Field test of telecom facility anomaly detection with variational auto encoder leveraging DAS data

Telecommunication aerial optical cables frequently experience failures that cause no optical power loss. It is possible to discern signs of the failure if we can detect cable vibration that deviates from the normal condition due to changes in the installation condition. We propose an anomaly detection method for aerial cables using variational auto-encoder (VAE) and distributed vibration data measured by Φ-OTDR. The proposed method offers robust anomaly detection even under a significant variety of vibration characteristics reflecting different facility installation conditions; it achieved 85.0 % accuracy in detecting anomalies in an evaluation of actual 20 cable detachments in the field.

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Publication Details

Published
2025-05-22
DOI
https://doi.org/10.1117/12.3073158
Primary Topic
Anomaly Detection Techniques and Applications
Type
article
Field-Weighted Citation Impact
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article

Field test of telecom facility anomaly detection with variational auto encoder leveraging DAS data

Chihiro Kito, Kunihiro Toge, Keisuke Murakami
Anomaly Detection Techniques and Applications
article

Field test of telecom facility anomaly detection with variational auto encoder leveraging DAS data

Chihiro Kito, Kunihiro Toge, Keisuke Murakami
article en

Abstract

Telecommunication aerial optical cables frequently experience failures that cause no optical power loss. It is possible to discern signs of the failure if we can detect cable vibration that deviates from the normal condition due to changes in the installation condition. We propose an anomaly detection method for aerial cables using variational auto-encoder (VAE) and distributed vibration data measured by Φ-OTDR. The proposed method offers robust anomaly detection even under a significant variety of vibration characteristics reflecting different facility installation conditions; it achieved 85.0 % accuracy in detecting anomalies in an evaluation of actual 20 cable detachments in the field.

NTT (Japan) (JP)
Openalex Percentile: Top 95%
Anomaly Detection Techniques and Applications
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