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An Event Recognition Method for Φ-OTDR Sensing System Based on Deep Learning.

Yi Shi1, Yuanye Wang2, Lei Zhao2

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A new deep learning method enhances phase-sensitive optical time domain reflectometer (Φ-OTDR) systems by accurately recognizing events. This advancement overcomes a key limitation, improving real-world applications for fiber optic sensing.

Keywords:
convolutional neural networkdeep learningevent recognitionΦ-OTDR

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Area of Science:

  • Optical Engineering
  • Machine Learning
  • Sensor Technology

Background:

  • Phase-sensitive optical time domain reflectometer (Φ-OTDR) systems are vital for long-range monitoring.
  • Current Φ-OTDR systems lack robust event recognition capabilities, limiting field applications.

Purpose of the Study:

  • To develop an effective event recognition method for Φ-OTDR systems using deep learning.
  • To address the bottleneck of event recognition in Φ-OTDR applications.

Main Methods:

  • A deep learning approach utilizing a convolutional neural network (CNN).
  • Directly inputting temporal-spatial data matrices from Φ-OTDR into the CNN.
  • Employing simple pre-processing steps: bandpass filtering and grayscale transformation for real-time processing.
  • Designing an optimized CNN with a small size, high training speed, and high classification accuracy.

Main Results:

  • Achieved 96.67% classification accuracy in recognizing 5 distinct event types.
  • Demonstrated a retraining time of only 7 minutes for new sensing setups.
  • Successfully processed 5644 event samples.

Conclusions:

  • The proposed deep learning method significantly improves event recognition in Φ-OTDR systems.
  • The optimized CNN offers a practical solution for real-time, accurate event classification.
  • This approach enhances the applicability of Φ-OTDR in pipeline pre-warning, perimeter security, and structural health monitoring.