用于多阶段工业过程中多变量时间序列异常检测的知识辅助多图依赖学习
Knowledge-Assisted Multi-Graph Dependency Learning for Multivariate Time Series Anomaly Detection in Multi-Stage Industrial Processes
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- Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）
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中文总结 AI 辅助
针对多阶段工业过程中多变量时间序列异常检测问题，提出知识辅助多图框架并构建三个互补图，利用多图注意力网络建模，通过纳入过程知识显著提升异常检测性能。
中文摘要 AI 辅助
工业过程常常从多个阶段的多个传感器生成复杂且相互依赖的时间序列数据，变量和过程阶段之间形成复杂依赖关系。通过多变量时间序列异常检测（MTAD）对这些时间序列进行有效监测和及时异常检测，对于防止故障和确保自动化系统可靠性至关重要。图神经网络（GNN）通过利用数据驱动的图来建模变量间复杂依赖关系，推动了MTAD发展，但现有基于GNN的方法常忽略关键过程知识，即便考虑该知识，将其无缝融入现有模型也颇具挑战，导致性能欠佳。为解决此局限，我们提出一种知识辅助多图框架，用于多阶段工业过程中MTAD的传感器依赖建模，将过程知识明确纳入图学习以增强依赖建模并提升异常检测性能。我们的方法构建三个互补图：一个纯数据驱动图和两个通过整合过程知识导出的结构约束进行细化的图。为有效利用这些图进行异常检测，我们采用多图注意力网络，实现对复杂依赖关系更准确、稳健的表示。在两个真实世界的多阶段工业数据集上的综合实验表明，纳入过程知识可显著提升异常检测性能。
英文摘要
Industrial processes often generate complex, interdependent time-series data from multiple sensors across multiple stages, forming complex dependencies among variables and process stages. Effective monitoring and timely anomaly detection of these time series through multivariate time series anomaly detection (MTAD) is crucial for preventing failures and ensuring the reliability of automated systems. Graph neural networks (GNNs) have advanced MTAD by leveraging data-driven graphs to model complex dependencies among variables, effectively capturing relational structures within multivariate time series to enhance anomaly detection performance. However, existing GNN-based approaches often overlook critical process knowledge, and even when this knowledge is considered, seamlessly incorporating it into existing models remains inherently challenging, leading to suboptimal performance. To address this limitation, we propose a knowledge-assisted multi-graph framework for modeling sensor dependencies in multi-stage industrial processes for MTAD, which explicitly incorporates process knowledge into graph learning to enhance dependency modeling and improve anomaly detection performance. Our method constructs three complementary graphs: one purely data-driven and two refined by integrating structural constraints derived from process knowledge. To effectively leverage these graphs for anomaly detection, we employ a multi-graph attention network, enabling a more accurate and robust representation of complex dependencies. Comprehensive experiments on two real-world, multi-stage industrial datasets demonstrate that incorporating process knowledge substantially enhances anomaly detection performance.
