Wang, S.; Jiao, X.; Jing, B.; Pan, J.; Meng, X.; Huang, Y.; Pei, S. Coupling Fault Diagnosis of Bearings Based on Hypergraph Neural Network. Preprints2024, 2024081809. https://doi.org/10.20944/preprints202408.1809.v1
APA Style
Wang, S., Jiao, X., Jing, B., Pan, J., Meng, X., Huang, Y., & Pei, S. (2024). Coupling Fault Diagnosis of Bearings Based on Hypergraph Neural Network. Preprints. https://doi.org/10.20944/preprints202408.1809.v1
Chicago/Turabian Style
Wang, S., Yifeng Huang and Shaoting Pei. 2024 "Coupling Fault Diagnosis of Bearings Based on Hypergraph Neural Network" Preprints. https://doi.org/10.20944/preprints202408.1809.v1
Abstract
Coupling faults that occur simultaneously widely exist in the operation of mechanical equipment, and coupling fault features are formed by nonlinear coupling of multiple base fault features. In this paper, hypergraph neural network is used to obtain the characteristics of coupling faults, and two coupling fault diagnosis frameworks based on hypergraph are provided. 1. Coupling fault diagnosis framework based on feature generation: The hypergraph neural network is used as a generator to provide negative samples for discriminator training. The generator outputs hyperedge vectors as fake samples, and each hyperedge connects a base fault containing a fault type. 2. Coupling fault diagnosis framework based on feature extraction: Each node is regarded as a fault type, and the fault characteristics of each type of fault are extracted through the aggregation operation of hypergraph. The attention mechanism is introduced to realize the dynamic adjustment of the hyperedge, and the dynamic vertex is used to classify the unknown faults. The results show that the diagnostic accuracy of coupling faults under the two frameworks reaches 88.6% and 86.76%, respectively.
Engineering, Safety, Risk, Reliability and Quality
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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