提出细粒度特征表示方法，提升多工况下未知故障的识别能力。
Open-Set Fault Diagnosis in Multimode Processes via Fine-Grained Deep Feature Representation
- 融合多尺度卷积与注意力机制，捕捉健康状态的细微特征差异。
- 通过距离损失增强类内紧凑性，实现更清晰的分类边界。
- 结合极值理论识别未知故障，适合复杂工业场景的异常检测。
可靠的故障诊断系统不仅需准确分类已知运行状态，还应有效识别未知故障。在多工况过程中，同一健康状态的样本常呈现多种聚类分布，导致难以构建紧凑且准确的决策边界。为此，本文提出一种名为细粒度聚类与拒识网络（FGCRN）的新模型。该模型结合多尺度深度卷积、双向门控循环单元和时序注意力机制，以提取判别性特征；设计基于距离的损失函数，强化类内紧凑性；通过无监督学习构建细粒度特征表示，揭示各健康状态的内在结构；利用极值理论建模样本特征与其对应细粒度表示间的距离，从而实现未知故障的有效识别。大量实验表明该方法具有优越性能。
原文摘要 · Abstract (English)
A reliable fault diagnosis system should not only accurately classify known health states but also effectively identify unknown faults. In multimode processes, samples belonging to the same health state often show multiple cluster distributions, making it difficult to construct compact and accurate decision boundaries for that state. To address this challenge, a novel open-set fault diagnosis model named fine-grained clustering and rejection network (FGCRN) is proposed. It combines multiscale depthwise convolution, bidirectional gated recurrent unit and temporal attention mechanism to capture discriminative features. A distance-based loss function is designed to enhance the intra-class compactness. Fine-grained feature representations are constructed through unsupervised learning to uncover the intrinsic structures of each health state. Extreme value theory is employed to model the distance between sample features and their corresponding fine-grained representations, enabling effective identification of unknown faults. Extensive experiments demonstrate the superior performance of the proposed method.
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