STHFD: Spatial–Temporal Hypergraph-Based Model for Aero-Engine Bearing Fault Diagnosis
Abstract
1. Introduction
- We developed a fault diagnosis architecture that leverages hypergraphs to model high-order relationships among signal modalities. The framework supports dynamic hyperedge construction and incorporates a multi-head attention mechanism for updating node embeddings.
- A dedicated learning strategy is proposed to separately construct spatial and temporal hyperedges, allowing the framework to better represent complex dependencies in the data. The embedding update process captures both the heterogeneity and interactivity among signal nodes through attention-based aggregation.
- We evaluated the proposed STHFD framework on real-world aero-engine bearing datasets. The experimental results consistently show that our approach outperforms existing baseline models in fault classification tasks, demonstrating its robustness and practical potential.
2. Related Work
2.1. Traditional Learning Methods
2.2. Graph-Based Learning Methods
3. Methodology
3.1. Problem Definition
3.2. Dynamic Hypergraph Construction
3.3. Hypergraph Embedding Learning
3.3.1. Hyperedge Embedding
3.3.2. Multi-Head Attention for Node Updating
3.3.3. Graph Readout and Final Prediction
4. Experiments
- RQ1: How does the proposed STHFD model compare with existing baseline methods in terms of diagnostic accuracy and robustness?
- RQ2: To what extent can different models distinguish between fault categories, and what are the common patterns of class-wise misclassification?
- RQ3: How does each component of the proposed model contribute to its overall performance?
4.1. Dataset
4.2. Model Configuration
4.3. Comparison Methods
- (1) RF [30]: Random Forest, a widely used ensemble learning algorithm that constructs multiple decision trees and aggregates their predictions via majority voting.
- (2) CNN [31]: A 1D Convolutional Neural Network designed to capture local temporal patterns in vibration signals.
- (3) LSTM [31]: A Long Short-Term Memory network capable of modeling long-range temporal dependencies within sequential data.
- (4) BiLSTM [23]: A bidirectional variant of LSTM that processes a sequence in both forward and backward directions to enhance contextual feature learning.
- (5) GCN [32]: A Graph Convolutional Network that utilizes spatial graph structures constructed from sensor topology to model inter-channel dependencies.
4.4. Experiment Results and Analysis
4.4.1. Performance Comparison (RQ1)
4.4.2. Confusion Pattern Analysis (RQ2)
4.4.3. Ablation Study (RQ3)
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Class | Metric | RF | LSTM | CNN | BiLSTM | GCN | STHFD |
---|---|---|---|---|---|---|---|
1 | Precision | 0.8154 | 0.8167 | 0.8226 | 0.8413 | 0.9623 | 1.0000 |
Recall | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | |
F1-score | 0.8983 | 0.8991 | 0.9027 | 0.9138 | 0.9808 | 1.0000 | |
2 | Precision | 0.7000 | 0.8000 | 0.7838 | 0.9138 | 0.9242 | 1.0000 |
Recall | 0.7000 | 0.9677 | 0.9508 | 0.9516 | 1.0000 | 1.0000 | |
F1-score | 0.7000 | 0.8759 | 0.8593 | 0.8613 | 0.9606 | 1.0000 | |
3 | Precision | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
Recall | 0.4737 | 0.5517 | 0.5345 | 0.5357 | 0.8793 | 1.0000 | |
F1-score | 0.6429 | 0.7111 | 0.6966 | 0.5357 | 0.9358 | 1.0000 | |
4 | Precision | 0.7500 | 0.9649 | 0.9474 | 0.9464 | 1.0000 | 1.0000 |
Recall | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | |
F1-score | 0.8571 | 0.9821 | 0.9730 | 0.9725 | 1.0000 | 1.0000 |
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Bao, P.; Yi, W.; Zhu, Y.; Shen, Y.; Chai, B.X. STHFD: Spatial–Temporal Hypergraph-Based Model for Aero-Engine Bearing Fault Diagnosis. Aerospace 2025, 12, 612. https://doi.org/10.3390/aerospace12070612
Bao P, Yi W, Zhu Y, Shen Y, Chai BX. STHFD: Spatial–Temporal Hypergraph-Based Model for Aero-Engine Bearing Fault Diagnosis. Aerospace. 2025; 12(7):612. https://doi.org/10.3390/aerospace12070612
Chicago/Turabian StyleBao, Panfeng, Wenjun Yi, Yue Zhu, Yufeng Shen, and Boon Xian Chai. 2025. "STHFD: Spatial–Temporal Hypergraph-Based Model for Aero-Engine Bearing Fault Diagnosis" Aerospace 12, no. 7: 612. https://doi.org/10.3390/aerospace12070612
APA StyleBao, P., Yi, W., Zhu, Y., Shen, Y., & Chai, B. X. (2025). STHFD: Spatial–Temporal Hypergraph-Based Model for Aero-Engine Bearing Fault Diagnosis. Aerospace, 12(7), 612. https://doi.org/10.3390/aerospace12070612