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Unsupervised learning method for events identification in φ-OTDR

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Abstract

In this paper, an unsupervised-learning method for events-identification in φ-OTDR fiber-optic distributed vibration sensor is proposed. The different vibration-events including blowing, raining, direct and indirect hitting, and noise-induced false vibration are clustered by the k-means algorithm. The equivalent classification accuracy of 99.4% has been obtained, compared with the actual classes of vibration-events in the experiment. With the cluster-number of 3, the maximal Calinski-Harabaz index and Silhouette coefficient are obtained as 2653 and 0.7206, respectively. It is found that our clustering method is effective for the events-identification of φ-OTDR without any prior labels, which provides an interesting application of unsupervised-learning in self-classification of vibration-events for φ-OTDR.

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Acknowledgements

This work is supported by the Fundamental Research Funds for the Central Universities (2019JBM345), the Beijing Natural Science Foundation (4192047).

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Author notes

  1. Jie Zhang and Xiaoting Zhao contributed equally to this work.

Authors and Affiliations

  1. School of Artificial Intelligence, Beijing Technology and Business University, Beijing, 100048, China

    Jie Zhang

  2. Key Laboratory of Education Ministry On Luminescence and Optical Information Technology, Department of Physics, National Physical Experiment Teaching Demonstration Center, School of Science, Beijing Jiaotong University, Beijing, 100044, China

    Xiaoting Zhao, Fanchao Meng, Jinmin Ding, Yingli Niu, Xinghua Zhang & Sheng Liang

  3. School of Electronic Engineering, Xi’an University of Posts and Telecommunications, Xi’an, 710121, China

    Yiming Zhao

  4. School of Instrumentation Science and Opto-Electronics Engineering, Hefei University of Technology, NO.193 Tunxi Road, Hefei, 230009, Anhui province, China

    Xiang Zhong

  5. School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, 100044, China

    Yidan Wang

  6. Beijing Haidongqing Electrical and Mechanical Equipment Co., Ltd, Beijing, 101300, China

    Liang Dong

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  1. Jie Zhang
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Correspondence to Sheng Liang.

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Zhang, J., Zhao, X., Zhao, Y. et al. Unsupervised learning method for events identification in φ-OTDR. Opt Quant Electron 54, 457 (2022). https://doi.org/10.1007/s11082-022-03748-y

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