Search Results

Documents authored by Travé-Massuyès, Louise

Document
DOI: 10.4230/OASIcs.DX.2025.2
Are Diagnostic Concepts Within the Reach of LLMs?

Authors: Anna Sztyber-Betley, Elodie Chanthery, Louise Travé-Massuyès, Silke Merkelbach, Karol Kukla, Maxence Glotin, Alexander Diedrich, and Oliver Niggemann

Published in: OASIcs, Volume 136, 36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025)

Abstract
Model-based diagnosis is a cornerstone of system health monitoring, allowing for the identification of faulty components based on observed behavior and a formal system model. However, obtaining a useful and reliable model is often an expensive and manual task. While the generation of a formal model was the aim of previous work, in this paper, we propose a methodology to use large language models to generate Minimal Structurally Overdetermined sets (MSOs). MSOs are specific subsets of the model equations from which diagnosis tests can be obtained. We investigate two different directions: (i) the large-language-models' ability to generate MSO sets for hybrid systems, similar to those generated by the well-known Fault Diagnosis Toolbox (FDT) (ii) the automated generation of MSOs for Boolean circuits, as well as the computation of the diagnoses. We thus show how both dynamic and static systems can be analysed by large-language models and how their output can be used for effective fault diagnosis. We evaluate our approach on a set of arithmetic and logic circuits, using OpenAI’s LLMs 4o-mini, o1, and o3-mini.
Cite as

Anna Sztyber-Betley, Elodie Chanthery, Louise Travé-Massuyès, Silke Merkelbach, Karol Kukla, Maxence Glotin, Alexander Diedrich, and Oliver Niggemann. Are Diagnostic Concepts Within the Reach of LLMs?. In 36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025). Open Access Series in Informatics (OASIcs), Volume 136, pp. 2:1-2:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{sztyberbetley_et_al:OASIcs.DX.2025.2,
  author =	{Sztyber-Betley, Anna and Chanthery, Elodie and Trav\'{e}-Massuy\`{e}s, Louise and Merkelbach, Silke and Kukla, Karol and Glotin, Maxence and Diedrich, Alexander and Niggemann, Oliver},
  title =	{{Are Diagnostic Concepts Within the Reach of LLMs?}},
  booktitle =	{36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025)},
  pages =	{2:1--2:20},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-394-2},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{136},
  editor =	{Quinones-Grueiro, Marcos and Biswas, Gautam and Pill, Ingo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2025.2},
  URN =		{urn:nbn:de:0030-drops-247913},
  doi =		{10.4230/OASIcs.DX.2025.2},
  annote =	{Keywords: Fault Diagnosis, Large Language Models, LLMs, Model Based Diagnosis, MSO, Redundancy Relations, Conflicts, Diagnoses}
}
Document
DOI: 10.4230/OASIcs.DX.2025.7
One-Shot Learning in Hybrid System Identification: A New Modular Paradigm

Authors: Swantje Plambeck, Maximilian Schmidt, Louise Travé-Massuyès, and Goerschwin Fey

Published in: OASIcs, Volume 136, 36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025)

Abstract
Identification of hybrid systems requires learning models that capture both discrete transitions and continuous dynamics from observational data. Traditional approaches follow a stepwise process, separating trace segmentation and mode-specific regression, which often leads to inconsistencies due to unmodeled interdependencies. In this paper, we propose a new iterative learning paradigm that jointly optimizes segmentation and flow function identification. The method incrementally constructs a hybrid model by evaluating and expanding candidate flow functions over observed traces, introducing new modes only when existing ones fail to explain the data. The approach is modular and agnostic to the choice of the regression technique, allowing the identification of hybrid systems with varying levels of complexity. Empirical results on benchmark examples demonstrate that the proposed method produces more compact models compared to traditional techniques, while supporting flexible integration of different regression methods. By favoring fewer, more generalizable modes, the resulting models are not only likely to reduce complexity but also simplify diagnostic reasoning, improve fault isolation, and enhance robustness by avoiding overfitting to spurious mode changes.
Cite as

Swantje Plambeck, Maximilian Schmidt, Louise Travé-Massuyès, and Goerschwin Fey. One-Shot Learning in Hybrid System Identification: A New Modular Paradigm. In 36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025). Open Access Series in Informatics (OASIcs), Volume 136, pp. 7:1-7:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{plambeck_et_al:OASIcs.DX.2025.7,
  author =	{Plambeck, Swantje and Schmidt, Maximilian and Trav\'{e}-Massuy\`{e}s, Louise and Fey, Goerschwin},
  title =	{{One-Shot Learning in Hybrid System Identification: A New Modular Paradigm}},
  booktitle =	{36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025)},
  pages =	{7:1--7:18},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-394-2},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{136},
  editor =	{Quinones-Grueiro, Marcos and Biswas, Gautam and Pill, Ingo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2025.7},
  URN =		{urn:nbn:de:0030-drops-247969},
  doi =		{10.4230/OASIcs.DX.2025.7},
  annote =	{Keywords: Hybrid System, Model Learning, Symbolic Regression}
}
Document
PhD Panel
DOI: 10.4230/OASIcs.DX.2025.16
Unsupervised Multimodal Learning for Fault Diagnosis and Prognosis - Application to Radiotherapy Systems (PhD Panel)

Authors: Kélian Poujade, Louise Travé-Massuyès, Jérémy Pirard, and Laure Vieillevigne

Published in: OASIcs, Volume 136, 36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025)

Abstract
Modern complex systems, such as radiotherapy machines, require robust strategies for fault detection, diagnosis, and prognosis to ensure operational continuity and patient safety. While data-driven methods have gained traction, few studies address diagnostic and prognostic tasks using multimodal operational data under unsupervised or semi-supervised learning settings. This gap is particularly critical given the scarcity of labeled failure data in real-world environments. This work aims to design a unified approach for fault detection, diagnosis, and prognosis using multimodal data in the absence of complete labeling. To this end, autoencoders (AEs) are employed due to their suitability for unsupervised and self-supervised learning, flexibility in handling heterogeneous data, and ability to construct latent representations optimized for various downstream tasks. A specific implementation based on a Long Short-Term Memory β-Variational Autoencoder (LSTM-β-VAE) was developed to detect anomalies in machine logs. This framework is applied to TomoTherapy® systems - a highly complex and under-explored use case within the radiotherapy domain. Initial results demonstrate strong anomaly detection performance on both a public benchmark dataset (HDFS) and a proprietary dataset derived from real-world TomoTherapy® machine faults. Beyond methodology, the paper includes a concise literature review of multimodal learning and data-driven diagnosis and prognosis with a focus on AEs. Based on this review, key research directions are identified for the continuation of the thesis, especially the integration of explainable AI as a means to enhance diagnosis capabilities in the absence of labeled faults.
Cite as

Kélian Poujade, Louise Travé-Massuyès, Jérémy Pirard, and Laure Vieillevigne. Unsupervised Multimodal Learning for Fault Diagnosis and Prognosis - Application to Radiotherapy Systems (PhD Panel). In 36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025). Open Access Series in Informatics (OASIcs), Volume 136, pp. 16:1-16:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{poujade_et_al:OASIcs.DX.2025.16,
  author =	{Poujade, K\'{e}lian and Trav\'{e}-Massuy\`{e}s, Louise and Pirard, J\'{e}r\'{e}my and Vieillevigne, Laure},
  title =	{{Unsupervised Multimodal Learning for Fault Diagnosis and Prognosis - Application to Radiotherapy Systems}},
  booktitle =	{36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025)},
  pages =	{16:1--16:17},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-394-2},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{136},
  editor =	{Quinones-Grueiro, Marcos and Biswas, Gautam and Pill, Ingo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2025.16},
  URN =		{urn:nbn:de:0030-drops-248058},
  doi =		{10.4230/OASIcs.DX.2025.16},
  annote =	{Keywords: Artificial Intelligence, Diagnosis, Prognosis, Radiotherapy machines}
}
Document
DOI: 10.4230/OASIcs.DX.2024.1
A Hierarchical Monitoring and Diagnosis System for Autonomous Robots

Authors: Gerald Steinbauer-Wagner, Leo Fürbaß, Marco De Bortoli, and Louise Travé-Massuyès

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

Abstract
This paper addresses the capability of autonomous robots to achieve flexible goals in dynamic environments. In such a setting numerous challenges jeopardize the robustness of such systems. Thus, we propose a hierarchical diagnosis concept for layered control architectures, that can detect and deal with such challenges to maintain a consistent knowledge about the world and to allow reliable decision-making. Layered control systems use various knowledge representations and decision-making mechanisms teamed with specialized isolated fault-handling approaches. However, some issues can only be identified if the information from different layers is combined. Our approach addresses challenges like failing actions, uncertain observations, and unmodeled events by propagating observations and diagnoses results throughout the hierarchy. This enhances adaptability and dependability in various domains. In this paper, we present a prototype architecture following this approach.
Cite as

Gerald Steinbauer-Wagner, Leo Fürbaß, Marco De Bortoli, and Louise Travé-Massuyès. A Hierarchical Monitoring and Diagnosis System for Autonomous Robots. In 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024). Open Access Series in Informatics (OASIcs), Volume 125, pp. 1:1-1:9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{steinbauerwagner_et_al:OASIcs.DX.2024.1,
  author =	{Steinbauer-Wagner, Gerald and F\"{u}rba{\ss}, Leo and De Bortoli, Marco and Trav\'{e}-Massuy\`{e}s, Louise},
  title =	{{A Hierarchical Monitoring and Diagnosis System for Autonomous Robots}},
  booktitle =	{35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)},
  pages =	{1:1--1:9},
  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.2024.1},
  URN =		{urn:nbn:de:0030-drops-220938},
  doi =		{10.4230/OASIcs.DX.2024.1},
  annote =	{Keywords: Cognitive Architecture, Autonomous Agents, Dependability, Hierarchical Monitoring and Diagnosis}
}
Document
DOI: 10.4230/OASIcs.DX.2024.3
A Review of Fault Diagnosis Techniques Applied to Aircraft Air Data Sensors

Authors: Lucas Lima Lopes, Louise Travé-Massuyès, Carine Jauberthie, and Guillaume Alcalay

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

Abstract
Air data sensors provide essential measurements to ensure the availability of autopilot and to maintain aircraft performance, flight envelope protection and optimal aerodynamic surfaces control laws. The importance of these sensors imply the existence of embedded fault tolerance features, mainly represented by hardware redundancy. The latter is prone to fail in case of common fault of multiple sensors, especially if the faults are coherent and simultaneous. Increasing the robustness of fault detection and isolation (FDI) techniques for air data sensors to the aforementioned conditions is essential for the development of more autonomous aircraft, reducing crew workload and guaranteeing flight protections under adverse conditions. This paper reviews recent works on Air Data System (ADS) FDI, assessing proposed model, data and signal-driven approaches. We finally argue in favor of data-driven and hybrid approaches for the development of virtual sensors and semi-supervised anomaly detectors, offering an overview of ways forward.
Cite as

Lucas Lima Lopes, Louise Travé-Massuyès, Carine Jauberthie, and Guillaume Alcalay. A Review of Fault Diagnosis Techniques Applied to Aircraft Air Data Sensors. In 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024). Open Access Series in Informatics (OASIcs), Volume 125, pp. 3:1-3:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{limalopes_et_al:OASIcs.DX.2024.3,
  author =	{Lima Lopes, Lucas and Trav\'{e}-Massuy\`{e}s, Louise and Jauberthie, Carine and Alcalay, Guillaume},
  title =	{{A Review of Fault Diagnosis Techniques Applied to Aircraft Air Data Sensors}},
  booktitle =	{35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)},
  pages =	{3:1--3:20},
  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.2024.3},
  URN =		{urn:nbn:de:0030-drops-220957},
  doi =		{10.4230/OASIcs.DX.2024.3},
  annote =	{Keywords: air data, FDI, aeronautics, review, survey, diagnostics, fault}
}
Document
DOI: 10.4230/OASIcs.DX.2024.5
Bridging Hardware and Software Diagnosis: Leveraging Fault Signature Matrix and Spectrum-Based Fault Localization Similarities

Authors: Louise Travé-Massuyès and Franz Wotawa

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

Abstract
This paper examines two prominent Fault Detection and Isolation methodologies: the Signature Matrix approach, traditionally used in hardware systems, and the Spectrum-based approach, applied in software fault localization. Despite their distinct operational domains, both methods share the objective of precisely identifying and isolating faults. This study aims to compare these approaches and to highlight their similarities in principle. Through a comparative analysis, we assess how the structured pattern recognition of the Signature Matrix method and the statistical analysis capabilities of the Spectrum-based approach can be synergized to enhance diagnostic processes of cyber-physical systems that are composed of both hardware and software components. The investigation is motivated by the prospect of developing a hybrid Fault Detection and Isolation strategy that incorporates the robust detection mechanisms of hardware diagnostics with the techniques used in software fault localization. The findings are intended to advance the theoretical framework of Fault Detection and Isolation systems and suggest practical implementations across varied technological platforms, thereby improving the reliability and efficiency of fault detection and isolation in both hardware and software contexts.
Cite as

Louise Travé-Massuyès and Franz Wotawa. Bridging Hardware and Software Diagnosis: Leveraging Fault Signature Matrix and Spectrum-Based Fault Localization Similarities. In 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024). Open Access Series in Informatics (OASIcs), Volume 125, pp. 5:1-5:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{travemassuyes_et_al:OASIcs.DX.2024.5,
  author =	{Trav\'{e}-Massuy\`{e}s, Louise and Wotawa, Franz},
  title =	{{Bridging Hardware and Software Diagnosis: Leveraging Fault Signature Matrix and Spectrum-Based Fault Localization Similarities}},
  booktitle =	{35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)},
  pages =	{5:1--5:15},
  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.2024.5},
  URN =		{urn:nbn:de:0030-drops-220972},
  doi =		{10.4230/OASIcs.DX.2024.5},
  annote =	{Keywords: Diagnosis, Fault detection and identification, Software debugging}
}
Document
DOI: 10.4230/OASIcs.DX.2024.13
MSO Sets and MTES for Dummies

Authors: Maxence Glotin, Louise Travé-Massuyès, and Elodie Chanthery

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

Abstract
Structural analysis-based diagnosis allows for the extraction of a wealth of information and properties by studying a structural model that represents a physical system. This diagnosis approach is centered on structurally overdetermined sets, which enable the generation of residuals for fault detection and isolation. As the 'for Dummies' editorial collection, this article aims at taking on complex concepts and making them easy to understand. It aims to clarify and compare key concepts in structural analysis, focusing on Minimally Structurally Overdetermined (MSO) sets and Minimal Test Equation Supports (MTES). Additionally, we explain and illustrate the Dulmage-Mendelsohn decomposition, which helps identify structurally overdetermined parts of the system and plays a important role in the structural analysis process. Through detailed exploration and practical examples, we demonstrate the roles, applications, and interrelations of these sets, highlighting their respective strengths and limitations. The paper provides an overview of the algorithms used to identify and use these sets, including a theoretical and practical comparison of their computational efficiency and diagnostic capabilities.
Cite as

Maxence Glotin, Louise Travé-Massuyès, and Elodie Chanthery. MSO Sets and MTES for Dummies. In 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024). Open Access Series in Informatics (OASIcs), Volume 125, pp. 13:1-13:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{glotin_et_al:OASIcs.DX.2024.13,
  author =	{Glotin, Maxence and Trav\'{e}-Massuy\`{e}s, Louise and Chanthery, Elodie},
  title =	{{MSO Sets and MTES for Dummies}},
  booktitle =	{35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)},
  pages =	{13:1--13:15},
  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.2024.13},
  URN =		{urn:nbn:de:0030-drops-221054},
  doi =		{10.4230/OASIcs.DX.2024.13},
  annote =	{Keywords: Structural analysis, MTES, MSO sets}
}
Document
Short Paper
DOI: 10.4230/OASIcs.DX.2024.30
Usability of Symbolic Regression for Hybrid System Identification - System Classes and Parameters (Short Paper)

Authors: Swantje Plambeck, Maximilian Schmidt, Audine Subias, Louise Travé-Massuyès, and Goerschwin Fey

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

Abstract
Hybrid systems, which combine both continuous and discrete behavior, are used in many fields, including robotics, biological systems, and control systems. However, due to their complexity, finding an accurate model is a challenge. This paper discusses the usage of symbolic regression to learn hybrid systems from data and specifically analyses learning parameters for a recent algorithm. Symbolic regression is a powerful tool that can automatically discover accurate and interpretable mathematical models in the form of symbolic expressions. Models generated by symbolic regression are a valuable tool for system identification and diagnosis, e.g., to predict future system behavior or detect anomalies. A major opportunity of our approach is the ability to detect transitions between different continuous behaviors of a system directly based on the dynamics. From a diagnosis perspective, this can advantageously be used to detect the system entering fault modes and identify their models. This paper presents a parameter study for a symbolic regression based identification algorithm.
Cite as

Swantje Plambeck, Maximilian Schmidt, Audine Subias, Louise Travé-Massuyès, and Goerschwin Fey. Usability of Symbolic Regression for Hybrid System Identification - System Classes and Parameters (Short Paper). In 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024). Open Access Series in Informatics (OASIcs), Volume 125, pp. 30:1-30:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{plambeck_et_al:OASIcs.DX.2024.30,
  author =	{Plambeck, Swantje and Schmidt, Maximilian and Subias, Audine and Trav\'{e}-Massuy\`{e}s, Louise and Fey, Goerschwin},
  title =	{{Usability of Symbolic Regression for Hybrid System Identification - System Classes and Parameters}},
  booktitle =	{35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)},
  pages =	{30:1--30:14},
  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.2024.30},
  URN =		{urn:nbn:de:0030-drops-221223},
  doi =		{10.4230/OASIcs.DX.2024.30},
  annote =	{Keywords: Hybrid Systems, Symbolic Regression, System Identification}
}
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}
}
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