In the domain of mechanical equipment maintenance, the necessity for efficient and accurate fault diagnosis is critical. Traditional Graph Neural Network (GNN) methods, which employ time-series data for fault diagnosis, have proven effective but are far from perfect. Their common pitfall lies in mapping time-series data into graph data, often leading to loss of crucial temporal information and computational inefficiencies. These limitations could result in suboptimal diagnosis, potentially compromising the longevity and performance of mechanical systems. Driven by the need to improve on these limitations, we introduce ACVGN, a novel end-to-end intelligent diagnostic framework that unites the strengths of the Adaptive Convergent Visibility Graph (ACVG) algorithm and an enhanced DiffPool model. The cornerstone of our approach, the ACVG algorithm, adeptly transforms time-series data into graph format, thereby preserving both local and global dynamics from the original data. This rich representation is then processed by our improved DiffPool model, a powerful GNN model purposefully designed for high-accuracy classification tasks. The effectiveness of the proposed ACVGN framework is substantiated by its performance on the widely-used rolling bearing dataset, where it outshines existing methods in terms of both mapping efficiency and fault diagnosis accuracy. These promising results not only reinforce the effectiveness of our proposed method but also highlight the potential for its wider applicability in other scenarios involving time-series data analysis and graph-based machine learning tasks. In conclusion, our study advances the development of more intelligent, efficient, and precise diagnostic tools for mechanical devices, ensuring more effective fault detection and diagnosis, and thereby potentially improving the lifespan and functionality of mechanical systems.

中文翻译：

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自适应收敛可见性图网络：一种可解释的智能滚动轴承诊断方法


在机械设备维护领域，高效、准确的故障诊断至关重要。传统的图神经网络（GNN）方法利用时间序列数据进行故障诊断，已被证明是有效的，但还远远不够完美。它们的常见缺陷在于将时间序列数据映射到图形数据，这通常会导致关键时间信息的丢失和计算效率低下。这些限制可能会导致诊断不理想，从而可能损害机械系统的寿命和性能。在改善这些限制的需求的推动下，我们引入了 ACVGN，这是一种新颖的端到端智能诊断框架，它结合了自适应收敛可见性图 (ACVG) 算法和增强型 DiffPool 模型的优势。我们方法的基石 ACVG 算法巧妙地将时间序列数据转换为图形格式，从而保留原始数据的局部和全局动态。然后，我们改进的 DiffPool 模型会处理这种丰富的表示，这是一个强大的 GNN 模型，专门为高精度分类任务而设计。所提出的 ACVGN 框架的有效性得到了其在广泛使用的滚动轴承数据集上的性能的证实，它在映射效率和故障诊断精度方面都优于现有方法。这些有希望的结果不仅增强了我们提出的方法的有效性，而且凸显了其在涉及时间序列数据分析和基于图的机器学习任务的其他场景中更广泛适用的潜力。 总之，我们的研究促进了机械设备更智能、更高效、更精确的诊断工具的开发，确保更有效的故障检测和诊断，从而有可能提高机械系统的使用寿命和功能。