Multi-scale Dilated Variational Graph Convolutional Autoencoder (MG-VAE) model for gas turbine fault diagnosis. The model integrates a multi-scale dilated convolutional attention mechanism to extract features across different scales, enhancing its ability to represent complex d...

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Bibliographic Details
Published in:	IEEE access Vol. 12; pp. 104818 - 104832
Main Authors:	Kun, Zhang, Hongren, Li, Xin, Wang, Daxing, Xie, Shuai, Yang
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Ablation Accuracy Artificial neural networks Convolutional neural networks Data mining Data models Fault diagnosis Feature extraction Gas turbine Gas turbines Industrial applications MG-VAE Monitoring noise robustness Real gases Turbines
ISSN:	2169-3536, 2169-3536
Online Access:	Get full text
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