Multi-Metric Fusion Hypergraph Neural Network for Rotating Machinery Fault Diagnosis
Effective fault diagnosis in rotating machinery means extracting fault features from complex samples. However, traditional data-driven methods often overly rely on labeled samples and struggle with extracting high-order complex features. To address these issues, a novel Multi-Metric Fusion Hypergrap...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Actuators |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-0825/14/5/242 |
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| Summary: | Effective fault diagnosis in rotating machinery means extracting fault features from complex samples. However, traditional data-driven methods often overly rely on labeled samples and struggle with extracting high-order complex features. To address these issues, a novel Multi-Metric Fusion Hypergraph Neural Network (MMF-HGNN) is proposed for fault diagnosis in rotating machinery. The approach involves constructing hypergraphs for sample vertices using three metrics: instance distance, distribution distance, and spatiotemporal distance. An innovative hypergraph fusion strategy is then applied to integrate these normalized hypergraphs, and a dual-layer hypergraph neural network is utilized for fault diagnosis. Experimental results on three different fault datasets demonstrate that the MMF-HGNN method excels in feature extraction, reduces reliance on labeled samples, achieving a classification accuracy of 0.9965 ± 0.0025 even with only 5% of labeled samples, and shows strong robustness to noise across varying signal-to-noise ratios. |
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| ISSN: | 2076-0825 |