Laser & Optoelectronics Progress, Volume. 58, Issue 13, 1306003(2021)

Processing and Application of Fiber Optic Distributed Sensing Signal Based on Φ-OTDR

Huijuan Wu1、*, Xinyu Liu1, and Yunjiang Rao1,2、**
Author Affiliations
  • 1Key Laboratory of Fiber Optic Sensing and Communication, Ministry of Education, University of Electronic Science and Technology of China, Chengdu , Sichuan 611731, China
  • 2Fiber Optic Sensing Research Center, Zhijiang Laboratory, Hangzhou , Zhejiang 310000, China
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    Figures & Tables(42)
    Principle of the DVS/DAS based on Φ-OTDR
    Spatio-temporal structure of the Φ-OTDR signal[44]
    De-noising and anomaly detection results based on STFT. (a) Original differential trace; (b) local energy distribution along the trace; (c) local energy distribution after the background subtraction; (d) intrusion detection and location in the energy trace; (e) intrusion detection and location in the original differential trace[45]
    Signal-noise separation method based on multi-scale wavelet decomposition[44]
    Signal-noise separation results based on multi-scale wavelet decomposition. (a) Original temporal signal; (b) combined component of a6 and d6; (c) combined component of d3 and d4; (d) combined component of d1 and d2[44]
    Signal-noise separation results based on multi-scale wavelet decomposition. (a) Before the signal-noise separation; (b) after the signal-noise separation[45]
    Mining and recognition processing flow of sequential information based on HMM[36]
    State transition relationship between short-term SU features[36]
    Common typical event signals. (a) Background noise; (b) manual digging signal; (c) machine excavation signal; (d) traffic interference; (e) forging plant noise; (f) fabricating plant noise[36]
    Hidden state sequence mined by HMM. (a) Background noise; (b) manual digging signal; (c) machine excavation signal; (d) traffic interference; (e) forging plant noise; (f) fabricating plant noise[36]
    Training losses of different CNNs[33]
    Classification results of different CNN[33]
    Classification results of 1D-CNN combined with different models[33]
    Ten-fold cross classification results of 1D-CNN combined with different models[33]
    Spatio-temporal feature extraction process based on CNN-BiLSTM[42]
    Visualization results of different features. (a) Artificial features; (b) 2D-CNN features; (c) BiLSTM features; (d) CNN-BiLSTM features[42]
    Ten-fold cross-validation results of different models[42]
    Recognition time of single sample[42]
    Spatial energy distribution characteristics with different vertical distances. (a) 6 m; (b) 14 m[46]
    Vertical distances estimation method based on spatial energy distribution and integrated learning model[46]
    Test signal of the mechanical knocking. (a) Knocking scene; (b) time domain signal diagram[46]
    Spatial energy attenuation curves of the machine knocking signals. (a) Group 1; (b) group 2[46]
    Test signal of the mechanical excavation. (a) Excavation scene; (b) time domain signal diagram[46]
    Spatial energy attenuation curves of the excavation signals[46]
    Principles of border control and security technology[7]
    Laying method of optical cable and the monitoring signal before and after noise removal. (a) Laying method of the optical cable; (b) monitoring signal before denoising; (c) monitoring signal after denoising[7]
    Monitoring site for excavation prevention of long-distance oil pipelines. (a) Monitoring equipment; (b) gas station; (c) on-site test environment[49]
    Characteristic radar chart of typical event in an oil pipeline. (a) Background noise; (b) manual excavation; (c) mechanical excavation; (d) traffic disturbance; (e) factory interference
    Principle of the pipeline optical cable anti-theft and operation and maintenance monitoring system
    Interface of online monitoring and inspection. (a) Online positioning and inspection based on Baidu map; (b) statistical results of optical cable information
    Project site of submarine cable safety monitoring. (a) Monitored marine area; (b) monitoring center; (c) monitoring setup
    Monitoring site and test equipment of overhead transmission cables. (a) Monitoring center; (b) monitoring setup[49]
    Frequency and space distribution of cable wind dance. (a) 1:00—2:00; (b) 14:00—15:00[50]
    Installation wiring diagram of outdoor optical cable. (a) Sectional view; (b) top view; (c) installation process[4]
    Leak response signal of the DVS/DAS system. (a) Response graph of leakage when the valve is not opened; (b) response graph of leakage when the valve is opened[4]
    • Table 1. DVS/DAS signal detection and recognition method combined with machine learning model

      View table

      Table 1. DVS/DAS signal detection and recognition method combined with machine learning model

      Institutional unitFeature extraction dimensionRecognition network or modelAttention mechanismEnd-to-end networkRef.
      Beijing Jiaotong UniversitytemporalXGBoostnofalse

      31

      32

      temporalF-ELMnofalse
      University of Electronic Science and Technology of Chinatemporal1D-CNNnotrue33
      University of San Pablo Central European UniversitytemporalGMMsnofalse

      34

      35

      temporal

      contextual sequence

      GMMs+HMMnofalse
      University of Electronic Science and Technology of Chinatemporal structure and contextual sequenceHMMnofalse36
      Tianjin Universitymultiscale temporalMS-CNN+CPLnotrue37
      Anhui Universitymultiscale temporalMS-CNNnotrue38
      Transportation, Security, Energy & Automation Systems Business Sectortime-frequency2D-CNNnofalse28
      Beijing Institute of Technologytime-frequency2D-CNNnofalse29
      Zhejiang Universitytime-frequency2D-CNN+SVMnofalse30
      Shanghai Maritime Universitytime-frequencyPNNnofalse39
      University of Colognetime-frequencyALSTMyesfalse40
      Tianjin Universityspatial-temporal2D-CNNnofalse41
      University of Electronic Science and Technology of Chinaspatial-temporal1D-CNN+BiLSTMnotrue42
      Sichuan Universityspatial-temporal2D-CNN+ LSTMnofalse43
    • Table 2. Actual detection results of different methods

      View table

      Table 2. Actual detection results of different methods

      Different detection methodEnergy threshold detection methodModular maximum method of wavelet transformSTFT-based method
      PD/%76.7395.6598.76
      NAR(24 h)2871612
    • Table 3. Local structural features of the short-term SU

      View table

      Table 3. Local structural features of the short-term SU

      Feature typeFeature name
      Time domainmain impact strength、short time average magnitude、short time average energy
      Frequency domainfrequency band variance of PSD、frequency band information entropy of PSD、mean of amplitude of PSD、procrustes mean shape of PSD、amplitude standard deviation of PSD、shape standard deviation of PSD、amplitude of skewness of PSD、shape of skewness of PSD、amplitude of kurtosis of PSD、 shape of kurtosis of PSD
      Transformation domainwavelet packet energy spectrum、information entropy of wavelet packet、MFCC
      Model featureautoregression model coefficient、linear prediction model coefficient
    • Table 4. Classification performances of different models

      View table

      Table 4. Classification performances of different models

      ModelAverage recognition rateTypePrecisionRecallF-value
      HMM0.98211.00001.00001.0000
      21.00001.00001.0000
      31.00001.00001.0000
      40.95241.00000.9756
      51.00000.91300.9545
      SVM0.91911.00001.00001.0000
      20.75001.00000.8571
      31.00001.00001.0000
      40.89740.87500.8861
      51.00000.82610.9048
      RF0.92811.00000.95240.9756
      20.86670.86670.8667
      30.92311.00000.9600
      40.90000.90000.9000
      50.91300.91300.9130
      XGB0.93710.95241.00000.9756
      20.86671.00000.9286
      31.00001.00001.0000
      40.97500.86670.9176
      50.86960.95240.9091
      DT0.89211.00000.95240.9756
      20.81250.86670.8387
      31.00001.00001.0000
      40.86110.77500.8158
      50.74070.86960.8000
      BN0.78310.95241.00000.9756
      20.66670.45450.5405
      31.00000.92310.9600
      40.57500.79310.6667
      50.95650.81480.8800
    • Table 5. Parameters of CNN with different dimensions

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      Table 5. Parameters of CNN with different dimensions

      Network1D-CNN2D-CNN (T-F matrix)2D-CNN RGB image
      C1(1×5+1)×32=192(5×5+1)×32=832(1+5×5)×32=832
      C2(1+5)×64=384(5×5+1)×64=1664(1+5×5)×64=1664
      C3(1+5)×96=576(5×5+1)×96=2496(1+5×5)×96=2496
      FC164×96×1000=6144002×16×96×1000=307200013×16×96×1000=19968000
      FC21000×1000=10000001000×1000=10000001000×1000=1000000
      Total number of parametersabout 16000about 40800about 20000
    • Table 6. Model recognition results of the mechanical knock events[46]

      View table

      Table 6. Model recognition results of the mechanical knock events[46]

      Distance /mError accuracy(±1 m)/%Error accuracy(±2 m)/%Threat levelAccuracy rate /%
      0100100100
      1100100
      2100100
      3100100
      4100100
      510010090.8
      6100100
      7100100
      87171
      987100
      1083100
      11100100100
      12100100
      13100100
      14100100
      1589100
    • Table 7. Location results of the mechanical excavation[46]

      View table

      Table 7. Location results of the mechanical excavation[46]

      Distance/mError accuracy(±1 m)/%Error accuracy(±2 m)/%Threat levelAccuracy rate/%
      2868695.3
      3100100
      4100100
      6336665.8
      8100100
      11808085
      13100100
      1580100
      176060
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    Huijuan Wu, Xinyu Liu, Yunjiang Rao. Processing and Application of Fiber Optic Distributed Sensing Signal Based on Φ-OTDR[J]. Laser & Optoelectronics Progress, 2021, 58(13): 1306003

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    Paper Information

    Category: Fiber Optics and Optical Communications

    Received: Apr. 12, 2021

    Accepted: May. 19, 2021

    Published Online: Jul. 14, 2021

    The Author Email: Wu Huijuan (hjwu@uestc.edu.cn), Rao Yunjiang (yjrao@uestc.edu.cn)

    DOI:10.3788/LOP202158.1306003

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