Signal processing using artificial neural network for BOTDA sensor system.

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.

[1]  Mohsen Amiri Farahani,et al.  A Detailed Evaluation of the Correlation-Based Method Used for Estimation of the Brillouin Frequency Shift in BOTDA Sensors , 2013, IEEE Sensors Journal.

[2]  Liang Chen,et al.  Recent Progress in Distributed Fiber Optic Sensors , 2012, Sensors.

[3]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[4]  Zohir Dibi,et al.  On the Capability of Artificial Neural Networks to Compensate Nonlinearities in Wavelength Sensing , 2009, Sensors.

[5]  Ofer Lahav,et al.  ANNz: Estimating Photometric Redshifts Using Artificial Neural Networks , 2004 .

[6]  Chao Lu,et al.  Modulation format identification in heterogeneous fiber-optic networks using artificial neural networks. , 2012, Optics express.

[7]  Hans G. Hornung,et al.  Neural network data analysis for laser-induced thermal acoustics , 2000 .

[8]  Zuyuan He,et al.  Operation of Brillouin Optical Correlation-Domain Reflectometry: Theoretical Analysis and Experimental Validation , 2010, Journal of Lightwave Technology.

[9]  Xiaoyi Bao,et al.  Time-division multiplexing-based BOTDA over 100 km sensing length. , 2011, Optics letters.

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

[11]  Munindar P. Singh,et al.  Weather Forecasting Model using Artificial Neural Network , 2012 .

[12]  L. Thévenaz.  Brillouin distributed time-domain sensing in optical fibers: state of the art and perspectives , 2010 .

[13]  Luc Thévenaz,et al.  Modeling and evaluating the performance of Brillouin distributed optical fiber sensors. , 2013, Optics express.

[14]  L. Thévenaz,et al.  Simplex-coded BOTDA fiber sensor with 1 m spatial resolution over a 50 km range. , 2010, Optics Letters.

[15]  David J. Webb,et al.  The accuracy of parameter estimation from noisy data, with application to resonance peak estimation in distributed Brillouin sensing , 1998 .

[16]  Yuanhong Yang,et al.  Application of Levenberg-Marquardt algorithm in the Brillouin spectrum fitting , 2008, International Symposium on Instrumentation and Control Technology.

[17]  Michael Y. Hu,et al.  Forecasting with artificial neural networks: The state of the art , 1997 .

[18]  Chao Lu,et al.  Temperature sensing in BOTDA system by using artificial neural network , 2015 .

[19]  Anthony W. Brown,et al.  Characterization of the Brillouin-loss spectrum of single-mode fibers by use of very short (<10-ns) pulses. , 1999, Optics letters.

[20]  M. A. Farahani,et al.  Accurate estimation of Brillouin frequency shift in Brillouin optical time domain analysis sensors using cross correlation. , 2011, Optics letters.