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Open AccessArticle
A Rotating Machinery Fault Diagnosis Method Based on Dynamic Graph Convolution Network and Hard Threshold Denoising
by
Qiting Zhou
Qiting Zhou 1,2,
Longxian Xue
Longxian Xue 1,*,
Jie He
Jie He 1,
Sixiang Jia
Sixiang Jia 2 and
Yongbo Li
Yongbo Li 2
1
Chengdu Aircraft Design & Research Institute, Chengdu 610091, China
2
School of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, China
*
Author to whom correspondence should be addressed.
Sensors 2024, 24(15), 4887; https://doi.org/10.3390/s24154887 (registering DOI)
Submission received: 24 June 2024
/
Revised: 14 July 2024
/
Accepted: 16 July 2024
/
Published: 27 July 2024
Abstract
With the development of precision sensing instruments and data storage devices, the fusion of multi-sensor data in gearbox fault diagnosis has attracted much attention. However, existing methods have difficulty in capturing the local temporal dependencies of multi-sensor monitoring information, and the inescapable noise severely decreases the accuracy of multi-sensor information fusion diagnosis. To address these issues, this paper proposes a fault diagnosis method based on dynamic graph convolutional neural networks and hard threshold denoising. Firstly, considering that the relationships between monitoring data from different sensors change over time, a dynamic graph structure is adopted to model the temporal dependencies of multi-sensor data, and, further, a graph convolutional neural network is constructed to achieve the interaction and feature extraction of temporal information from multi-sensor data. Secondly, to avoid the influence of noise in practical engineering, a hard threshold denoising strategy is designed, and a learnable hard threshold denoising layer is embedded into the graph neural network. Experimental fault datasets from two typical gearbox fault test benches under environmental noise are used to verify the effectiveness of the proposed method in gearbox fault diagnosis. The experimental results show that the proposed DDGCN method achieves an average diagnostic accuracy of up to 99.7% under different levels of environmental noise, demonstrating good noise resistance.
Share and Cite
MDPI and ACS Style
Zhou, Q.; Xue, L.; He, J.; Jia, S.; Li, Y.
A Rotating Machinery Fault Diagnosis Method Based on Dynamic Graph Convolution Network and Hard Threshold Denoising. Sensors 2024, 24, 4887.
https://doi.org/10.3390/s24154887
AMA Style
Zhou Q, Xue L, He J, Jia S, Li Y.
A Rotating Machinery Fault Diagnosis Method Based on Dynamic Graph Convolution Network and Hard Threshold Denoising. Sensors. 2024; 24(15):4887.
https://doi.org/10.3390/s24154887
Chicago/Turabian Style
Zhou, Qiting, Longxian Xue, Jie He, Sixiang Jia, and Yongbo Li.
2024. "A Rotating Machinery Fault Diagnosis Method Based on Dynamic Graph Convolution Network and Hard Threshold Denoising" Sensors 24, no. 15: 4887.
https://doi.org/10.3390/s24154887
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