COVER STORY | LLMS & DIAGNOSTICS fault diagnosis LLMs and
The use of AI in complex nuclear systems offers substantial benefits, but must do more than identify a potential fault. To be trusted the AI has to explain how it came to that conclusion.
THE OPERATION OF COMPLEX SYSTEMS such as nuclear power plants has necessitated advanced diagnostic capabilities, particularly as part of a broader autonomous operation framework. However, while the need for diagnostics is driven by the need to achieve cost efficiency and enhanced responses, a diagnostic tool’s effectiveness largely depends on the ability of operators to understand and trust the information presented. In safety-critical environments like nuclear power plants, where operators must make informed decisions, the ability to understand and trust the diagnostic information is of paramount importance. It is not sufficient to be told that something is wrong, rather it is crucial to understand why and how it is wrong to make the most effective corrective actions. A paper by Akshay J. Dave, Tat Nghia Nguyen, and Richard B. Vilim of the Nuclear Science and Engineering Division of Argonne National Laboratory, explores this theme in a project supported by the US Department of Energy, Office of Nuclear Energy. Their analysis concludes that to address this problem two requirements must be met. Firstly, is the provision of a diagnostics capability and secondly, the use
of a computational agent that can provide explanations about those diagnoses that emerge. Their paper, titled ‘Integrating LLMs for Explainable Fault Diagnosis in Complex Systems’ presents a system incorporating both aspects for explainable fault diagnostics. While the concept of explainability lacks a universally
agreed-upon definition, their work employs a physics- based diagnostic model to derive the causal relationships between potential faults and fault symptoms. This approach, the authors say, allows precise rationales for any offered diagnoses.
Creating a diagnostic framework One of the diagnostic framework’s challenges is constructing a physics-based model of the target system. At the component level, approximations of the physics, including the mass, momentum, and energy conservation equations, are utilised to formulate the component models. Each component model may contain several model parameters to be determined by fitting against training data. The model training process for each model
Above: The LLM model was applied to the purification loop of the Mechanisms Engineering Test Loop (METL) liquid sodium facility at the Argonne National Laboratory
34 | July 2025 |
www.neimagazine.com
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45
