用动态注意力机制提升机械故障诊断的准确性和抗噪能力
Polarized Direct Cross-Attention Message Passing in GNNs for Machinery Fault Diagnosis
- 基于节点特征自动生成图结构，动态调整信息传递权重
- 在三个工业数据集上达到领先准确率，噪声环境下仍保持稳定
- 适合需要高可靠性的工业设备故障检测场景
安全关键工业系统的可靠性依赖于旋转机械故障的精准诊断。传统图神经网络因依赖预设静态图结构和同质聚合方式，在建模复杂动态交互方面存在局限。本文提出极化直接交叉注意力（PolaDCA）关系学习框架，通过数据驱动构建图结构实现自适应消息传递。该方法基于直接交叉注意力（DCA）机制，从三类语义不同的节点特征（个体特性、邻域共识、邻域多样性）中动态推断注意力权重，无需固定邻接矩阵。理论分析表明，PolaDCA相比传统GNN具有更强的抗噪鲁棒性。在XJTUSuprgear、CWRUBearing和Three-Phase Flow Facility三个工业数据集上的大量实验验证了其卓越的诊断准确率与跨噪声条件下的泛化能力，优于七种基准方法。该框架为安全关键工业应用提供了有效解决方案。
原文摘要 · Abstract (English)
The reliability of safety-critical industrial systems hinges on accurate and robust fault diagnosis in rotating machinery. Conventional graph neural networks (GNNs) for machinery fault diagnosis face limitations in modeling complex dynamic interactions due to their reliance on predefined static graph structures and homogeneous aggregation schemes. To overcome these challenges, this paper introduces polarized direct cross-attention (PolaDCA), a novel relational learning framework that enables adaptive message passing through data-driven graph construction. Our approach builds upon a direct cross-attention (DCA) mechanism that dynamically infers attention weights from three semantically distinct node features (such as individual characteristics, neighborhood consensus, and neighborhood diversity) without requiring fixed adjacency matrices. Theoretical analysis establishes PolaDCA's superior noise robustness over conventional GNNs. Extensive experiments on industrial datasets (i.e., XJTUSuprgear, CWRUBearing and Three-Phase Flow Facility datasets) demonstrate state-of-the-art diagnostic accuracy and enhanced generalization under varying noise conditions, outperforming seven competitive baseline methods. The proposed framework provides an effective solution for safety-critical industrial applications.
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