# Integrating LLMs for Explainable Fault Diagnosis in Complex Systems

arXiv: https://arxiv.org/abs/2402.06695 · HTML: https://mlsanity.com/paper/2402.06695/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 8 references that are in the map.
- First posted on arXiv 8 Feb 2024.

## Builds on (most cited first, 1 of 1)

- [Visual Analytics for Explainable Deep Learning](https://mlsanity.com/paper/1804.02527/) (2018)

## Built on by (most cited first, 7 of 7)

- [Towards Secure Intelligent O-RAN Architecture: Vulnerabilities, Threats and Promising Technical Solutions using LLMs](https://mlsanity.com/paper/2411.08640/) (2024)
- [Agentic Physical AI toward a Domain-Specific Foundation Model for Energy Systems: A Case Study on Nuclear Reactor Control](https://mlsanity.com/paper/2512.23292/) (2025)
- [Complex System Diagnostics Using a Knowledge Graph-Informed and Large Language Model-Enhanced Framework](https://mlsanity.com/paper/2505.21291/) (2025)
- [CAMB: A comprehensive industrial LLM benchmark on civil aviation maintenance](https://mlsanity.com/paper/2508.20420/) (2025)
- [Scientific feedback reveals productive regimes beyond training conventions](https://mlsanity.com/paper/2512.15756/) (2025)
- [Overcoming the Regulatory Bottleneck via Agent-to-Agent Protocols: A Nuclear Case Study](https://mlsanity.com/paper/2606.07866/) (2026)
- [Adapting Vision-Language Models for Human-Readable XAI in Industrial Object Detection](https://mlsanity.com/paper/2609.31690/) (2026)

## Abstract (arXiv)

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.