Structural health monitoring (SHM) is critical to safeguarding the safety and reliability of aerospace, civil, and mechanical infrastructure. Machine learning-based data-driven approaches have gained popularity in SHM due to advancements in sensors and computational power. However, machine learning models used in SHM are vulnerable to adversarial examples -- even small changes in input can lead to different model outputs. This paper aims to address this problem by discussing adversarial defenses in SHM. In this paper, we propose an adversarial training method for defense, which uses circle loss to optimize the distance between features in training to keep examples away from the decision boundary. Through this simple yet effective constraint, our method demonstrates substantial improvements in model robustness, surpassing existing defense mechanisms.

翻译：结构健康监测（SHM）对于保障航空航天、土木及机械基础设施的安全与可靠性至关重要。随着传感器与计算能力的进步，基于机器学习的数据驱动方法在SHM领域日益普及。然而，SHM中使用的机器学习模型易受对抗样本攻击——即使输入发生微小变化也可能导致模型输出不同。本文旨在通过探讨SHM中的对抗防御策略来解决这一问题。我们提出一种基于对抗训练的防御方法，该方法利用Circle Loss优化训练过程中特征间的距离，使样本远离决策边界。通过这种简洁而有效的约束，我们的方法显著提升了模型鲁棒性，其性能超越了现有防御机制。