Enhancing Fault Diagnosis in Mechanical Systems with Graph Neural Networks Addressing Class Imbalance

Wenhao Lu, Wei Wang, Xuefei Qin and Zhiqiang Cai ()
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Wenhao Lu: School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, China
Wei Wang: School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, China
Xuefei Qin: School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, China
Zhiqiang Cai: School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, China

Mathematics, 2024, vol. 12, issue 13, 1-22

Abstract: Recent advancements in intelligent diagnosis rely heavily on data-driven methods. However, these methods often encounter challenges in adequately addressing class imbalances in the context of the fault diagnosis of mechanical systems. This paper proposes the MeanRadius-SMOTE graph neural network (MRS-GNN), a novel framework designed to synthesize node representations in GNNs to effectively mitigate this issue. Through integrating the MeanRadius-SMOTE oversampling technique into the GNN architecture, the MRS-GNN demonstrates an enhanced capability to learn from under-represented classes while preserving the intrinsic connectivity patterns of the graph data. Comprehensive testing on various datasets demonstrates the superiority of the MRS-GNN over traditional methods in terms of classification accuracy and handling class imbalances. The experimental results on three publicly available fault diagnosis datasets show that the MRS-GNN improves the classification accuracy by 18 percentage points compared to some popular methods. Furthermore, the MRS-GNN exhibits a higher robustness in extreme imbalance scenarios, achieving an AUC-ROC value of 0.904 when the imbalance rate is 0.4. This framework not only enhances the fault diagnosis accuracy but also offers a scalable solution applicable to diverse mechanical and complex systems, demonstrating its utility and adaptability in various operating environments and fault conditions.

Keywords: class imbalance; graph neural networks; fault diagnosis; oversampling techniques (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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