用光纤传感监测海底电缆暴露长度变化，小样本下仍有效。
Monitoring exposure-length variations in submarine power cables using distributed fiber-optic sensing
- 用回归方法提取振动特征，抑制环境干扰，保留暴露长度信息。
- 实验显示异常评分与暴露长度变化强相关（r = -0.83），F1达0.82。
- 适合在数据少的海上环境中做电缆健康状态实时监控。
本研究提出一种基于分布式声学传感（DAS）的异常检测框架，用于监测海底自由悬跨电缆的暴露长度变化。针对海上环境中的环境波动和训练数据有限问题，引入基于回归的特征提取方法，获得低维潜在表示，保留暴露长度相关的振动特性，同时抑制环境影响。提取的特征用于一类支持向量机（SVM）的异常检测。通过波浪水池实验验证，暴露长度范围为2至10米，实验结果表明异常评分随暴露长度变化近似单调下降，相关系数达$r = -0.83$。尽管仅使用少量样本训练，二分类任务的F1分数仍达到0.82。结果表明，在严重数据限制下，暴露长度变化可被可靠检测，支持了基于DAS的电缆状态监测潜力。
原文摘要 · Abstract (English)
This study proposes an anomaly-detection framework for monitoring exposure-length variations in submarine free-span cables using Distributed Acoustic Sensing (DAS), which is one of the distributed fiber-optic sensing technologies. To address environmental variability and limited training data in offshore environments, a regression-based feature extraction method was introduced to derive low-dimensional latent representations that retain exposure length-dependent vibration characteristics while suppressing environmental influences. The extracted features were used for one-class Support Vector Machine (SVM)-based anomaly detection. The proposed framework was evaluated through wave-tank experiments with exposure lengths ranging from 2 to 10 m. Experimental results showed that anomaly scores decreased approximately monotonically with increasing exposure-length change, exhibiting a strong correlation ($r = -0.83$). The binary classification achieved an F1 score of 0.82 despite training with only small-sample datasets. These findings demonstrate that exposure-length variations can be reliably detected under severe data limitations, supporting the potential of DAS-based cable condition monitoring.
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