基于分布式变形传感和卡尔曼滤波的海底电缆异常事件的基于模型的检测
Model-Based Detection of Anomalous Events in Submarine Cables Using Distributed Deformation Sensing and Kalman Filtering
AI总结：
研究利用分布式变形传感和卡尔曼滤波对海底电缆异常事件进行基于模型的检测，将电缆建模并经空间离散化获得状态空间表示，用卡尔曼滤波器估计动态状态，通过统计假设检验进行异常检测，能可靠识别局部干扰且对环境激励保持鲁棒性。
摘要：
海底电力和通信电缆是关键的全球基础设施，但易受海上活动和故意破坏造成的机械损坏。因此，持续监测这些资产对于早期发现异常事件至关重要。本文提出了一个基于模型的框架，用于利用沿电缆的空间分布式变形测量对海底电缆进行实时异常检测。将电缆建模为受固定边界条件的阻尼波动方程支配的张紧结构。通过空间离散化获得有限维状态空间表示，从而能够使用卡尔曼滤波器在随机环境干扰下估计电缆的动态状态。然后将异常检测表述为应用于滤波器创新序列的统计假设检验。与纯数据驱动的警报相比，该框架提供了一个可解释的残差信号，其阈值可与规定的误报概率相关。数值模拟表明，该框架能够可靠地识别局部干扰，同时对环境激励保持鲁棒性。
英文摘要：
Submarine power and telecommunication cables constitute critical global infrastructure, yet they remain vulnerable to mechanical damage caused by maritime activities and intentional tampering. Continuous monitoring of these assets is therefore essential for early detection of anomalous events. This paper proposes a model-based framework for real-time anomaly detection in submarine cables using spatially distributed deformation measurements along the cable. The cable is modeled as a tensioned structure governed by a damped wave equation with fixed boundary conditions. A finite-dimensional state-space representation is obtained through spatial discretization, enabling the use of a Kalman filter to estimate the cable's dynamic state under stochastic environmental disturbances. Anomaly detection is then formulated as a statistical hypothesis test applied to the innovation sequence of the filter. Compared with purely data-driven alarms, the proposed framework provides an interpretable residual signal whose threshold can be related to a prescribed false-alarm probability. Numerical simulations demonstrate that the proposed framework can reliably identify localized disturbances while remaining robust to ambient environmental excitation.
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