Summary of Faultexplainer: Leveraging Large Language Models For Interpretable Fault Detection and Diagnosis, by Abdullah Khan et al.
FaultExplainer: Leveraging Large Language Models for Interpretable Fault Detection and Diagnosis
by Abdullah Khan, Rahul Nahar, Hao Chen, Gonzalo E. Constante Flores, Can Li
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents FaultExplainer, an interactive tool for fault detection, diagnosis, and explanation in chemical processes. It integrates real-time sensor data visualization, Principal Component Analysis (PCA)-based fault detection, and identification of top contributing variables within a user interface powered by large language models (LLMs). The LLMs’ reasoning capabilities are evaluated in two scenarios: one where historical root causes are provided, and one where they are not. Experimental results using GPT-4o and o1-preview models demonstrate the system’s strengths in generating plausible and actionable explanations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FaultExplainer is a new tool that helps people detect and fix problems in chemical processes. It uses computers to look at real-time data from sensors, find patterns, and figure out what’s going wrong. The tool also provides explanations for why something went wrong, which can help operators make better decisions. The authors tested the tool using two different scenarios: one where they had historical information about what caused problems in the past, and another where they didn’t have that information. They found that the tool was good at generating helpful explanations, but it’s not perfect and sometimes makes mistakes. |
Keywords
» Artificial intelligence » Gpt » Pca » Principal component analysis
