Integrating LLMs for Explainable Fault Diagnosis in Complex Systems
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866916121164120064 |
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| author | Dave, Akshay J. Nguyen, Tat Nghia Vilim, Richard B. |
| author_facet | Dave, Akshay J. Nguyen, Tat Nghia Vilim, Richard B. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_06695 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Integrating LLMs for Explainable Fault Diagnosis in Complex Systems Dave, Akshay J. Nguyen, Tat Nghia Vilim, Richard B. Artificial Intelligence Machine Learning Systems and Control 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. |
| title | Integrating LLMs for Explainable Fault Diagnosis in Complex Systems |
| topic | Artificial Intelligence Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2402.06695 |