AI 中文总结
针对未知工况下动态工业系统故障诊断的分布偏移问题，提出工况引导的选择性自适应方法，通过离线对抗学习与在线可靠样本更新提升诊断性能，在齿轮箱、电机数据集上优于现有最优方法，具实用潜力。
AI 中文摘要
对于动态工业系统，未知工况下的故障诊断仍具挑战性，因为工况变化引发的分布偏移会显著降低诊断模型在实际应用中的性能。为解决该问题，本文提出一种工况引导的选择性自适应方法。具体而言，在离线阶段开发了一种带渐进训练的新型连续工况对抗学习策略，以提升诊断模型的泛化能力；在线部署阶段，利用剩余工况响应从流数据中识别可靠的未标记样本，再用这些样本更新诊断模型。在实际的齿轮箱和电机数据集上开展的大量实验表明，所提框架在诊断精度上优于现有最优方法，同时保持较低的测试时间，展现出实际工业应用潜力。
英文摘要
Fault diagnosis under unknown operating conditions remains challenging for dynamic industrial systems, as the distribution shift caused by changing operating conditions can significantly degrade the performance of diagnostic models in real-world applications. To address the problem, a condition-guided selective adaptation approach is proposed. Specifically, a novel continuous operating-condition adversarial learning strategy with progressive training is developed in the offline stage to enhance the generalization ability of the diagnostic model. During online deployment, residual operating-condition responses are exploited to identify reliable unlabeled samples from streaming data, which are then used to update the diagnostic model. Extensive experiments on real-world gearbox and motor datasets have demonstrated that the proposed framework outperforms state-of-the-art methods in diagnostic accuracy while maintaining relatively low test-time, showing its potential for practical industrial applications.
