Using multi-modal LLMs to create models for fault diagnosis
Publication date
2024-11-26
Document type
Konferenzbeitrag
Author
Merkelbach, Silke
Sztyber-Betley, Anna
Travé-Massuyès, Louise
Chanthery, Elodie
Dumitrescu, Roman
Organisational unit
Conference
35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024) ; Vienna, Austria ; November 4–7, 2024
Publisher
Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH
Series or journal
Open Access Series in Informatics (OASIcs)
ISSN
Periodical volume
125
Book title
35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)
First page
31:1
Last page
31:15
Peer-reviewed
✅
Part of the university bibliography
✅
Language
English
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.
Description
This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
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) https://doi.org/10.4230/OASIcs.DX.2024.31
Version
Published version
Access right on openHSU
Metadata only access