Integrating LLMs for Explainable Fault Diagnosis in Complex Systems

TL;DR

This paper presents a novel integrated system combining physics-based diagnostics with large language models to improve explainability and operator understanding of faults in complex systems like nuclear plants.

Contribution

It introduces a new approach that merges model-based fault diagnosis with AI-driven explanations, enhancing transparency in complex system diagnostics.

Findings

Effective fault explanation in a molten salt facility

Improved operator understanding of sensor data and faults

Enhanced reliability and transparency of autonomous diagnostics

Abstract

This paper introduces an integrated system designed to enhance the explainability of fault diagnostics in complex systems, such as nuclear power plants, where operator understanding is critical for informed decision-making. By combining a physics-based diagnostic tool with a Large Language Model, we offer a novel solution that not only identifies faults but also provides clear, understandable explanations of their causes and implications. The system's efficacy is demonstrated through application to a molten salt facility, showcasing its ability to elucidate the connections between diagnosed faults and sensor data, answer operator queries, and evaluate historical sensor anomalies. Our approach underscores the importance of merging model-based diagnostics with advanced AI to improve the reliability and transparency of autonomous systems.