Recent development in artificial neural network based distributed fiber optic sensors

Distributed fiber optic sensors are promising technique for measuring strain, temperature and vibration over tens of kilometres by utilizing the backscattered Rayleigh, Raman and Brillouin signals. Recently, the use of an artificial neural network (ANN) has been adopted into the distributed fiber sensors for advanced data analytics, fast data processing time, high sensing accuracy and event classification. In this paper, the recent developments of ANN-based distributed fiber sensors and their operating principles are reviewed. Moreover, the performance of ANN is compared with the conventional signal processing algorithms. The future perspective view that can be extended further research development has also been discussed.

[1]  Yanjun Zhang,et al.  A novel fitting algorithm for Brillouin scattering spectrum of distributed sensing systems based on RBFN networks , 2013 .

[2]  Zhi-Hong Mao,et al.  Fiber-optical distributed acoustic sensing signal enhancements using ultrafast laser and artificial intelligence for human movement detection and pipeline monitoring , 2019, OPTO.

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

[4]  Chao Lu,et al.  Integrating Radio-Over-Fiber Communication System and BOTDR Sensor System , 2020, Sensors.

[5]  Chao Lu,et al.  Signal processing using artificial neural network for BOTDA sensor system. , 2016, Optics express.

[6]  Changyuan Yu,et al.  Deep neural networks assisted BOTDA for simultaneous temperature and strain measurement with enhanced accuracy. , 2019, Optics express.

[7]  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.

[8]  Sema E. Alptekin,et al.  Artificial neural networks for robotics coordinate transformation , 1992 .

[9]  Wai Pang Ng,et al.  Employing wavelength diversity technique to enhance the Brillouin gain response in BOTDA system , 2016, 2016 Optical Fiber Communications Conference and Exhibition (OFC).

[10]  Wai Pang Ng,et al.  Future of distributed fiber sensors (invited paper) , 2016, 2016 15th International Conference on Optical Communications and Networks (ICOCN).

[11]  Chao Lu,et al.  Brillouin Optical Time-Domain Analyzer Assisted by Support Vector Machine for Ultrafast Temperature Extraction , 2017, Journal of Lightwave Technology.

[12]  Maria-Teresa Hussels,et al.  Fiber Optic Train Monitoring with Distributed Acoustic Sensing: Conventional and Neural Network Data Analysis , 2020, Sensors.

[13]  W S McCulloch,et al.  A logical calculus of the ideas immanent in nervous activity , 1990, The Philosophy of Artificial Intelligence.

[14]  Paul R. Ohodnicki,et al.  Phase-optical time domain reflectometry (Θ-OTDR) with enhanced performance , 2020, OPTO.

[15]  Kaiqiang Gao,et al.  Back propagation neutral network based signal acquisition for Brillouin distributed optical fiber sensors. , 2019, Optics express.

[16]  Nageswara Lalam,et al.  Distributed optical fiber sensing: Review and perspective , 2019, Applied Physics Reviews.

[17]  Xiaoxia Wu,et al.  Applications of Artificial Neural Networks in Optical Performance Monitoring , 2009, Journal of Lightwave Technology.

[18]  Moshe Tur,et al.  Analytical expression and experimental validation of the Brillouin gain spectral broadening at any sensing spatial resolution , 2017, 2017 25th Optical Fiber Sensors Conference (OFS).

[19]  L. Thévenaz,et al.  Brillouin gain spectrum characterization in single-mode optical fibers , 1997 .

[20]  Wai Pang Ng,et al.  Performance Improvement of Brillouin Ring Laser Based BOTDR System Employing a Wavelength Diversity Technique , 2018, Journal of Lightwave Technology.

[21]  Haipeng Shen,et al.  Artificial intelligence in healthcare: past, present and future , 2017, Stroke and Vascular Neurology.

[22]  Elfed Lewis,et al.  Neural networks and pattern recognition techniques applied to optical fibre sensors , 2000 .

[23]  Cicero Martelli,et al.  NARX neural network model for strong resolution improvement in a distributed temperature sensor. , 2018, Applied optics.

[24]  Tara N. Sainath,et al.  Deep Learning for Audio Signal Processing , 2019, IEEE Journal of Selected Topics in Signal Processing.

[25]  C. Lu,et al.  Temperature profile extraction using artificial neural network in BOTDA sensor system , 2015, 2015 Opto-Electronics and Communications Conference (OECC).

[26]  Assaf Klar,et al.  Feasibility study of the automated detection and localization of underground tunnel excavation using Brillouin optical time domain reflectometer , 2009, Defense + Commercial Sensing.

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

[28]  Raja Giryes,et al.  Efficient Processing of Distributed Acoustic Sensing Data Using a Deep Learning Approach , 2019, Journal of Lightwave Technology.

[29]  Hongqiao Wen,et al.  Partial Discharge Recognition Based on Optical Fiber Distributed Acoustic Sensing and a Convolutional Neural Network , 2019, IEEE Access.

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

[31]  Wai Pang Ng,et al.  Performance analysis of Brillouin optical time domain reflectometry (BOTDR) employing wavelength diversity and passive depolarizer techniques , 2018 .

[32]  Mohit Chamania,et al.  Artificial Intelligence (AI) Methods in Optical Networks: A Comprehensive Survey , 2018, Opt. Switch. Netw..

[33]  Scott Schreck,et al.  Neural networks: Applications and opportunities in aeronautics , 1996 .

[34]  Vijay Vusirikala,et al.  Field and lab experimental demonstration of nonlinear impairment compensation using neural networks , 2019, Nature Communications.