用图模型处理振动数据，提升工业故障诊断的鲁棒性。
Ensemble-Enhanced Graph Autoencoder with GAT and Transformer-Based Encoders for Robust Fault Diagnosis
- 将时序振动数据转为图结构，结合熵与动态时间规整构建节点关系。
- 在CWRU数据集上达0.99平均F1分数，显著优于CNN、LSTM等基线模型。
- 适用于复杂工况下的故障分类，尤其对难分类别表现突出。
工业机械故障分类对提升可靠性、减少停机至关重要，但不同工况下振动模式差异大，带来挑战。本文提出一种基于图的新框架，将不同功率下的机械振动时序数据转换为图表示。利用香农熵确定最优数据分段窗口，确保每段捕捉显著时序特征；通过动态时间规整（DTW）度量片段相似性以定义图边。构建了包含深度图变压器编码器、解码器及集成分类器的图自编码器（GAE），用于学习潜在图表示并分类多种故障。在案例西部保留大学（CWRU）数据集上评估性能，并在华中科技大学（HUST）数据集上测试跨数据集泛化能力。结果表明，该方法在CWRU数据集上平均F1得分为0.99，显著优于基线模型（CNN、LSTM、RNN、GRU、Bi-LSTM，F1分数0.94–0.97，双样本秩和检验p < 0.05，其中Bi-LSTM对比p < 0.05），尤其在困难类别（如第8类：0.99 vs Bi-LSTM的0.71）表现更优。可视化分析显示，振动幅度增强且故障动力学多样的数据集有助于提升泛化能力。该框架为复杂工况下的故障诊断提供稳健方案，并揭示了数据集特性对模型性能的影响。
原文摘要 · Abstract (English)
Fault classification in industrial machinery is vital for enhancing reliability and reducing downtime, yet it remains challenging due to the variability of vibration patterns across diverse operating conditions. This study introduces a novel graph-based framework for fault classification, converting time-series vibration data from machinery operating at varying horsepower levels into a graph representation. We utilize Shannon's entropy to determine the optimal window size for data segmentation, ensuring each segment captures significant temporal patterns, and employ Dynamic Time Warping (DTW) to define graph edges based on segment similarity. A Graph Auto Encoder (GAE) with a deep graph transformer encoder, decoder, and ensemble classifier is developed to learn latent graph representations and classify faults across various categories. The GAE's performance is evaluated on the Case Western Reserve University (CWRU) dataset, with cross-dataset generalization assessed on the HUST dataset. Results show that GAE achieves a mean F1-score of 0.99 on the CWRU dataset, significantly outperforming baseline models-CNN, LSTM, RNN, GRU, and Bi-LSTM (F1-scores: 0.94-0.97, p < 0.05, Wilcoxon signed-rank test for Bi-LSTM: p < 0.05) -- particularly in challenging classes (e.g., Class 8: 0.99 vs. 0.71 for Bi-LSTM). Visualization of dataset characteristics reveals that datasets with amplified vibration patterns and diverse fault dynamics enhance generalization. This framework provides a robust solution for fault diagnosis under varying conditions, offering insights into dataset impacts on model performance.
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