Few-Shot Class-Incremental Fault Diagnosis (FSC-FD), which aims to continuously learn from new fault classes with only a few samples without forgetting old ones, is critical for real-world industrial systems. However, this challenging task severely amplifies the issues of catastrophic forgetting of old knowledge and overfitting on scarce new data. To address these challenges, this paper proposes a novel framework built upon Dual-Granularity Representations, termed the Dual-Granularity Guidance Network (DGGN). Our DGGN explicitly decouples feature learning into two parallel streams: 1) a fine-grained representation stream, which utilizes a novel Multi-Order Interaction Aggregation module to capture discriminative, class-specific features from the limited new samples. 2) a coarse-grained representation stream, designed to model and preserve general, class-agnostic knowledge shared across all fault types. These two representations are dynamically fused by a multi-semantic cross-attention mechanism, where the stable coarse-grained knowledge guides the learning of fine-grained features, preventing overfitting and alleviating feature conflicts. To further mitigate catastrophic forgetting, we design a Boundary-Aware Exemplar Prioritization strategy. Moreover, a decoupled Balanced Random Forest classifier is employed to counter the decision boundary bias caused by data imbalance. Extensive experiments on the TEP benchmark and a real-world MFF dataset demonstrate that our proposed DGGN achieves superior diagnostic performance and stability compared to state-of-the-art FSC-FD approaches. Our code is publicly available at https://github.com/MentaY/DGGN

翻译：少样本类增量故障诊断（FSC-FD）旨在仅用少量样本持续学习新的故障类别而不遗忘旧类别，对于实际工业系统至关重要。然而，这一具有挑战性的任务严重加剧了旧知识的灾难性遗忘和对稀缺新数据的过拟合问题。为解决这些挑战，本文提出了一种基于双粒度表征的新颖框架，称为双粒度引导网络（DGGN）。我们的DGGN明确将特征学习解耦为两个并行流：1）细粒度表征流，利用新颖的多阶交互聚合模块从有限的新样本中捕获具有区分性的类别特定特征；2）粗粒度表征流，旨在建模并保留所有故障类型共享的通用、类别无关知识。这两种表征通过多语义交叉注意力机制动态融合，其中稳定的粗粒度知识引导细粒度特征的学习，防止过拟合并缓解特征冲突。为进一步减轻灾难性遗忘，我们设计了一种边界感知样本优先级策略。此外，采用解耦的平衡随机森林分类器以应对数据不平衡引起的决策边界偏差。在TEP基准数据集和真实世界MFF数据集上的大量实验表明，与最先进的FSC-FD方法相比，我们提出的DGGN实现了更优的诊断性能和稳定性。我们的代码公开于https://github.com/MentaY/DGGN