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Volume 39 Issue 6
Sep.  2015
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Application of clustering analysis in distributed fiber optical vibration sensing systems

  • Corresponding author: YIN Xinda, yinxinda@wutos.com
  • Received Date: 2014-08-11
    Accepted Date: 2014-08-15
  • To solve the problem that it is difficult to position the perturbation point and judge the perturbation time under multiple simultaneous perturbations in a distributed optical fiber vibration sensing system, signal processing method was carried out and clustering analysis technology was applied. Firstly, hotspot map gathered by the system was processed above a threshold, and then the target objects were extracted. According to the results of clustering analysis, perturbation points are positioned and perturbation time is confirmed. The results show that the method makes an accurate and reasonable judgment for positioning the perturbation behavior and perturbation duration and the noise interference is eliminated at the same time. This is one step of event classification. The study provides the basis for the final system alarm strategy.
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    ESTER M, KRIEGEL H P,JRGS,et al.A Density-based algorithm for discovering clusters in large spatial databases with noise[C]//Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining.Portland, Oregon,USA:American Association for Artificial Intelligence Press,1996:226-231.
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Application of clustering analysis in distributed fiber optical vibration sensing systems

    Corresponding author: YIN Xinda, yinxinda@wutos.com
  • 1. Wuhan Research Institute of Post and Telecommunication, Wuhan 430074, China;
  • 2. WUTOS Technology Co. Ltd., Wuhan 430223, China

Abstract: To solve the problem that it is difficult to position the perturbation point and judge the perturbation time under multiple simultaneous perturbations in a distributed optical fiber vibration sensing system, signal processing method was carried out and clustering analysis technology was applied. Firstly, hotspot map gathered by the system was processed above a threshold, and then the target objects were extracted. According to the results of clustering analysis, perturbation points are positioned and perturbation time is confirmed. The results show that the method makes an accurate and reasonable judgment for positioning the perturbation behavior and perturbation duration and the noise interference is eliminated at the same time. This is one step of event classification. The study provides the basis for the final system alarm strategy.

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