Distributed φ-OTDR signal classification based on VMD and SVM

Hailun Jia; Lei Cao; Kang Xie; Jiajun Wu; Zhenjia Li; Xuan Cao; Guojie Tu

doi:10.1117/12.3029092

6 May 2024 Distributed φ-OTDR signal classification based on VMD and SVM

Hailun Jia, Lei Cao, Kang Xie, Jiajun Wu, Zhenjia Li, Xuan Cao, Guojie Tu

Proceedings Volume 13107, Fourth International Conference on Sensors and Information Technology (ICSI 2024); 1310722 (2024) https://doi.org/10.1117/12.3029092
Event: Fourth International Conference on Sensors and Information Technology (ICSI 2024), 2024, Xiamen, China

Abstract

With the development of distributed fiber optic sensing, the recognition of different vibration modes has become increasingly important. In this paper, a distributed external-heterodyne φ-OTDR system is used for outdoor vibration acquisition and mode recognition. In the event recognition experiment, feature vectors are obtained using the Variational Mode Decomposition (VMD) algorithm. Support Vector Machine (SVM) is then used to classify the input feature vectors accordingly. Through this experimental method, it is possible to accurately identify five types of vibrations: stepping, tapping, wind blowing, passing subway, and passing car. Compared to traditional methods, the accuracy has improved from 72.50% to 91.25%, demonstrating promising application prospects.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hailun Jia, Lei Cao, Kang Xie, Jiajun Wu, Zhenjia Li, Xuan Cao, and Guojie Tu "Distributed φ-OTDR signal classification based on VMD and SVM", Proc. SPIE 13107, Fourth International Conference on Sensors and Information Technology (ICSI 2024), 1310722 (6 May 2024); https://doi.org/10.1117/12.3029092

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