RESEARCH PAPER
Multi-Scale Graph Transformer for Rolling Bearing Fault Diagnosis
1
College of Intelligent Systems Science and Engineering, Harbin Engineering University, China
2
College of Power and Energy Engineering, Harbin Engineering University, China
Submission date: 2024-05-07
Final revision date: 2024-09-11
Acceptance date: 2024-10-16
Online publication date: 2024-10-20
Publication date: 2024-10-20
Corresponding author
Yunpeng Cao
College of Power and Energy Engineering, Harbin Engineering University, 150001, Harbin, China
College of Power and Energy Engineering, Harbin Engineering University, 150001, Harbin, China
Eksploatacja i Niezawodność – Maintenance and Reliability 2025;27(2):194779
HIGHLIGHTS
- Multi-Scale Graph Transformer enhances fault diagnosis precision significantly.
- Graph Node Aggregation Mechanism broadens receptive fields for feature extraction.
- Centrality and Spatial Encoding capture intricate graph node structural insights.
- Transformer Self-Attention improves crucial fault signature identification.
KEYWORDS
Deep LearningFeature ExtractionMulti-scale Graph TransformerRolling BearingsAdvanced Fault Diagnosis
TOPICS
ABSTRACT
Traditional graph neural networks often encounter limitations in fault diagnosis due to insufficient feature extraction at a single scale, particularly in complex operational scenarios. To overcome these challenges, we introduce an innovative multi-scale graph Transformer framework for rolling bearing fault diagnosis. This framework incorporates a distinctive multi-scale feature aggregation mechanism, along with centrality and spatial encoding for graph nodes, to enhance structural insights. Leveraging multi-head self-attention, our approach efficiently extracts and learns fault features, thereby significantly improving fault identification. Extensive experiments on the designed bearing dataset, as well as a customized rolling bearing apparatus, validate the efficacy of our method. Our model achieves a peak diagnostic precision of 99.5% and maintains an average accuracy exceeding 97.9%, underscoring its robustness and adaptability across diverse operational scenarios.
ACKNOWLEDGEMENTS
This research was supported by the National Science and Technology Major Project (2019-I-0003-0004).
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| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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