提出新框架，让设备故障诊断在噪声中更稳定可靠。
Global-focal Adaptation with Information Separation for Noise-robust Transfer Fault Diagnosis
- 分离噪声与故障特征，用对抗学习增强跨域鲁棒性
- 在三个公开数据集上准确率超越现有方法
- 适合工业场景下噪声大、数据差异大的故障诊断
现有迁移故障诊断方法通常假设数据干净或领域相似，难以应对工业环境中噪声严重且领域偏移共存的挑战。为此，本文提出信息分离全局-焦点对抗网络（ISGFAN），一种在噪声条件下实现跨域故障诊断的鲁棒框架。该框架基于信息分离架构，结合对抗学习与改进的正交损失，解耦出与领域无关的故障表征，有效分离噪声干扰与领域特异性特征。为进一步提升迁移鲁棒性，ISGFAN采用全局-焦点领域对抗策略，同时约束模型的条件分布与边缘分布：焦点对抗组件缓解无监督场景下噪声导致的类别特异性迁移障碍，全局领域分类器保证整体分布对齐。在三个公开基准数据集上的实验表明，所提方法显著优于现有主流方法，验证了ISGFAN框架的有效性。代码与数据已开源：https://github.com/JYREN-Source/ISGFAN
原文摘要 · Abstract (English)
Existing transfer fault diagnosis methods typically assume either clean data or sufficient domain similarity, which limits their effectiveness in industrial environments where severe noise interference and domain shifts coexist. To address this challenge, we propose an information separation global-focal adversarial network (ISGFAN), a robust framework for cross-domain fault diagnosis under noise conditions. ISGFAN is built on an information separation architecture that integrates adversarial learning with an improved orthogonal loss to decouple domain-invariant fault representation, thereby isolating noise interference and domain-specific characteristics. To further strengthen transfer robustness, ISGFAN employs a global-focal domain-adversarial scheme that constrains both the conditional and marginal distributions of the model. Specifically, the focal domain-adversarial component mitigates category-specific transfer obstacles caused by noise in unsupervised scenarios, while the global domain classifier ensures alignment of the overall distribution. Experiments conducted on three public benchmark datasets demonstrate that the proposed method outperforms other prominent existing approaches, confirming the superiority of the ISGFAN framework. Data and code are available at https://github.com/JYREN-Source/ISGFAN
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