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A deep learning model enabled multi-event recognition for distributed optical fiber sensing

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Abstract

Fiber optic sensors that utilize backscattered light offer distributed real-time measurements and have been seen tremendous improvements in sensing distance and spatial resolution over the last decades. However, these improvements in sensor capabilities lead to a significant increase in the amount of data that needs to be processed. Traditional processing schemes are no longer adequate, so the development of novel signal processing methods is critical. Phase-sensitive optical time domain reflectometry (Φ-OTDR) is now applied in various applications for multi-event recognition, and it would usually be difficult, sometimes even unrealistic to label all the acquired samples due to its real-time and seamless monitoring nature. To fully take advantage of the information contained within the large number of unlabeled samples, which were formerly not utilized and hence wasted, we propose a semi-supervised model to boost the event classification performance of Φ-OTDR. The model extracts respectively the temporal features and the spatial bidirectional features together with a dual attention mechanism. Its classification accuracy has been improved up to 96.9% with only 1230 labeled samples. In addition, our model shows significant advantages when the number of labeled samples is reduced. Importantly, our method improves the accuracy of multi-event classification without any modification to the optical setup.

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Acknowledgements

This work was supported in part by Fundamental Research Funds for Central Universities (Grant No. 021314380211), National Key Research and Development Program of China (Grant No. 2021YFB2900704), and Outstanding Chinese and Foreign Youth Exchange Program of China Association for Science and Technology. We are especially grateful to Ms. Manling TIAN, whose previous research on the semi-supervised model provided strong support for our research in this paper. At the same time, we would like to thank Ms. Shuman SUN for her help in setting 2D-CNN model’s parameters.

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Authors and Affiliations

  1. Institute of Information Science, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China

    Yujiao Li, Xiaomin Cao, Wenhao Ni & Kuanglu Yu

  2. Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing Jiaotong University, Beijing, 100044, China

    Yujiao Li, Xiaomin Cao, Wenhao Ni & Kuanglu Yu

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  1. Yujiao Li
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  2. Xiaomin Cao
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  3. Wenhao Ni
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  4. Kuanglu Yu
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Correspondence to Kuanglu Yu.

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Li, Y., Cao, X., Ni, W. et al. A deep learning model enabled multi-event recognition for distributed optical fiber sensing. Sci. China Inf. Sci. 67, 132404 (2024). https://doi.org/10.1007/s11432-023-3896-4

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  • DOI: https://doi.org/10.1007/s11432-023-3896-4

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