2026-09-28T11:48:20Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:27821 2026-09-28T06:35:12Z ddc:004 open_access

HSU TwinFlow: A Living Benchmark for Evaluating Anomaly Detection and Diagnosis Methods in Cyber-Physical Production Systems Using Digital-Twin-Generated Data Hranisavljevic, Nemanja Diedrich, Alexander Moddemann, Lukas Schaeffer, Domenic Marek, Frank Pill, Ingo Niggemann, Oliver Benchmark Digital Twin Manufacturing Production Systems Cyber-Physical Systems Artificial Intelligence Anomaly Detection Diagnosis System Reconfiguration We present a living (evolving) benchmark for evaluating data-driven methods for fault-related tasks, including fault (anomaly) detection and diagnosis, based on datasets generated by a realistic digital twin of a cyber-physical production system (CPPS). The digital twin is designed to capture the structural layout, operational behavior, and material flow of a modern production facility in a realistic manner. Scenario definitions, represented as sequences of high-level events, are used to generate labeled model-training and evaluation data from carefully designed fault scenarios. In addition, the datasets include contextual data, such as scenario metadata, product configurations, material-flow information, together with system knowledge such as the component hierarchy, component-type information, and material-flow graph. The proposed benchmark therefore provides a practical basis for method development and qualitative evaluation beyond simplified or isolated test setups. We provide initial benchmark results by evaluating two representative methods for each of the two considered tasks: anomaly detection and fault diagnosis. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Nemanja Hranisavljevic and Alexander Diedrich and Lukas Moddemann and Domenic Schaeffer and Frank Marek and Ingo Pill and Oliver Niggemann 2026 Is Part Of OASIcs, Volume 148, 37th International Conference on Principles of Diagnosis and Resilient Systems (DX 2026) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/OASIcs.DX.2026.7 urn:nbn:de:0030-drops-278212 https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2026.7 eng https://creativecommons.org/licenses/by/4.0/legalcode