量化鲁棒性：用于网络物理系统深度学习预测的基准框架
Quantifying Robustness: A Benchmarking Framework for Deep Learning Forecasting in Cyber-Physical Systems
AI总结：
本文提出了一种用于工业网络物理系统深度学习预测的鲁棒性评估框架，通过分布鲁棒性定义和实际干扰模拟，量化模型性能并促进研究可重复性。
摘要：
网络物理系统（CPS）在制造业和能源分配等领域生成复杂的时间序列数据，这对于预测与健康管理（PHM）至关重要。尽管深度学习（DL）方法已显示出强大的预测能力，但其在工业CPS中的应用仍然有限，原因是缺乏足够的鲁棒性。现有的鲁棒性评估主要集中在形式验证或对抗性扰动上，无法充分代表现实世界CPS场景中的复杂性。为此，我们引入了一个基于分布鲁棒性的实用鲁棒性定义，专门针对工业CPS，并提出了一种系统性的鲁棒性评估框架。我们的框架模拟了现实中的干扰，如传感器漂移、噪声和不规则采样，使预测模型能够在真实世界CPS数据集上进行彻底的鲁棒性分析。鲁棒性定义提供了一个标准化的分数来量化和比较模型在不同数据集上的性能，帮助在选型和架构设计中做出明智决策。通过广泛的实证研究评估了主要的DL架构（包括循环、卷积、基于注意、模块化和结构化状态空间模型），我们展示了我们方法的适用性和有效性。我们公开发布我们的鲁棒性基准，以鼓励进一步的研究和可重复性。
英文摘要：
Cyber-Physical Systems (CPS) in domains such as manufacturing and energy distribution generate complex time series data crucial for Prognostics and Health Management (PHM). While Deep Learning (DL) methods have demonstrated strong forecasting capabilities, their adoption in industrial CPS remains limited due insufficient robustness. Existing robustness evaluations primarily focus on formal verification or adversarial perturbations, inadequately representing the complexities encountered in real-world CPS scenarios. To address this, we introduce a practical robustness definition grounded in distributional robustness, explicitly tailored to industrial CPS, and propose a systematic framework for robustness evaluation. Our framework simulates realistic disturbances, such as sensor drift, noise and irregular sampling, enabling thorough robustness analyses of forecasting models on real-world CPS datasets. The robustness definition provides a standardized score to quantify and compare model performance across diverse datasets, assisting in informed model selection and architecture design. Through extensive empirical studies evaluating prominent DL architectures (including recurrent, convolutional, attention-based, modular, and structured state-space models) we demonstrate the applicability and effectiveness of our approach. We publicly release our robustness benchmark to encourage further research and reproducibility.
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