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Open AccessArticle
A Self-Attention Legendre Graph Convolution Network for Rotating Machinery Fault Diagnosis
by
Jiancheng Ma
Jiancheng Ma 1,
Jinying Huang
Jinying Huang 1,2,*,
Siyuan Liu
Siyuan Liu 1,
Jia Luo
Jia Luo 3
and
Licheng Jing
Licheng Jing 2
1
School of Computer Science and Technology, North University of China, Taiyuan 030051, China
2
School of Mechanical Engineering, North University of China, Taiyuan 030051, China
3
School of Energy and Power Engineering, North University of China, Taiyuan 030051, China
*
Author to whom correspondence should be addressed.
Sensors 2024, 24(17), 5475; https://doi.org/10.3390/s24175475 (registering DOI)
Submission received: 23 July 2024
/
Revised: 14 August 2024
/
Accepted: 22 August 2024
/
Published: 23 August 2024
Abstract
Rotating machinery is widely used in modern industrial systems, and its health status can directly impact the operation of the entire system. Timely and accurate diagnosis of rotating machinery faults is crucial for ensuring production safety, reducing economic losses, and improving efficiency. Traditional deep learning methods can only extract features from the vertices of the input data, thereby overlooking the information contained in the relationships between vertices. This paper proposes a Legendre graph convolutional network (LGCN) integrated with a self-attention graph pooling method, which is applied to fault diagnosis of rotating machinery. The SA-LGCN model converts vibration signals from Euclidean space into graph signals in non-Euclidean space, employing a fast local spectral filter based on Legendre polynomials and a self-attention graph pooling method, significantly improving the model’s stability and computational efficiency. By applying the proposed method to 10 different planetary gearbox fault tasks, we verify that it offers significant advantages in fault diagnosis accuracy and load adaptability under various working conditions.
Share and Cite
MDPI and ACS Style
Ma, J.; Huang, J.; Liu, S.; Luo, J.; Jing, L.
A Self-Attention Legendre Graph Convolution Network for Rotating Machinery Fault Diagnosis. Sensors 2024, 24, 5475.
https://doi.org/10.3390/s24175475
AMA Style
Ma J, Huang J, Liu S, Luo J, Jing L.
A Self-Attention Legendre Graph Convolution Network for Rotating Machinery Fault Diagnosis. Sensors. 2024; 24(17):5475.
https://doi.org/10.3390/s24175475
Chicago/Turabian Style
Ma, Jiancheng, Jinying Huang, Siyuan Liu, Jia Luo, and Licheng Jing.
2024. "A Self-Attention Legendre Graph Convolution Network for Rotating Machinery Fault Diagnosis" Sensors 24, no. 17: 5475.
https://doi.org/10.3390/s24175475
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