Integrating LLMs for Explainable Fault Diagnosis in Complex Systems
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Decision MakingDiagnosticFault DiagnosisLanguage ModelingLanguage ModellingLarge Language ModelSimilar Papers 제목 키워드 기반
S2S-FDD: Bridging Industrial Time Series and Natural Language for Explainable Zero-shot Fault Diagnosis
Fault diagnosis is critical for the safe operation of industrial systems. Conventional diagnosis models typically produce abstract outputs such as anomaly scores or fault categories, failing to answer critical operationa…
Fault DiagnosisExplainable Artificial Intelligence based Soft Evaluation Indicator for Arc Fault Diagnosis
Novel AI-based arc fault diagnosis models have demonstrated outstanding performance in terms of classification accuracy. However, an inherent problem is whether these models can actually be trusted to find arc faults. In…
Fault DiagnosisA Trustworthy Industrial Fault Diagnosis Architecture Integrating Probabilistic Models and Large Language Models
There are limitations of traditional methods and deep learning methods in terms of interpretability, generalization, and quantification of uncertainty in industrial fault diagnosis, and there are core problems of insuffi…
Fault DiagnosisExploring LLM-based Frameworks for Fault Diagnosis
Large Language Model (LLM)-based systems present new opportunities for autonomous health monitoring in sensor-rich industrial environments. This study explores the potential of LLMs to detect and classify faults directly…
Continual LearningFault DiagnosisFault Diagnosis in Power Grids with Large Language Model
Power grid fault diagnosis is a critical task for ensuring the reliability and stability of electrical infrastructure. Traditional diagnostic systems often struggle with the complexity and variability of power grid data.…
DiagnosticFault DiagnosisLanguage ModelingLanguage Modelling+2