Feb. 13, 2024, 5:43 a.m. | Akshay J. Dave Tat Nghia Nguyen Richard B. Vilim

cs.LG updates on arXiv.org arxiv.org

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 …

complex systems cs.ai cs.lg cs.sy decision diagnosis diagnostic diagnostics eess.sy explainability language language model large language large language model llms making novel nuclear nuclear power paper physics plants power solution systems tool understanding

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