Bearing fault detection by using graph autoencoder and ensemble learning.
Sci Rep
; 14(1): 5206, 2024 Mar 03.
Article
em En
| MEDLINE
| ID: mdl-38433237
ABSTRACT
The research and application of bearing fault diagnosis techniques are crucial for enhancing equipment reliability, extending bearing lifespan, and reducing maintenance expenses. Nevertheless, most existing methods encounter challenges in discriminating between signals from machines operating under normal and faulty conditions, leading to unstable detection results. To tackle this issue, the present study proposes a novel approach for bearing fault detection based on graph neural networks and ensemble learning. Our key contribution is a novel stochasticity-based compositional method that transforms Euclidean-structured data into a graph format for processing by graph neural networks, with feature fusion and a newly proposed ensemble learning strategy for outlier detection specifically designed for bearing fault diagnosis. This approach marks a significant advancement in accurately identifying bearing faults, highlighting our study's pivotal role in enhancing diagnostic methodologies.
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Bases de dados:
MEDLINE
Idioma:
En
Revista:
Sci Rep
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
China