用大模型提升边缘设备少样本故障诊断能力，兼顾精度与部署效率。
Syn-Diag: An LLM-based Synergistic Framework for Generalizable Few-shot Fault Diagnosis on the Edge
- 通过跨模态预训练实现信号特征与语义空间对齐
- 1样本下仍保持高诊断准确率，跨工况表现优异
- 适合工业现场资源受限环境的智能诊断应用
工业故障诊断面临数据稀缺和大型AI模型难以在资源受限环境下部署的双重挑战。本文提出Syn-Diag，一种基于大语言模型的云边协同框架，用于少样本故障诊断。该框架采用三层机制：1）视觉-语义协同，通过跨模态预训练将信号特征与大模型语义空间对齐；2）内容感知推理，动态构建上下文提示以增强小样本下的诊断准确性；3）云边协同，利用知识蒸馏生成轻量级边缘模型，支持通过共享决策空间在线更新。在六个涵盖不同CWRU和SEU工况的数据集上进行的大量实验表明，Syn-Diag显著优于现有方法，尤其在1样本及跨工况场景中表现突出。边缘模型性能接近云端版本，同时模型规模减少83%，延迟降低50%，为现代智能诊断提供了一种实用、鲁棒且可部署的解决方案。
原文摘要 · Abstract (English)
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.
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