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Documents authored by Moddemann, Lukas

Document
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}
}
Document
DOI: 10.4230/OASIcs.DX.2024.7
Design Principles for Falsifiable, Replicable and Reproducible Empirical Machine Learning Research

Authors: Daniel Vranješ, Jonas Ehrhardt, René Heesch, Lukas Moddemann, Henrik Sebastian Steude, and Oliver Niggemann

Published in: OASIcs, Volume 125, 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)

Abstract
Machine learning is becoming increasingly important in the diagnosis and planning fields, where data-driven models and algorithms are being employed as alternatives to traditional first-principle approaches. Empirical research plays a fundamental role in the machine learning domain. At the heart of impactful empirical research lies the development of clear research hypotheses, which then shape the design of experiments. The execution of experiments must be carried out with precision to ensure reliable results, followed by statistical analysis to interpret these outcomes. This process is key to either supporting or refuting initial hypotheses. Despite its importance, there is a high variability in research practices across the machine learning community and no uniform understanding of quality criteria for empirical research. To address this gap, we propose a model for the empirical research process, accompanied by guidelines to uphold the validity of empirical research. By embracing these recommendations, greater consistency, enhanced reliability and increased impact can be achieved.
Cite as

Daniel Vranješ, Jonas Ehrhardt, René Heesch, Lukas Moddemann, Henrik Sebastian Steude, and Oliver Niggemann. Design Principles for Falsifiable, Replicable and Reproducible Empirical Machine Learning Research. In 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024). Open Access Series in Informatics (OASIcs), Volume 125, pp. 7:1-7:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{vranjes_et_al:OASIcs.DX.2024.7,
  author =	{Vranje\v{s}, Daniel and Ehrhardt, Jonas and Heesch, Ren\'{e} and Moddemann, Lukas and Steude, Henrik Sebastian and Niggemann, Oliver},
  title =	{{Design Principles for Falsifiable, Replicable and Reproducible Empirical Machine Learning Research}},
  booktitle =	{35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)},
  pages =	{7:1--7:13},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-356-0},
  ISSN =	{2190-6807},
  year =	{2024},
  volume =	{125},
  editor =	{Pill, Ingo and Natan, Avraham and Wotawa, Franz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2024.7},
  URN =		{urn:nbn:de:0030-drops-220991},
  doi =		{10.4230/OASIcs.DX.2024.7},
  annote =	{Keywords: machine learning, hypothesis design, research design, experimental research, statistical testing, diagnosis, planning}
}

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