Multireceptive Field Graph Convolutional Networks for Machine Fault Diagnosis

Deep learning (DL) based methods have swept the field of mechanical fault diagnosis, because of the powerful ability of feature representation. However, many of existing DL methods fail in relationship mining between signals explicitly. Unlike those deep neural networks, graph convolutional networks...

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Veröffentlicht in:	IEEE transactions on industrial electronics (1982) Jg. 68; H. 12; S. 12739 - 12749
Hauptverfasser:	Li, Tianfu, Zhao, Zhibin, Sun, Chuang, Yan, Ruqiang, Chen, Xuefeng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.12.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Aggregates Artificial neural networks Chebyshev approximation Convolution Data mining Deep learning Eigenvalues and eigenfunctions Euclidean geometry Euclidean space Fault diagnosis graph convolutional networks Graphical representations Graphs Industries Laplace equations Machine learning mechanical fault diagnosis multireceptive field
ISSN:	0278-0046, 1557-9948
Online-Zugang:	Volltext
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