Consultation on Industrial Machine Faults with Large language Models

TL;DR

This paper presents a novel approach using Large Language Models with multi-round prompting to improve industrial machine fault diagnosis, achieving higher accuracy and better contextual understanding than traditional methods.

Contribution

It introduces a structured multi-round prompting technique for LLMs to enhance fault diagnosis accuracy in industrial settings, surpassing baseline models.

Findings

Achieved 91% accuracy in fault diagnosis

Outperformed baseline models in diverse fault types

Demonstrated improved contextual understanding

Abstract

Industrial machine fault diagnosis is a critical component of operational efficiency and safety in manufacturing environments. Traditional methods rely heavily on expert knowledge and specific machine learning models, which can be limited in their adaptability and require extensive labeled data. This paper introduces a novel approach leveraging Large Language Models (LLMs), specifically through a structured multi-round prompting technique, to improve fault diagnosis accuracy. By dynamically crafting prompts, our method enhances the model's ability to synthesize information from diverse data sources, leading to improved contextual understanding and actionable recommendations. Experimental results demonstrate that our approach outperforms baseline models, achieving an accuracy of 91% in diagnosing various fault types. The findings underscore the potential of LLMs in revolutionizing industrial fault consultation practices, paving the way for more effective maintenance strategies in complex environments.