FaultExplainer: Leveraging Large Language Models for Interpretable Fault Detection and Diagnosis
This work addresses interpretability for process operators in chemical fault detection, but it is incremental as it combines existing methods like PCA and LLMs in a new interface.
The paper tackles the lack of interpretability and difficulty in diagnosing unseen faults in chemical process fault detection by presenting FaultExplainer, an interactive tool that integrates PCA-based detection with LLMs for explanation, showing strengths in generating plausible explanations but with limitations like feature reliance and hallucinations.
Machine learning algorithms are increasingly being applied to fault detection and diagnosis (FDD) in chemical processes. However, existing data-driven FDD platforms often lack interpretability for process operators and struggle to identify root causes of previously unseen faults. This paper presents FaultExplainer, an interactive tool designed to improve fault detection, diagnosis, and explanation in the Tennessee Eastman Process (TEP). FaultExplainer integrates real-time sensor data visualization, Principal Component Analysis (PCA)-based fault detection, and identification of top contributing variables within an interactive user interface powered by large language models (LLMs). We evaluate the LLMs' reasoning capabilities in two scenarios: one where historical root causes are provided, and one where they are not to mimic the challenge of previously unseen faults. Experimental results using GPT-4o and o1-preview models demonstrate the system's strengths in generating plausible and actionable explanations, while also highlighting its limitations, including reliance on PCA-selected features and occasional hallucinations.