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A Self-Attention Legendre Graph Convolution Network for Rotating Machinery Fault Diagnosis

Authors :
Jiancheng Ma
Jinying Huang
Siyuan Liu
Jia Luo
Licheng Jing
Source :
Sensors, Vol 24, Iss 17, p 5475 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 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.

Details

Language :
English
ISSN :
14248220
Volume :
24
Issue :
17
Database :
Directory of Open Access Journals
Journal :
Sensors
Publication Type :
Academic Journal
Accession number :
edsdoj.7a544d3013be497ea226b4c2090859d3
Document Type :
article
Full Text :
https://doi.org/10.3390/s24175475