Towards the generation of models for fault diagnosis of CPS using VQA models
Publication date
2024-03
Document type
Conference paper
Author
Organisational unit
Publisher
UB HSU
Book title
Machine learning for cyber physical systems
Peer-reviewed
✅
Part of the university bibliography
✅
Language
English
Keyword
Visual question
Large language model
Fault diagnosis
First-principles model
Application of LLMs
Abstract
In many use cases cyber-physical systems are employed to produce products of small batch sizes as efficiently as possible. From an engineering standpoint, a major drawback of this flexibility is that the architecture of the cyber-physical system may change multiple times over its lifetime to accommodate new product variants. To keep a cyber-physical system working normally it has become common to employ fault diagnosis algorithms. These algorithms partly rely on physical first-principles models that need to be updated when the architecture of the system changes which usually has to be done manually. In this article we present a practical approach to obtain such a first-principles model through evaluating piping and instrumentation diagrams (P&IDs) with visual questions answering (VQA) models. We demonstrate that it is possible to leverage VQA models to construct physical equations which are a preliminary stage for the creation of models suitable for fault diagnosis. We evaluate our approach on OpenAIs GPT-4 Vision Preview model using a P&ID we created for a benchmark water tank system. Our results show that VQA models can be used to create physical first-principles models.
Version
Published version
Access right on openHSU
Open access