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

Phase-sensitive optical time domain reflectometer (Φ-OTDR) based distributed optical fiber sensing system has been widely used in many fields such as long range pipeline pre-warning, perimeter security and structure health monitoring. However, the lack of event recognition ability is always being the bottleneck of Φ-OTDR in field application. An event recognition method based on deep learning is proposed in this paper. This method directly uses the temporal-spatial data matrix from Φ-OTDR as the input of a convolutional neural network (CNN). Only a simple bandpass filtering and a gray scale transformation are needed as the pre-processing, which achieves real-time. Besides, an optimized network structure with small size, high training speed and high classification accuracy is built. Experiment results based on 5644 events samples show that this network can achieve 96.67% classification accuracy in recognition of 5 kinds of events and the retraining time is only 7 min for a new sensing setup.

[1]  J. de Vries.  A low cost fence impact classification system with neural networks , 2004, 2004 IEEE Africon. 7th Africon Conference in Africa (IEEE Cat. No.04CH37590).

[2]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Guo-ming Ma,et al.  A Non-Intrusive Electrical Discharge Localization Method for Gas Insulated Line Based on Phase-Sensitive OTDR and Michelson Interferometer , 2019, IEEE Transactions on Power Delivery.

[4]  Katerina Krebber,et al.  Wavelength-scanning coherent OTDR for dynamic high strain resolution sensing. , 2018, Optics express.

[5]  Pedro Corredera,et al.  Machine Learning Methods for Pipeline Surveillance Systems Based on Distributed Acoustic Sensing: A Review , 2017 .

[6]  Toygar Akgun,et al.  Deep learning based multi-threat classification for phase-OTDR fiber optic distributed acoustic sensing applications , 2017, Commercial + Scientific Sensing and Imaging.

[7]  Feng Zhang,et al.  Multi-target recognition used in airpoty fiber fence warning system , 2010, 2010 International Conference on Machine Learning and Cybernetics.

[8]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[9]  Hwang-Ki Min,et al.  Abnormal Signal Detection in Gas Pipes Using Neural Networks , 2007, IECON 2007 - 33rd Annual Conference of the IEEE Industrial Electronics Society.

[10]  J. Juarez,et al.  Distributed fiber-optic intrusion sensor system , 2005, Journal of Lightwave Technology.

[11]  Jing Li,et al.  On-line monitoring system of 35 kV 3-core submarine power cable based on φ-OTDR , 2018 .

[12]  Bin Zhang,et al.  Ultra-Long-Distance Hybrid BOTDA/Ф-OTDR , 2018, Sensors.

[13]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[14]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Ming Tang,et al.  Performance enhancement of ROTDR using deep convolutional neural networks , 2018 .

[16]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[17]  Xiangrong Liu,et al.  One-Dimensional CNN-Based Intelligent Recognition of Vibrations in Pipeline Monitoring With DAS , 2019, Journal of Lightwave Technology.

[18]  Qian Sun,et al.  Recognition of a Phase-Sensitivity OTDR Sensing System Based on Morphologic Feature Extraction , 2015, Sensors.

[19]  Zuyuan He,et al.  Frequency Response Enhancement of Direct-Detection Phase-Sensitive OTDR by Using Frequency Division Multiplexing , 2018, Journal of Lightwave Technology.

[20]  Fei Jiang,et al.  An event recognition method for fiber distributed acoustic sensing systems based on the combination of MFCC and CNN , 2018, International Conference on Optical Instruments and Technology.

[21]  Sascha Liehr,et al.  Real-time dynamic strain sensing in optical fibers using artificial neural networks. , 2019, Optics express.

[22]  Hugo F. Martins,et al.  Toward Prevention of Pipeline Integrity Threats Using a Smart Fiber-Optic Surveillance System , 2016, Journal of Lightwave Technology.

[23]  Ersan YAZAN,et al.  Comparison of the stochastic gradient descent based optimization techniques , 2017, 2017 International Artificial Intelligence and Data Processing Symposium (IDAP).

[24]  Raja Giryes,et al.  Deep Learning Approach for Processing Fiber-Optic DAS Seismic Data , 2018 .

[25]  Juan C Juarez,et al.  Field test of a distributed fiber-optic intrusion sensor system for long perimeters. , 2007, Applied optics.

[26]  Xiaohan Sun,et al.  Vibration pattern recognition and classification in OTDR based distributed optical-fiber vibration sensing system , 2014, Smart Structures.

[27]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).