Abstract

We experimentally demonstrate the use of artificial neural network (ANN) to process sensing signals obtained from Brillouin optical time domain analyzer (BOTDA). The distributed temperature information is extracted directly from the local Brillouin gain spectra (BGSs) along the fiber under test without the process of determination of Brillouin frequency shift (BFS) and hence conversion from BFS to temperature. Unlike our previous work for short sensing distance where ANN is trained by measured BGSs, here we employ ideal BGSs with different linewidths to train the ANN in order to take the linewidth variation due to different conditions from the training and testing phases into account, making it feasible for long distance sensing. Moreover, the performance of ANN is compared with other two techniques, Lorentzian curve fitting and cross-correlation method, and our results show that ANN has higher accuracy and larger tolerance to measurement error, especially at large frequency scanning step. We also show that the temperature extraction from BOTDA measurements employing ANN is significantly faster than the other two approaches. Hence ANN can be an excellent alternative tool to process BGSs measured by BOTDA and obtain temperature distribution along the fiber, especially when large frequency scanning step is adopted to significantly reduce the measurement time but without sacrifice of sensing accuracy.

© 2016 Optical Society of America

Full Article  |  PDF Article
OSA Recommended Articles
Temperature extraction in Brillouin optical time-domain analysis sensors using principal component analysis based pattern recognition

Abul Kalam Azad, Faisal Nadeem Khan, Waled Hussein Alarashi, Nan Guo, Alan Pak Tao Lau, and Chao Lu
Opt. Express 25(14) 16534-16549 (2017)

Support vector machine assisted BOTDA utilizing combined Brillouin gain and phase information for enhanced sensing accuracy

Huan Wu, Liang Wang, Nan Guo, Chester Shu, and Chao Lu
Opt. Express 25(25) 31210-31220 (2017)

Real-time dynamic strain sensing in optical fibers using artificial neural networks

Sascha Liehr, Lena Ann Jäger, Christos Karapanagiotis, Sven Münzenberger, and Stefan Kowarik
Opt. Express 27(5) 7405-7425 (2019)

References

  • View by:
  • |
  • |
  • |

  1. C. A. Galindez-Jamioy and J. M. Lopez-Higuera, “Brillouin Distributed Fiber Sensors: An Overview and Applications,” J. of Sensors 204121, 17 (2012).
  2. X. Bao and L. Chen, “Recent Progress in Distributed Fiber Optic Sensors,” Sensors (Basel) 12(12), 8601–8639 (2012).
    [Crossref] [PubMed]
  3. Y. Mizuno, W. Zou, Z. He, and K. Hotate, “Operation of Brillouin optical correlation-domain reflectometry: theoretical analysis and experimental validation,” J. Lightwave Technol. 28(22), 3300–3306 (2010).
  4. L. Thévenaz, “Brillouin distributed time-domain sensing in optical fibers: state of the art and perspectives,” Front. Optoelectron 3(1), 13–21 (2010).
    [Crossref]
  5. M. A. Soto, G. Bolognini, F. Di Pasquale, and L. Thévenaz, “Simplex-coded BOTDA fiber sensor with 1 m spatial resolution over a 50 km range,” Opt. Lett. 35(2), 259–261 (2010).
    [Crossref] [PubMed]
  6. C. A. Galindez, A. Quintela, M. A. Quintela, and J. M. Lopez-Higuera, “30cm of spatial resolution using pre-excitation pulse BOTDA technique,” Proc. SPIE 7753, 77531–77534 (2011).
    [Crossref]
  7. Y. Dong, L. Chen, and X. Bao, “Time-division multiplexing-based BOTDA over 100 km sensing length,” Opt. Lett. 36(2), 277–279 (2011).
    [Crossref] [PubMed]
  8. Y. Mao, N. Guo, K. L. Yu, H. Y. Tam, and C. Lu, “1-cm-Spatial-Resolution Brillouin Optical Time-Domain Analysis Based on Bright Pulse Brillouin Gain and Complementary Code,” IEEE Ph. J. 4(6), 2242–2248 (2012).
  9. C. N. Pannell, J. Dhliwayo, and D. J. Webb, “The accuracy of parameter estimation from noisy data, with application to resonance peak estimation in distributed Brillouin sensing,” Meas. Sci. Technol. 9(1), 50–57 (1998).
    [Crossref]
  10. C. Zhang, Y. Yang, and A. Li, “Application of Levenberg–Marquardt algorithm in the Brillouin spectrum fitting,” Proc. SPIE 7129, 71291Y (2008).
    [Crossref]
  11. M. A. Soto and L. Thévenaz, “Modeling and evaluating the performance of Brillouin distributed optical fiber sensors,” Opt. Express 21(25), 31347–31366 (2013).
    [Crossref] [PubMed]
  12. C. Li and Y. Li, “Fitting of Brillouin spectrum based on LabVIEW,” in Proc. 5th Int. Conf. Wireless Commun., Netw. Mobile Comput. 1–4 Sep. 2009.
    [Crossref]
  13. M. A. Farahani, E. Castillo-Guerra, and B. G. Colpitts, “Accurate estimation of Brillouin frequency shift in Brillouin optical time domain analysis sensors using cross correlation,” Opt. Lett. 36(21), 4275–4277 (2011).
    [Crossref] [PubMed]
  14. M. A. Farahani, E. Castillo-Guerra, and B. G. Colpitts, “A Detailed Evaluation of the Correlation-Based Method Used for Estimation of the Brillouin Frequency Shift in BOTDA Sensors,” IEEE Sens. J. 13(12), 4589–4598 (2013).
    [Crossref]
  15. A. K. Azad, L. Wang, N. Guo, C. Lu, and H. Y. Tam, “Temperature profile extraction using artificial neural network in BOTDA sensor system,” in The 20th Optoelectronics and Communications Conference (OECC), Shanghai, China, Jun.2015, paper 1570099759.
    [Crossref]
  16. A. K. Azad, L. Wang, N. Guo, C. Lu, and H. Y. Tam, “Temperature sensing in BOTDA system by using artificial neural network,” Electron. Lett. 51(20), 1578–1580 (2015).
    [Crossref]
  17. R. Ruiz-Lomber, J. M. Serrano, and J. M. Lopez-Higuera, “Automatic strain detection in a Brillouin Optical Time Domain sensor using Principal Component Analysis and Artificial Neural Networks,” Sensors (IEEE, 2014), pp. 1539–1542.
  18. S. Schlamp, H. G. Hornung, and E. B. Cummings, “Neural network data analysis for laser-induced thermal acoustics,” Meas. Sci. Technol. 11(6), 784–794 (2000).
    [Crossref]
  19. F. N. Khan, Y. Zhou, A. P. T. Lau, and C. Lu, “Modulation format identification in heterogeneous fiber-optic networks using artificial neural networks,” Opt. Express 20(11), 12422–12431 (2012).
    [Crossref] [PubMed]
  20. M. L. Hafiane, Z. Dibi, and O. Manck, “On the Capability of Artificial Neural Networks to Compensate Nonlinearities in Wavelength Sensing,” Sensors (Basel) 9(4), 2884–2894 (2009).
    [Crossref] [PubMed]
  21. K. Abhishek, M. P. Singh, S. Ghosh, and A. Anand, “Weather Forcasting models using Artificial Neural Network,” Procedia Tech. 4, 311–318 (2012).
    [Crossref]
  22. G. Zhang, B. E. Patuwo, and M. Y. Hu, “Forecasting with Artificial Neural Networks: The state of the art,” Int. J. Forecast. 14, 35–62 (1998).
    [Crossref]
  23. A. C. Adrian and L. Ofer, “ANN z: Estimating Photometric Redshifts Using Artificial Neural Networks,” Publ. Astron. Soc. Pac. 116(818), 345–351 (2004).
    [Crossref]
  24. S. Rajasekaran and G. A. V. Pai, Neural Network, Fuzzy Logic, and Genetic Algorithms - Synthesis and Applications (Prentice Hall, 2005)
  25. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature 323(6088), 533–536 (1986).
    [Crossref]
  26. J. Li, J. Cheng, J. Shi, and F. Huang, “Brief Introduction of Back Propagation (BP) Neural Network Algorithm and Its Improvements,” Proc. AISC169, 553–558 (2012).
    [Crossref]
  27. M. Niklès, L. Thévenaz, and P. A. Robert, “Brillouin gain spectrum characterization in single-mode optical fibers,” J. Lightwave Technol. 15(10), 1842–1851 (1997).
    [Crossref]
  28. X. Bao, A. Brown, M. Demerchant, and J. Smith, “Characterization of the Brillouin-loss spectrum of single-mode fibers by use of very short (<10-ns) pulses,” Opt. Lett. 24(8), 510–512 (1999).
    [Crossref] [PubMed]

2015 (1)

A. K. Azad, L. Wang, N. Guo, C. Lu, and H. Y. Tam, “Temperature sensing in BOTDA system by using artificial neural network,” Electron. Lett. 51(20), 1578–1580 (2015).
[Crossref]

2013 (2)

M. A. Farahani, E. Castillo-Guerra, and B. G. Colpitts, “A Detailed Evaluation of the Correlation-Based Method Used for Estimation of the Brillouin Frequency Shift in BOTDA Sensors,” IEEE Sens. J. 13(12), 4589–4598 (2013).
[Crossref]

M. A. Soto and L. Thévenaz, “Modeling and evaluating the performance of Brillouin distributed optical fiber sensors,” Opt. Express 21(25), 31347–31366 (2013).
[Crossref] [PubMed]

2012 (5)

F. N. Khan, Y. Zhou, A. P. T. Lau, and C. Lu, “Modulation format identification in heterogeneous fiber-optic networks using artificial neural networks,” Opt. Express 20(11), 12422–12431 (2012).
[Crossref] [PubMed]

C. A. Galindez-Jamioy and J. M. Lopez-Higuera, “Brillouin Distributed Fiber Sensors: An Overview and Applications,” J. of Sensors 204121, 17 (2012).

X. Bao and L. Chen, “Recent Progress in Distributed Fiber Optic Sensors,” Sensors (Basel) 12(12), 8601–8639 (2012).
[Crossref] [PubMed]

Y. Mao, N. Guo, K. L. Yu, H. Y. Tam, and C. Lu, “1-cm-Spatial-Resolution Brillouin Optical Time-Domain Analysis Based on Bright Pulse Brillouin Gain and Complementary Code,” IEEE Ph. J. 4(6), 2242–2248 (2012).

K. Abhishek, M. P. Singh, S. Ghosh, and A. Anand, “Weather Forcasting models using Artificial Neural Network,” Procedia Tech. 4, 311–318 (2012).
[Crossref]

2011 (3)

2010 (3)

2009 (1)

M. L. Hafiane, Z. Dibi, and O. Manck, “On the Capability of Artificial Neural Networks to Compensate Nonlinearities in Wavelength Sensing,” Sensors (Basel) 9(4), 2884–2894 (2009).
[Crossref] [PubMed]

2008 (1)

C. Zhang, Y. Yang, and A. Li, “Application of Levenberg–Marquardt algorithm in the Brillouin spectrum fitting,” Proc. SPIE 7129, 71291Y (2008).
[Crossref]

2004 (1)

A. C. Adrian and L. Ofer, “ANN z: Estimating Photometric Redshifts Using Artificial Neural Networks,” Publ. Astron. Soc. Pac. 116(818), 345–351 (2004).
[Crossref]

2000 (1)

S. Schlamp, H. G. Hornung, and E. B. Cummings, “Neural network data analysis for laser-induced thermal acoustics,” Meas. Sci. Technol. 11(6), 784–794 (2000).
[Crossref]

1999 (1)

1998 (2)

G. Zhang, B. E. Patuwo, and M. Y. Hu, “Forecasting with Artificial Neural Networks: The state of the art,” Int. J. Forecast. 14, 35–62 (1998).
[Crossref]

C. N. Pannell, J. Dhliwayo, and D. J. Webb, “The accuracy of parameter estimation from noisy data, with application to resonance peak estimation in distributed Brillouin sensing,” Meas. Sci. Technol. 9(1), 50–57 (1998).
[Crossref]

1997 (1)

M. Niklès, L. Thévenaz, and P. A. Robert, “Brillouin gain spectrum characterization in single-mode optical fibers,” J. Lightwave Technol. 15(10), 1842–1851 (1997).
[Crossref]

1986 (1)

D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature 323(6088), 533–536 (1986).
[Crossref]

Abhishek, K.

K. Abhishek, M. P. Singh, S. Ghosh, and A. Anand, “Weather Forcasting models using Artificial Neural Network,” Procedia Tech. 4, 311–318 (2012).
[Crossref]

Adrian, A. C.

A. C. Adrian and L. Ofer, “ANN z: Estimating Photometric Redshifts Using Artificial Neural Networks,” Publ. Astron. Soc. Pac. 116(818), 345–351 (2004).
[Crossref]

Anand, A.

K. Abhishek, M. P. Singh, S. Ghosh, and A. Anand, “Weather Forcasting models using Artificial Neural Network,” Procedia Tech. 4, 311–318 (2012).
[Crossref]

Azad, A. K.

A. K. Azad, L. Wang, N. Guo, C. Lu, and H. Y. Tam, “Temperature sensing in BOTDA system by using artificial neural network,” Electron. Lett. 51(20), 1578–1580 (2015).
[Crossref]

Bao, X.

Bolognini, G.

Brown, A.

Castillo-Guerra, E.

M. A. Farahani, E. Castillo-Guerra, and B. G. Colpitts, “A Detailed Evaluation of the Correlation-Based Method Used for Estimation of the Brillouin Frequency Shift in BOTDA Sensors,” IEEE Sens. J. 13(12), 4589–4598 (2013).
[Crossref]

M. A. Farahani, E. Castillo-Guerra, and B. G. Colpitts, “Accurate estimation of Brillouin frequency shift in Brillouin optical time domain analysis sensors using cross correlation,” Opt. Lett. 36(21), 4275–4277 (2011).
[Crossref] [PubMed]

Chen, L.

X. Bao and L. Chen, “Recent Progress in Distributed Fiber Optic Sensors,” Sensors (Basel) 12(12), 8601–8639 (2012).
[Crossref] [PubMed]

Y. Dong, L. Chen, and X. Bao, “Time-division multiplexing-based BOTDA over 100 km sensing length,” Opt. Lett. 36(2), 277–279 (2011).
[Crossref] [PubMed]

Colpitts, B. G.

M. A. Farahani, E. Castillo-Guerra, and B. G. Colpitts, “A Detailed Evaluation of the Correlation-Based Method Used for Estimation of the Brillouin Frequency Shift in BOTDA Sensors,” IEEE Sens. J. 13(12), 4589–4598 (2013).
[Crossref]

M. A. Farahani, E. Castillo-Guerra, and B. G. Colpitts, “Accurate estimation of Brillouin frequency shift in Brillouin optical time domain analysis sensors using cross correlation,” Opt. Lett. 36(21), 4275–4277 (2011).
[Crossref] [PubMed]

Cummings, E. B.

S. Schlamp, H. G. Hornung, and E. B. Cummings, “Neural network data analysis for laser-induced thermal acoustics,” Meas. Sci. Technol. 11(6), 784–794 (2000).
[Crossref]

Demerchant, M.

Dhliwayo, J.

C. N. Pannell, J. Dhliwayo, and D. J. Webb, “The accuracy of parameter estimation from noisy data, with application to resonance peak estimation in distributed Brillouin sensing,” Meas. Sci. Technol. 9(1), 50–57 (1998).
[Crossref]

Di Pasquale, F.

Dibi, Z.

M. L. Hafiane, Z. Dibi, and O. Manck, “On the Capability of Artificial Neural Networks to Compensate Nonlinearities in Wavelength Sensing,” Sensors (Basel) 9(4), 2884–2894 (2009).
[Crossref] [PubMed]

Dong, Y.

Farahani, M. A.

M. A. Farahani, E. Castillo-Guerra, and B. G. Colpitts, “A Detailed Evaluation of the Correlation-Based Method Used for Estimation of the Brillouin Frequency Shift in BOTDA Sensors,” IEEE Sens. J. 13(12), 4589–4598 (2013).
[Crossref]

M. A. Farahani, E. Castillo-Guerra, and B. G. Colpitts, “Accurate estimation of Brillouin frequency shift in Brillouin optical time domain analysis sensors using cross correlation,” Opt. Lett. 36(21), 4275–4277 (2011).
[Crossref] [PubMed]

Galindez, C. A.

C. A. Galindez, A. Quintela, M. A. Quintela, and J. M. Lopez-Higuera, “30cm of spatial resolution using pre-excitation pulse BOTDA technique,” Proc. SPIE 7753, 77531–77534 (2011).
[Crossref]

Galindez-Jamioy, C. A.

C. A. Galindez-Jamioy and J. M. Lopez-Higuera, “Brillouin Distributed Fiber Sensors: An Overview and Applications,” J. of Sensors 204121, 17 (2012).

Ghosh, S.

K. Abhishek, M. P. Singh, S. Ghosh, and A. Anand, “Weather Forcasting models using Artificial Neural Network,” Procedia Tech. 4, 311–318 (2012).
[Crossref]

Guo, N.

A. K. Azad, L. Wang, N. Guo, C. Lu, and H. Y. Tam, “Temperature sensing in BOTDA system by using artificial neural network,” Electron. Lett. 51(20), 1578–1580 (2015).
[Crossref]

Y. Mao, N. Guo, K. L. Yu, H. Y. Tam, and C. Lu, “1-cm-Spatial-Resolution Brillouin Optical Time-Domain Analysis Based on Bright Pulse Brillouin Gain and Complementary Code,” IEEE Ph. J. 4(6), 2242–2248 (2012).

Hafiane, M. L.

M. L. Hafiane, Z. Dibi, and O. Manck, “On the Capability of Artificial Neural Networks to Compensate Nonlinearities in Wavelength Sensing,” Sensors (Basel) 9(4), 2884–2894 (2009).
[Crossref] [PubMed]

He, Z.

Hinton, G. E.

D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature 323(6088), 533–536 (1986).
[Crossref]

Hornung, H. G.

S. Schlamp, H. G. Hornung, and E. B. Cummings, “Neural network data analysis for laser-induced thermal acoustics,” Meas. Sci. Technol. 11(6), 784–794 (2000).
[Crossref]

Hotate, K.

Hu, M. Y.

G. Zhang, B. E. Patuwo, and M. Y. Hu, “Forecasting with Artificial Neural Networks: The state of the art,” Int. J. Forecast. 14, 35–62 (1998).
[Crossref]

Khan, F. N.

Lau, A. P. T.

Li, A.

C. Zhang, Y. Yang, and A. Li, “Application of Levenberg–Marquardt algorithm in the Brillouin spectrum fitting,” Proc. SPIE 7129, 71291Y (2008).
[Crossref]

Li, C.

C. Li and Y. Li, “Fitting of Brillouin spectrum based on LabVIEW,” in Proc. 5th Int. Conf. Wireless Commun., Netw. Mobile Comput. 1–4 Sep. 2009.
[Crossref]

Li, Y.

C. Li and Y. Li, “Fitting of Brillouin spectrum based on LabVIEW,” in Proc. 5th Int. Conf. Wireless Commun., Netw. Mobile Comput. 1–4 Sep. 2009.
[Crossref]

Lopez-Higuera, J. M.

C. A. Galindez-Jamioy and J. M. Lopez-Higuera, “Brillouin Distributed Fiber Sensors: An Overview and Applications,” J. of Sensors 204121, 17 (2012).

C. A. Galindez, A. Quintela, M. A. Quintela, and J. M. Lopez-Higuera, “30cm of spatial resolution using pre-excitation pulse BOTDA technique,” Proc. SPIE 7753, 77531–77534 (2011).
[Crossref]

Lu, C.

A. K. Azad, L. Wang, N. Guo, C. Lu, and H. Y. Tam, “Temperature sensing in BOTDA system by using artificial neural network,” Electron. Lett. 51(20), 1578–1580 (2015).
[Crossref]

Y. Mao, N. Guo, K. L. Yu, H. Y. Tam, and C. Lu, “1-cm-Spatial-Resolution Brillouin Optical Time-Domain Analysis Based on Bright Pulse Brillouin Gain and Complementary Code,” IEEE Ph. J. 4(6), 2242–2248 (2012).

F. N. Khan, Y. Zhou, A. P. T. Lau, and C. Lu, “Modulation format identification in heterogeneous fiber-optic networks using artificial neural networks,” Opt. Express 20(11), 12422–12431 (2012).
[Crossref] [PubMed]

Manck, O.

M. L. Hafiane, Z. Dibi, and O. Manck, “On the Capability of Artificial Neural Networks to Compensate Nonlinearities in Wavelength Sensing,” Sensors (Basel) 9(4), 2884–2894 (2009).
[Crossref] [PubMed]

Mao, Y.

Y. Mao, N. Guo, K. L. Yu, H. Y. Tam, and C. Lu, “1-cm-Spatial-Resolution Brillouin Optical Time-Domain Analysis Based on Bright Pulse Brillouin Gain and Complementary Code,” IEEE Ph. J. 4(6), 2242–2248 (2012).

Mizuno, Y.

Niklès, M.

M. Niklès, L. Thévenaz, and P. A. Robert, “Brillouin gain spectrum characterization in single-mode optical fibers,” J. Lightwave Technol. 15(10), 1842–1851 (1997).
[Crossref]

Ofer, L.

A. C. Adrian and L. Ofer, “ANN z: Estimating Photometric Redshifts Using Artificial Neural Networks,” Publ. Astron. Soc. Pac. 116(818), 345–351 (2004).
[Crossref]

Pannell, C. N.

C. N. Pannell, J. Dhliwayo, and D. J. Webb, “The accuracy of parameter estimation from noisy data, with application to resonance peak estimation in distributed Brillouin sensing,” Meas. Sci. Technol. 9(1), 50–57 (1998).
[Crossref]

Patuwo, B. E.

G. Zhang, B. E. Patuwo, and M. Y. Hu, “Forecasting with Artificial Neural Networks: The state of the art,” Int. J. Forecast. 14, 35–62 (1998).
[Crossref]

Quintela, A.

C. A. Galindez, A. Quintela, M. A. Quintela, and J. M. Lopez-Higuera, “30cm of spatial resolution using pre-excitation pulse BOTDA technique,” Proc. SPIE 7753, 77531–77534 (2011).
[Crossref]

Quintela, M. A.

C. A. Galindez, A. Quintela, M. A. Quintela, and J. M. Lopez-Higuera, “30cm of spatial resolution using pre-excitation pulse BOTDA technique,” Proc. SPIE 7753, 77531–77534 (2011).
[Crossref]

Robert, P. A.

M. Niklès, L. Thévenaz, and P. A. Robert, “Brillouin gain spectrum characterization in single-mode optical fibers,” J. Lightwave Technol. 15(10), 1842–1851 (1997).
[Crossref]

Rumelhart, D. E.

D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature 323(6088), 533–536 (1986).
[Crossref]

Schlamp, S.

S. Schlamp, H. G. Hornung, and E. B. Cummings, “Neural network data analysis for laser-induced thermal acoustics,” Meas. Sci. Technol. 11(6), 784–794 (2000).
[Crossref]

Singh, M. P.

K. Abhishek, M. P. Singh, S. Ghosh, and A. Anand, “Weather Forcasting models using Artificial Neural Network,” Procedia Tech. 4, 311–318 (2012).
[Crossref]

Smith, J.

Soto, M. A.

Tam, H. Y.

A. K. Azad, L. Wang, N. Guo, C. Lu, and H. Y. Tam, “Temperature sensing in BOTDA system by using artificial neural network,” Electron. Lett. 51(20), 1578–1580 (2015).
[Crossref]

Y. Mao, N. Guo, K. L. Yu, H. Y. Tam, and C. Lu, “1-cm-Spatial-Resolution Brillouin Optical Time-Domain Analysis Based on Bright Pulse Brillouin Gain and Complementary Code,” IEEE Ph. J. 4(6), 2242–2248 (2012).

Thévenaz, L.

M. A. Soto and L. Thévenaz, “Modeling and evaluating the performance of Brillouin distributed optical fiber sensors,” Opt. Express 21(25), 31347–31366 (2013).
[Crossref] [PubMed]

L. Thévenaz, “Brillouin distributed time-domain sensing in optical fibers: state of the art and perspectives,” Front. Optoelectron 3(1), 13–21 (2010).
[Crossref]

M. A. Soto, G. Bolognini, F. Di Pasquale, and L. Thévenaz, “Simplex-coded BOTDA fiber sensor with 1 m spatial resolution over a 50 km range,” Opt. Lett. 35(2), 259–261 (2010).
[Crossref] [PubMed]

M. Niklès, L. Thévenaz, and P. A. Robert, “Brillouin gain spectrum characterization in single-mode optical fibers,” J. Lightwave Technol. 15(10), 1842–1851 (1997).
[Crossref]

Wang, L.

A. K. Azad, L. Wang, N. Guo, C. Lu, and H. Y. Tam, “Temperature sensing in BOTDA system by using artificial neural network,” Electron. Lett. 51(20), 1578–1580 (2015).
[Crossref]

Webb, D. J.

C. N. Pannell, J. Dhliwayo, and D. J. Webb, “The accuracy of parameter estimation from noisy data, with application to resonance peak estimation in distributed Brillouin sensing,” Meas. Sci. Technol. 9(1), 50–57 (1998).
[Crossref]

Williams, R. J.

D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature 323(6088), 533–536 (1986).
[Crossref]

Yang, Y.

C. Zhang, Y. Yang, and A. Li, “Application of Levenberg–Marquardt algorithm in the Brillouin spectrum fitting,” Proc. SPIE 7129, 71291Y (2008).
[Crossref]

Yu, K. L.

Y. Mao, N. Guo, K. L. Yu, H. Y. Tam, and C. Lu, “1-cm-Spatial-Resolution Brillouin Optical Time-Domain Analysis Based on Bright Pulse Brillouin Gain and Complementary Code,” IEEE Ph. J. 4(6), 2242–2248 (2012).

Zhang, C.

C. Zhang, Y. Yang, and A. Li, “Application of Levenberg–Marquardt algorithm in the Brillouin spectrum fitting,” Proc. SPIE 7129, 71291Y (2008).
[Crossref]

Zhang, G.

G. Zhang, B. E. Patuwo, and M. Y. Hu, “Forecasting with Artificial Neural Networks: The state of the art,” Int. J. Forecast. 14, 35–62 (1998).
[Crossref]

Zhou, Y.

Zou, W.

Electron. Lett. (1)

A. K. Azad, L. Wang, N. Guo, C. Lu, and H. Y. Tam, “Temperature sensing in BOTDA system by using artificial neural network,” Electron. Lett. 51(20), 1578–1580 (2015).
[Crossref]

Front. Optoelectron (1)

L. Thévenaz, “Brillouin distributed time-domain sensing in optical fibers: state of the art and perspectives,” Front. Optoelectron 3(1), 13–21 (2010).
[Crossref]

IEEE Ph. J. (1)

Y. Mao, N. Guo, K. L. Yu, H. Y. Tam, and C. Lu, “1-cm-Spatial-Resolution Brillouin Optical Time-Domain Analysis Based on Bright Pulse Brillouin Gain and Complementary Code,” IEEE Ph. J. 4(6), 2242–2248 (2012).

IEEE Sens. J. (1)

M. A. Farahani, E. Castillo-Guerra, and B. G. Colpitts, “A Detailed Evaluation of the Correlation-Based Method Used for Estimation of the Brillouin Frequency Shift in BOTDA Sensors,” IEEE Sens. J. 13(12), 4589–4598 (2013).
[Crossref]

Int. J. Forecast. (1)

G. Zhang, B. E. Patuwo, and M. Y. Hu, “Forecasting with Artificial Neural Networks: The state of the art,” Int. J. Forecast. 14, 35–62 (1998).
[Crossref]

J. Lightwave Technol. (2)

M. Niklès, L. Thévenaz, and P. A. Robert, “Brillouin gain spectrum characterization in single-mode optical fibers,” J. Lightwave Technol. 15(10), 1842–1851 (1997).
[Crossref]

Y. Mizuno, W. Zou, Z. He, and K. Hotate, “Operation of Brillouin optical correlation-domain reflectometry: theoretical analysis and experimental validation,” J. Lightwave Technol. 28(22), 3300–3306 (2010).

J. of Sensors (1)

C. A. Galindez-Jamioy and J. M. Lopez-Higuera, “Brillouin Distributed Fiber Sensors: An Overview and Applications,” J. of Sensors 204121, 17 (2012).

Meas. Sci. Technol. (2)

C. N. Pannell, J. Dhliwayo, and D. J. Webb, “The accuracy of parameter estimation from noisy data, with application to resonance peak estimation in distributed Brillouin sensing,” Meas. Sci. Technol. 9(1), 50–57 (1998).
[Crossref]

S. Schlamp, H. G. Hornung, and E. B. Cummings, “Neural network data analysis for laser-induced thermal acoustics,” Meas. Sci. Technol. 11(6), 784–794 (2000).
[Crossref]

Nature (1)

D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature 323(6088), 533–536 (1986).
[Crossref]

Opt. Express (2)

Opt. Lett. (4)

Proc. SPIE (2)

C. Zhang, Y. Yang, and A. Li, “Application of Levenberg–Marquardt algorithm in the Brillouin spectrum fitting,” Proc. SPIE 7129, 71291Y (2008).
[Crossref]

C. A. Galindez, A. Quintela, M. A. Quintela, and J. M. Lopez-Higuera, “30cm of spatial resolution using pre-excitation pulse BOTDA technique,” Proc. SPIE 7753, 77531–77534 (2011).
[Crossref]

Procedia Tech. (1)

K. Abhishek, M. P. Singh, S. Ghosh, and A. Anand, “Weather Forcasting models using Artificial Neural Network,” Procedia Tech. 4, 311–318 (2012).
[Crossref]

Publ. Astron. Soc. Pac. (1)

A. C. Adrian and L. Ofer, “ANN z: Estimating Photometric Redshifts Using Artificial Neural Networks,” Publ. Astron. Soc. Pac. 116(818), 345–351 (2004).
[Crossref]

Sensors (Basel) (2)

M. L. Hafiane, Z. Dibi, and O. Manck, “On the Capability of Artificial Neural Networks to Compensate Nonlinearities in Wavelength Sensing,” Sensors (Basel) 9(4), 2884–2894 (2009).
[Crossref] [PubMed]

X. Bao and L. Chen, “Recent Progress in Distributed Fiber Optic Sensors,” Sensors (Basel) 12(12), 8601–8639 (2012).
[Crossref] [PubMed]

Other (5)

A. K. Azad, L. Wang, N. Guo, C. Lu, and H. Y. Tam, “Temperature profile extraction using artificial neural network in BOTDA sensor system,” in The 20th Optoelectronics and Communications Conference (OECC), Shanghai, China, Jun.2015, paper 1570099759.
[Crossref]

C. Li and Y. Li, “Fitting of Brillouin spectrum based on LabVIEW,” in Proc. 5th Int. Conf. Wireless Commun., Netw. Mobile Comput. 1–4 Sep. 2009.
[Crossref]

R. Ruiz-Lomber, J. M. Serrano, and J. M. Lopez-Higuera, “Automatic strain detection in a Brillouin Optical Time Domain sensor using Principal Component Analysis and Artificial Neural Networks,” Sensors (IEEE, 2014), pp. 1539–1542.

S. Rajasekaran and G. A. V. Pai, Neural Network, Fuzzy Logic, and Genetic Algorithms - Synthesis and Applications (Prentice Hall, 2005)

J. Li, J. Cheng, J. Shi, and F. Huang, “Brief Introduction of Back Propagation (BP) Neural Network Algorithm and Its Improvements,” Proc. AISC169, 553–558 (2012).
[Crossref]

Cited By

OSA participates in Crossref's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (15)

Fig. 1
Fig. 1 BOTDA experiment setup, PC: Polarization Controller, EOM: Electro-Optic Modulator, RF: Radio Frequency, EDFA: Erbium-doped Fiber Amplifier, PS: Polarization Scrambler, PD: Photodetector, FBG: Fiber Bragg Grating, FUT: Fiber under test.
Fig. 2
Fig. 2 A typical feed-forward artificial neural network (ANN) with one hidden layer.
Fig. 3
Fig. 3 Two independent phases in using ANN to extract temperature information from BGSs.
Fig. 4
Fig. 4 (a) Averaged BGS for room temperature (~21 °C) to 65 °C and (b) BFS vs temperature.
Fig. 5
Fig. 5 Illustration of LCF and XCM. gu(ν) is an unknown noisy spectrum and gL(ν) is fitted by LCF technique. gr(ν) is an ideal Lorentzian curve and gc(ν) is obtained by cross-correlating gr(ν) and gu(ν).
Fig. 6
Fig. 6 (a) Normalized BGS along 41 km fiber with the last 50 m heated at 39.14 °C. The frequency scanning step is 1 MHz. (b) The BOTDA trace obtained at 10.84 GHz along the fiber. The inset shows the spatial resolution of ~4 m.
Fig. 7
Fig. 7 ANN determined temperature distribution along 41 km long FUT whose last 50 m is heated at (a) room temperature (~21 °C), (b) 29.90°C, (c) 39.14°C and (d) 48.63 °C; inset: temperature distribution within a range of last 100m from 40.9 km to 41 km.
Fig. 8
Fig. 8 LCF determined temperature distribution along 41 km long FUT whose last 50 m is heated at (a) room temperature (~21 °C), (b) 29.90°C, (c) 39.14°C and (d) 48.63 °C; inset: temperature distribution within a range of last 100m from 40.9 km to 41 km.
Fig. 9
Fig. 9 XCM determined temperature distribution along 41 km long FUT whose last 50 m is heated at (a) room temperature (~21 °C), (b) 29.90°C, (c) 39.14°C and (d) 48.63 °C; inset: temperature distribution within a range of last 100m from 40.9 km to 41 km.
Fig. 10
Fig. 10 Absolute Error distribution along the last 50 m FUT calculated using (a) ANN, (b) LCF and (c) XCM for a temperature of 29.90 °C.
Fig. 11
Fig. 11 Absolute Error distribution along the last 50 m FUT calculated using (a) ANN, (b) LCF and (c) XCM for a temperature of 39.14 °C.
Fig. 12
Fig. 12 Absolute Error distribution along the last 50 m FUT calculated using (a) ANN, (b) LCF and (c) XCM for a temperature of 48.63 °C.
Fig. 13
Fig. 13 RMSE for the last 50 m fiber heated to (a) 29.90 °C, (b) 39.14 °C and (c) 48.63 °C. At each frequency scanning step, the measured temperatures are obtained using ANN, LCF and XCM, respectively.
Fig. 14
Fig. 14 Standard deviation (SD) along the fiber computed within (a) every 50 m from 39 km to 40 km for the case of 1 MHz frequency scanning step, and (b) 50 m from 39.95 km to 40 km for cases of different frequency scanning steps.
Fig. 15
Fig. 15 Ratio of running time between (a) LCF and ANN and (b) XCM and ANN to process 10000 BGSs along the FUT after data acquisition.

Equations (3)

Equations on this page are rendered with MathJax. Learn more.

g(υ)= g B 1+ [(υ υ B )/(Δ υ B /2)] 2
υ B (T,ε)= C T ΔT+ C ε Δε+ υ B ( T o , ε o )
i=1 N [g( υ i )g( υ i ,P ) ] 2

Metrics