In the Industrial Internet of Things (IIoT), condition monitoring sensor signals from complex systems often exhibit nonlinear and stochastic spatial-temporal dynamics under varying conditions. These complex dynamics make fault detection particularly challenging. While previous methods effectively model these dynamics, they often neglect the evolution of relationships between sensor signals. Undetected shifts in these relationships can lead to significant system failures. Furthermore, these methods frequently misidentify novel operating conditions as faults. Addressing these limitations, we propose DyEdgeGAT (Dynamic Edge via Graph Attention), a novel approach for early-stage fault detection in IIoT systems. DyEdgeGAT's primary innovation lies in a novel graph inference scheme for multivariate time series that tracks the evolution of relationships between time series, enabled by dynamic edge construction. Another key innovation of DyEdgeGAT is its ability to incorporate operating condition contexts into node dynamics modeling, enhancing its accuracy and robustness. We rigorously evaluated DyEdgeGAT using both a synthetic dataset, simulating varying levels of fault severity, and a real-world industrial-scale multiphase flow facility benchmark with diverse fault types under varying operating conditions and detection complexities. The results show that DyEdgeGAT significantly outperforms other baseline methods in fault detection, particularly in the early stages with low severity, and exhibits robust performance under novel operating conditions.

翻译：在工业物联网（IIoT）中，来自复杂系统的状态监测传感器信号通常表现出非线性、随机性的时空动态特性，且随工况变化而演变。这些复杂动态特性使得故障检测极具挑战性。现有方法虽能有效建模此类动态特性，却往往忽视传感器信号间关系的演变过程。此类关系中的未检测偏移可能导致重大系统故障。此外，这些方法常将新型工况误判为故障。针对上述局限性，我们提出DyEdgeGAT（动态边图注意力机制），一种面向IIoT系统早期故障检测的新方法。DyEdgeGAT的核心创新在于提出了一种新型多元时间序列图推理方案，通过动态边构建追踪时间序列间关系的演变过程。另一项关键创新是其将工况上下文融入节点动力学建模的能力，从而增强了检测精度与鲁棒性。我们采用模拟不同故障严重程度的合成数据集，以及包含多种工况、故障类型及检测复杂度的真实工业规模多相流设施基准数据集，对DyEdgeGAT进行了严格评估。结果表明，DyEdgeGAT在故障检测方面显著优于其他基线方法，尤其在低严重程度的早期阶段表现突出，并在新型工况下展现出稳健的检测性能。