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Documents authored by Diedrich, Alexander

Document
DOI: 10.4230/OASIcs.DX.2024.9
Inferring Sensor Placement Using Critical Pairs and Satisfiability Modulo Theory

Authors: Alexander Diedrich, René Heesch, Marco Bozzano, Björn Ludwig, Alessandro Cimatti, and Oliver Niggemann

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

Abstract
Industrial fault diagnosis exhibits the perennial problem of reasoning with partial and real-valued information. This is mainly due to the fact that in real-world applications, industrial systems are only instrumented insofar, as sensor information is required for their functioning. However, such instrumentation leaves out much information that would be useful for fault diagnosis. This is problematic since consistency-based fault diagnosis uses available information and computes intermediate values within a system description. These values are then used to compare expected normal behaviour to actual observed values. In the past, this was done only for Boolean circuits. Recently, satisfiability modulo non-linear arithmetic (SMT) formulations have been developed that allow the calculation of real values, instead of only Boolean ones. Leveraging those formulations, we in this article present a novel method to infer missing sensor values using an SMT system description and the notion of critical pairs. We show on a running example and also empirically that we can infer novel measurements for five process industrial systems. We conclude that, although SMT calculations accumulate some error, we can infer novel optimal measurements for all systems.
Cite as

Alexander Diedrich, René Heesch, Marco Bozzano, Björn Ludwig, Alessandro Cimatti, and Oliver Niggemann. Inferring Sensor Placement Using Critical Pairs and Satisfiability Modulo Theory. In 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024). Open Access Series in Informatics (OASIcs), Volume 125, pp. 9:1-9:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{diedrich_et_al:OASIcs.DX.2024.9,
  author =	{Diedrich, Alexander and Heesch, Ren\'{e} and Bozzano, Marco and Ludwig, Bj\"{o}rn and Cimatti, Alessandro and Niggemann, Oliver},
  title =	{{Inferring Sensor Placement Using Critical Pairs and Satisfiability Modulo Theory}},
  booktitle =	{35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)},
  pages =	{9:1--9:19},
  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.2024.9},
  URN =		{urn:nbn:de:0030-drops-221013},
  doi =		{10.4230/OASIcs.DX.2024.9},
  annote =	{Keywords: Sensor Placement, Satisfiability Modulo Theory, Critical Pairs, Diagnosability}
}
Document
Short Paper
DOI: 10.4230/OASIcs.DX.2024.31
Using Multi-Modal LLMs to Create Models for Fault Diagnosis (Short Paper)

Authors: Silke Merkelbach, Alexander Diedrich, Anna Sztyber-Betley, Louise Travé-Massuyès, Elodie Chanthery, Oliver Niggemann, and Roman Dumitrescu

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

Abstract
Creating models that are usable for fault diagnosis is hard. This is especially true for cyber-physical systems that are subject to architectural changes and may need to be adapted to different product variants intermittently. We therefore can no longer rely on expert-defined and static models for many systems. Instead, models need to be created more cheaply and need to adapt to different circumstances. In this article we present a novel approach to create physical models for process industry systems using multi-modal large language models (i.e ChatGPT). We present a five-step prompting approach that uses a piping and instrumentation diagram (P&ID) and natural language prompts as its input. We show that we are able to generate physical models of three systems of a well-known benchmark. We further show that we are able to diagnose faults for all of these systems by using the Fault Diagnosis Toolbox. We found that while multi-modal large language models (MLLMs) are a promising method for automated model creation, they have significant drawbacks.
Cite as

Silke Merkelbach, Alexander Diedrich, Anna Sztyber-Betley, Louise Travé-Massuyès, Elodie Chanthery, Oliver Niggemann, and Roman Dumitrescu. Using Multi-Modal LLMs to Create Models for Fault Diagnosis (Short Paper). In 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024). Open Access Series in Informatics (OASIcs), Volume 125, pp. 31:1-31:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{merkelbach_et_al:OASIcs.DX.2024.31,
  author =	{Merkelbach, Silke and Diedrich, Alexander and Sztyber-Betley, Anna and Trav\'{e}-Massuy\`{e}s, Louise and Chanthery, Elodie and Niggemann, Oliver and Dumitrescu, Roman},
  title =	{{Using Multi-Modal LLMs to Create Models for Fault Diagnosis}},
  booktitle =	{35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)},
  pages =	{31:1--31:15},
  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.2024.31},
  URN =		{urn:nbn:de:0030-drops-221236},
  doi =		{10.4230/OASIcs.DX.2024.31},
  annote =	{Keywords: Fault Diagnosis, Large Language Models, LLMs, Physical Modelling, Process Industry, P\&IDs}
}
Document
Extended Abstract
DOI: 10.4230/OASIcs.DX.2024.32
Summary of "A Lazy Approach to Neural Numerical Planning with Control Parameters" (Extended Abstract)

Authors: René Heesch, Alessandro Cimatti, Jonas Ehrhardt, Alexander Diedrich, and Oliver Niggemann

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

Abstract
This is an extended abstract of the manuscript "A Lazy Approach to Neural Numerical Planning with Control Parameters" [René Heesch et al., 2024]. The paper presents a lazy, hierarchical approach to tackle the challenge of planning in complex numerical domains, where the effects of actions are influenced by control parameters, and may be described by neural networks.
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René Heesch, Alessandro Cimatti, Jonas Ehrhardt, Alexander Diedrich, and Oliver Niggemann. Summary of "A Lazy Approach to Neural Numerical Planning with Control Parameters" (Extended Abstract). In 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024). Open Access Series in Informatics (OASIcs), Volume 125, pp. 32:1-32:3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{heesch_et_al:OASIcs.DX.2024.32,
  author =	{Heesch, Ren\'{e} and Cimatti, Alessandro and Ehrhardt, Jonas and Diedrich, Alexander and Niggemann, Oliver},
  title =	{{Summary of "A Lazy Approach to Neural Numerical Planning with Control Parameters"}},
  booktitle =	{35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)},
  pages =	{32:1--32:3},
  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.2024.32},
  URN =		{urn:nbn:de:0030-drops-221243},
  doi =		{10.4230/OASIcs.DX.2024.32},
  annote =	{Keywords: Satisfiability Modulo Theory, Neural Numerical Planning with Control Parameters, Neural Networks}
}
Document
DOI: 10.4230/DagRep.14.1.25
Fusing Causality, Reasoning, and Learning for Fault Management and Diagnosis (Dagstuhl Seminar 24031)

Authors: Alessandro Cimatti, Ingo Pill, and Alexander Diedrich

Published in: Dagstuhl Reports, Volume 14, Issue 1 (2024)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar "Fusing Causality, Reasoning, and Learning for Fault Management and Diagnosis" (24031). The goal of this Dagstuhl Seminar was to provide an interdisciplinary forum to discuss the fundamental principles of fault management and diagnosis, bringing together international researchers and practitioners from the fields of symbolic reasoning, machine learning, and control engineering.
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Alessandro Cimatti, Ingo Pill, and Alexander Diedrich. Fusing Causality, Reasoning, and Learning for Fault Management and Diagnosis (Dagstuhl Seminar 24031). In Dagstuhl Reports, Volume 14, Issue 1, pp. 25-48, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@Article{cimatti_et_al:DagRep.14.1.25,
  author =	{Cimatti, Alessandro and Pill, Ingo and Diedrich, Alexander},
  title =	{{Fusing Causality, Reasoning, and Learning for Fault Management and Diagnosis (Dagstuhl Seminar 24031)}},
  pages =	{25--48},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2024},
  volume =	{14},
  number =	{1},
  editor =	{Cimatti, Alessandro and Pill, Ingo and Diedrich, Alexander},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.14.1.25},
  URN =		{urn:nbn:de:0030-drops-204899},
  doi =		{10.4230/DagRep.14.1.25},
  annote =	{Keywords: cyber-physical systems, diagnosis, fault detection and management, integrative ai, model-based reasoning}
}
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