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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.24880 |
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| _version_ | 1866913066854121472 |
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| author | Mishima, Sakiko Yajima, Yoshiyuki Tonami, Noriyuki Hino, Tomoyuki Aibe, Shugo Saikawa, Junichiro Mizuguchi, Koji |
| author_facet | Mishima, Sakiko Yajima, Yoshiyuki Tonami, Noriyuki Hino, Tomoyuki Aibe, Shugo Saikawa, Junichiro Mizuguchi, Koji |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_24880 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Monitoring exposure-length variations in submarine power cables using distributed fiber-optic sensing Mishima, Sakiko Yajima, Yoshiyuki Tonami, Noriyuki Hino, Tomoyuki Aibe, Shugo Saikawa, Junichiro Mizuguchi, Koji Signal Processing Machine Learning Physics and Society 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. |
| title | Monitoring exposure-length variations in submarine power cables using distributed fiber-optic sensing |
| topic | Signal Processing Machine Learning Physics and Society |
| url | https://arxiv.org/abs/2604.24880 |