这篇论文把GPT-2用在轴承故障诊断上，只用10%的标签数据就比SOTA高了17个百分点，工业场景下很实用。
该论文提出一种知识引导的两阶段迁移学习框架，核心是一个轻量级GPT-2风格Transformer，利用因果自注意力从振动信号中分层提取特征。框架通过多源预训练学习通用表示，并借助原型知识调制和分类自适应实现跨域迁移。在4个真实数据集上，仅用10%标签数据即达92.61%平均准确率，比现有最佳方法高17.24个百分点。该方法为工业4.0低成本预测性维护提供了可行方案。
An LLM-based Two-Stage Transformer Framework for Cross-Domain Bearing Fault Diagnosis with Limited Data
Bearing fault diagnosis faces critical challenges when dataset heterogeneity, operating condition variations, and limited labeled data occur simultaneously in industrial environments. Existing approaches address these issues in isolation and rely on implicit feature alignment, limiting effectiveness under concurrent challenges. This paper proposes a knowledge-guided two-stage transfer learning framework that employs a lightweight GPT-2-style Transformer with causal self-attention for hierarchical feature extraction from vibration signals, establishing explicit pathways where pre-trained encoder weights and fault prototype embeddings serve as knowledge carriers from multi-source pre-training to target adaptation. The framework addresses the dual-shift challenge through multi-source learning for generalizable representations, prototype-based knowledge modulation for target adaptation, and taxonomy-adaptive classification for seamless transfer across heterogeneous fault categories. Experimental validation on four real-world datasets demonstrates 92.61% average accuracy with only 10% labeled target data, outperforming state-of-the-art methods by 17.24 percentage points, establishing a practical pathway toward cost-effective predictive maintenance in Industry 4.0 applications.