Topological Data Analysis for High-Dimensional Dynamic Process Monitoring
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...
| Authors | Angan Mukherjee, Tyler A. Soderstrom, Michael J. Kurtz et al. |
|---|---|
| Primary category | eess.SY |
| Published | 18 June 2026 |
| Last updated at source | 18 June 2026 |
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