Integrating LLMs for Explainable Fault Diagnosis in Complex Systems
Akshay J. Dave Tat Nghia Nguyen Richard B. Vilim
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.