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DOI: 10.4230/OASIcs.DX.2026.7
HSU TwinFlow: A Living Benchmark for Evaluating Anomaly Detection and Diagnosis Methods in Cyber-Physical Production Systems Using Digital-Twin-Generated Data

Authors: Nemanja Hranisavljevic, Alexander Diedrich, Lukas Moddemann, Domenic Schaeffer, Frank Marek, Ingo Pill, and Oliver Niggemann

Published in: OASIcs, Volume 148, 37th International Conference on Principles of Diagnosis and Resilient Systems (DX 2026)

Abstract
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.
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Nemanja Hranisavljevic, Alexander Diedrich, Lukas Moddemann, Domenic Schaeffer, Frank Marek, Ingo Pill, and Oliver Niggemann. HSU TwinFlow: A Living Benchmark for Evaluating Anomaly Detection and Diagnosis Methods in Cyber-Physical Production Systems Using Digital-Twin-Generated Data. In 37th International Conference on Principles of Diagnosis and Resilient Systems (DX 2026). Open Access Series in Informatics (OASIcs), Volume 148, pp. 7:1-7:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{hranisavljevic_et_al:OASIcs.DX.2026.7,
  author =	{Hranisavljevic, Nemanja and Diedrich, Alexander and Moddemann, Lukas and Schaeffer, Domenic and Marek, Frank and Pill, Ingo and Niggemann, Oliver},
  title =	{{HSU TwinFlow: A Living Benchmark for Evaluating Anomaly Detection and Diagnosis Methods in Cyber-Physical Production Systems Using Digital-Twin-Generated Data}},
  booktitle =	{37th International Conference on Principles of Diagnosis and Resilient Systems (DX 2026)},
  pages =	{7:1--7:14},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-455-0},
  ISSN =	{2190-6807},
  year =	{2026},
  volume =	{148},
  editor =	{Pill, Ingo and Zanella, Marina and Provan, Gregory},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2026.7},
  URN =		{urn:nbn:de:0030-drops-278212},
  doi =		{10.4230/OASIcs.DX.2026.7},
  annote =	{Keywords: Benchmark, Digital Twin, Manufacturing, Production Systems, Cyber-Physical Systems, Artificial Intelligence, Anomaly Detection, Diagnosis, System Reconfiguration}
}

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