用拓扑分析与神经ODE监测工业时序数据中的异常事件
Topological Data Analysis for High-Dimensional Dynamic Process Monitoring
- 将多变量时序数据建模为流形，用拓扑特征描述其结构
- 通过神经ODE学习拓扑结构的动态演化，精准检测多种事件
- 相比传统方法，对复杂非线性过程更具优势，适合工业监控
实时过程监控需要从高维时序数据中提取可操作信息。本文提出一种结合拓扑数据分析（TDA）与机器学习的新方法：将多变量时序数据表示为流形，并使用拓扑描述子总结其结构；随后利用神经微分方程学习系统拓扑结构的动态演化。基于工业过程的真实数据，实验表明该基于轨迹的事件检测方法能有效识别多种类型事件。与基于重构的方法（如主成分分析、自编码器）及基于轨迹的科普曼自编码器方法对比，本方法在捕捉复杂动态行为方面表现更优。
原文摘要 · Abstract (English)
Real-time process monitoring requires methods that extract actionable information from high-dimensional time-series data. In this work, we present a new approach for process monitoring that combines tools of topological data analysis (TDA) and machine learning. In the proposed approach, we represent multivariate time-series data as manifolds and use topological descriptors to summarize the structure of such data; we then use a neural ordinary differential equation to learn the dynamic evolution of the topological structure of the system. Using real data from an industrial process, we show that this trajectory-based event detection approach is effective at detecting diverse types of events. We contrast this approach against reconstruction-based approaches such as principal component analysis and autoencoders and against a trajectory-based approach that uses Koopman autoencoders.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。