Syn-Diag：一种基于大语言模型的云边协同框架，用于边缘端泛化小样本故障诊断
Syn-Diag: An LLM-based Synergistic Framework for Generalizable Few-shot Fault Diagnosis on the Edge
- Beihang University（北京航空航天大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
Syn-Diag提出云边协同的LLM框架，通过三层机制解决小样本故障诊断，在六个数据集上显著优于现有方法，边缘模型体积减83%、延迟降50%而性能相当。
AI中文摘要：
工业故障诊断面临数据稀缺和在资源受限环境中部署大型AI模型困难的双重挑战。本文介绍了Syn-Diag，一种新颖的云边协同框架，利用大语言模型来克服小样本故障诊断中的这些限制。Syn-Diag基于三层机制构建：1）视觉-语义协同，通过跨模态预训练将信号特征与大语言模型的语义空间对齐；2）内容感知推理，动态构建上下文提示以在有限样本下提高诊断准确性；3）云边协同，利用知识蒸馏创建轻量级、高效的边缘模型，能够通过共享决策空间进行在线更新。在覆盖CWRU和SEU不同工作条件的六个数据集上进行的大量实验表明，Syn-Diag显著优于现有方法，尤其是在1样本和跨条件场景中。边缘模型在模型大小减少83%和延迟降低50%的情况下，实现了与云版本相当的性能，为现代智能诊断提供了一种实用、稳健且可部署的范式。
英文摘要：
Industrial fault diagnosis faces the dual challenges of data scarcity and the difficulty of deploying large AI models in resource-constrained environments. This paper introduces Syn-Diag, a novel cloud-edge synergistic framework that leverages Large Language Models to overcome these limitations in few-shot fault diagnosis. Syn-Diag is built on a three-tiered mechanism: 1) Visual-Semantic Synergy, which aligns signal features with the LLM's semantic space through cross-modal pre-training; 2) Content-Aware Reasoning, which dynamically constructs contextual prompts to enhance diagnostic accuracy with limited samples; and 3) Cloud-Edge Synergy, which uses knowledge distillation to create a lightweight, efficient edge model capable of online updates via a shared decision space. Extensive experiments on six datasets covering different CWRU and SEU working conditions show that Syn-Diag significantly outperforms existing methods, especially in 1-shot and cross-condition scenarios. The edge model achieves performance comparable to the cloud version while reducing model size by 83% and latency by 50%, offering a practical, robust, and deployable paradigm for modern intelligent diagnostics.
