用大模型多轮对话提升工业故障诊断准确率
Consultation on Industrial Machine Faults with Large language Models
- 通过动态构建提示词，让大模型整合多源信息
- 在多种故障类型上达到91%诊断准确率
- 适合需要智能维护支持的制造场景
工业机器故障诊断是制造业运行效率与安全的关键。传统方法依赖专家经验与特定机器学习模型，适应性差且需大量标注数据。本文提出一种新方法，利用大语言模型（LLMs）结合结构化多轮提示技术，提升故障诊断准确性。通过动态生成提示，增强模型对多源数据的综合能力，改善上下文理解并提供可操作建议。实验表明，该方法优于基线模型，在多种故障类型诊断中达到91%准确率。结果表明，大语言模型有望革新工业故障咨询实践，为复杂环境下的高效维护策略提供新路径。
原文摘要 · Abstract (English)
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.
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