Labview-Based System for Fiber Links Events Detection
With the rapid development of modern communication, diagnosing the fiber-optic quality and faults in real-time is widely focused. In this paper, a Labview-based system is proposed for fiber-optic faults detection. The wavelet threshold denoising method combined with Empirical Mode Decomposition (EMD) is applied to denoise the optical time domain reflectometer (OTDR) signal. Then the method based on Gabor representation is used to detect events. Experimental measurements show that signal to noise ratio (SNR) of the OTDR signal is improved by 1.34dB on average, compared with using the wavelet threshold denosing method. The proposed system has a high score in event detection capability and accuracy. The maximum detectable fiber length of the proposed Labview-based system can be 65km.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.3298819Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 164
 P. Healey, “Review of long wavelength single-mode optical fiber reflectometry techniques,” Journal of lightwave technology, vol. 3, no. 4, pp. 876–886, 1985.
 X. Gu and M. Sablatash, “Estimation and detection in otdr using analyzing wavelets,” in Proceedings of IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis. IEEE, 1994, pp. 353–356.
 M. Barnoski, M. Rourke, S. Jensen, and R. Melville, “Optical time domain reflectometer,” Applied optics, vol. 16, no. 9, pp. 2375–2379, 1977.
 W. Lee, J. C. Lee, S. I. Myong, and S. S. Lee, “Analysis on causes of faults and otdr waveforms for optical link management,” in 2012 International Conference on ICT Convergence (ICTC). IEEE, 2012, pp. 679–684.
 B. Friedlander and B. Porat, “Detection of transient signals by the gabor representation,” IEEE transactions on acoustics, speech, and signal processing, vol. 37, no. 2, pp. 169–180, 1989.
 M. D. Jones, “Using simplex codes to improve otdr sensitivity,” IEEE Photonics Technology Letters, vol. 5, no. 7, pp. 822–824, 1993.
 F. Liu and C. J. Zarowski, “Events in fiber optics given noisy otdr data. i. gsr/mdl method,” IEEE Transactions on Instrumentation and Measurement, vol. 50, no. 1, pp. 47–58, 2001.
 ——, “Detection and location of connection splice events in fiber optics given noisy otdr data. part ii. r1msde method,” IEEE Transactions on Instrumentation and Measurement, vol. 53, no. 2, pp. 546–556, 2004.
 Y. Kim, J. Sung, S. R. Hong, and J. Park, “Analyzing otdr measurement data using the kalman filter,” IEEE Transactions on Instrumentation and Measurement, vol. 57, no. 5, pp. 947–951, 2008.
 J. Moura, “Detection and characterisation of events with an otdr.”
 M. Usama and M. S. Sheikh, “Vector indexing algorithm for post processing of otdr data,” in Proceedings of the 2013 18th European Conference on Network and Optical Communications & 2013 8th Conference on Optical Cabling and Infrastructure (NOC-OC&I). IEEE, 2013, pp. 257–262.
 H. Chaoju and L. Jun, “The application of wavelet transform in analysis of otdr curve,” in 2010 Second International Conference on Intelligent Human-Machine Systems and Cybernetics, vol. 2. IEEE, 2010, pp. 216–219.
 H. Xiaoli, C. Houjin, and W. Changli, “One data processing method for detecting fibre events,” in Proceedings 7th International Conference on Signal Processing, 2004. Proceedings. ICSP’04. 2004., vol. 3. IEEE, 2004, pp. 2556–2559.
 X. Zhang, H. Zhao, G. Sun, and T. Cui, “Localization of non-reflective events in otdr data combining dwt with template matching,” in 2011 4th International Congress on Image and Signal Processing, vol. 4. IEEE, 2011, pp. 2275–2279.
 M. Xiaojing, D. Yi, H. Hao, and H. Weisheng, “Analysis of connection splice events in otdr data using short fourier transform method
[j],” Chinese Journal of Scientific Instrument, vol. 9, 2010.
 H. Kong, Y. Dong, Q. Zhou, W. Xie, C. Ma, and W. Hu, “Events detection in otdr data based on a method combining correlation matching with stft,” in Asia Communications and Photonics Conference. Optical Society of America, 2014, pp. ATh3A–148.
 M. A. Farahani, M. T. Wylie, E. Castillo-Guerra, and B. G. Colpitts, “Reduction in the number of averages required in botda sensors using wavelet denoising techniques,” Journal of Lightwave Technology, vol. 30, no. 8, pp. 1134–1142, 2012.
 W.-g. Hu, S.-p. Wan, B.-j. Li, L. Zhong, and W. Yu, “Study on the detection signal of otdr based on wavelet denoising and approximate entropy,” in 2012 Symposium on Photonics and Optoelectronics. IEEE, 2012, pp. 1–4.
 J. P. V. D. Weid, M. H. Souto, G. C. Amaral, and J. Garcia, “Adaptive filter for automatic identification of multiple faults in a noisy otdr profile,” Journal of Lightwave Technology, vol. 34, no. 14, pp. 3418–3424, 2016.
 H. Qiang, Z. Zhang, D. Wang, L. Lei, and X. Hou, “An otdr event analysis algorithm based on emd-based denoising and wavelet transform,” in IEEE International Conference on Electronic Measurement & Instruments, 2016.
 X. X. Liu, F. L. Han, and J. G. Wang, “Wavelet extended emd noise reduction model for signal trend extraction,” in International Congress on Image & Signal Processing, 2009.
 P. Flandrin, G. Rilling, and P. Goncalves, “Empirical mode decomposition as a filter bank,” IEEE Signal Processing Letters, vol. 11, no. 2, pp. 112–114, 2004.
 B. G. Colpitts, E. Castilloguerra, M. T. V. Wylie, and M. A. Farahani, “Reduction in the number of averages required in botda sensors using wavelet denoising techniques,” Journal of Lightwave Technology, vol. 30, no. 8, pp. 1134–1142, 2012.
 T. Kailath, Modern signal processing, 1985.
 A. Janssen, “Gabor representation and wigner distribution of signals,” in ICASSP’84. IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 9. IEEE, 1984, pp. 258–261.
 P. Blanchard, J. Dubard, L. Ducos, and R. Thauvin, “Simulation method of reflectance measurement error using the otdr,” IEEE Photonics Technology Letters, vol. 10, no. 5, pp. 705–706, 1998.