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Related Experiment Video

Updated: Apr 23, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Adaptive feature fusion network for machine fault diagnosis with multiple knowledge based graphs.

ChangChao Liu1, Jinzhe Han2, Yue Zhang2

  • 1School of Control Sciences and Engineering, Shandong University, Jinan, 250061, Shandong, China.

Scientific Reports
|April 21, 2026
PubMed
Summary

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This study introduces a hybrid graph neural network (HGNN) with an adaptive feature fusion network (AFFN) for robust rotating machinery fault diagnosis. The AFFN-HGNN improves accuracy and efficiency in noisy environments.

Area of Science:

  • Intelligent fault diagnosis
  • Machine learning for engineering systems
  • Signal processing and analysis

Background:

  • Graph neural networks (GNNs) model sensor signal dependencies for fault diagnosis.
  • Existing GNNs struggle with poor representation and edge redundancy in direct signal-to-graph construction.
  • Noise in monitoring environments degrades GNN performance for rotating machinery fault diagnosis.

Purpose of the Study:

  • To propose a novel Adaptive Feature Fusion Network-Hybrid Graph Neural Network (AFFN-HGNN) for rotating machinery fault diagnosis.
  • To enhance feature representation and model adaptability in noisy conditions.
  • To improve the accuracy and robustness of intelligent fault diagnosis systems.

Main Methods:

  • Constructing multiple knowledge-based graphs to explore fault information from diverse perspectives.
Keywords:
Adaptive feature fusionDeep learningGraph embeddingGraph neural networksIntelligent fault diagnosis

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  • Developing an adaptive feature fusion network (AFFN) to fuse time-spatial dependencies and node correlations.
  • Dynamically adjusting fusion weights to optimize learning efficiency and minimize computational overhead.

    • Main Results:

    • The proposed AFFN-HGNN demonstrated superior robustness and accuracy in fault diagnosis across three mechanical systems.
    • Experimental results confirmed improved performance compared to existing GNN-based fault diagnosis models.
    • The method effectively handles noisy environments, enhancing diagnostic reliability.

    Conclusions:

    • The AFFN-HGNN offers a significant advancement in intelligent fault diagnosis for rotating machinery.
    • Adaptive feature fusion and multi-graph construction are key to overcoming limitations of traditional GNNs.
    • The approach provides a more accurate and efficient solution for fault diagnosis in challenging industrial settings.