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Photonic Sensors

, Volume 8, Issue 3, pp 234–241 | Cite as

RVFL-Based Optical Fiber Intrusion Signal Recognition With Multi-Level Wavelet Decomposition as Feature

  • Yanping Wang
  • Dianjun Gong
  • Liping Pang
  • Dan Yang
Open Access
Regular
  • 113 Downloads

Abstract

The optical fiber pre-warning system (OFPS) has been gradually considered as one of the important means for pipeline safety monitoring. Intrusion signal types are correctly identified which could reduce the cost of troubleshooting and maintenance of the pipeline. Most of the previous feature extraction methods in OFPS are usually quested from the view of time domain. However, in some cases, there is no distinguishing feature in the time domain. In the paper, firstly, the intrusion signal features of the running, digging, and pick mattock are extracted in the frequency domain by multi-level wavelet decomposition, that is, the intrusion signals are decomposed into five bands. Secondly, the average energy ratio of different frequency bands is obtained, which is considered as the feature of each intrusion type. Finally, the feature samples are sent into the random vector functional-link (RVFL) network for training to complete the classification and identification of the signals. Experimental results show that the algorithm can correctly distinguish the different intrusion signals and achieve higher recognition rate.

Keywords

OFPS multi-level wavelet decomposition optical fiber signal recognition RVFL 

Notes

Acknowledgment

The authors wish to express their gratitude to the anonymous reviewers and the associate editor for their rigorous comments during the review process. In addition, authors also would like to thank SUN Chengbin and TAN Lei in our laboratory for their great contributions to the data-collection work. This work was supported by the National Natural Science Foundation of China (Grant Nos. 61571014 and 61601006), Beijing Nature Science Foundation (Grant No. 4172017), and Beijing Municipal Science and Technology Project (Grant No. Z161100001016003).

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Copyright information

© The Author(s) 2018

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • Yanping Wang
    • 1
  • Dianjun Gong
    • 1
  • Liping Pang
    • 2
  • Dan Yang
    • 1
  1. 1.School of Electronic and Information EngineeringNorth China University of TechnologyBeijingChina
  2. 2.School of Aviation Science and EngineeringBeijing University of Aeronautics and AstronauticsBeijingChina

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