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.
[1] P. Healey, “Review of long wavelength single-mode optical fiber
reflectometry techniques,” Journal of lightwave technology, vol. 3, no. 4,
pp. 876–886, 1985.
[2] 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.
[3] M. Barnoski, M. Rourke, S. Jensen, and R. Melville, “Optical time
domain reflectometer,” Applied optics, vol. 16, no. 9, pp. 2375–2379,
1977.
[4] 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.
[5] 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.
[6] M. D. Jones, “Using simplex codes to improve otdr sensitivity,” IEEE
Photonics Technology Letters, vol. 5, no. 7, pp. 822–824, 1993.
[7] 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.
[8] ——, “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.
[9] 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.
[10] J. Moura, “Detection and characterisation of events with an otdr.”
[11] 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.
[12] 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.
[13] 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.
[14] 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.
[15] 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.
[16] 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.
[17] 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.
[18] 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.
[19] 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.
[20] 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.
[21] 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.
[22] 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.
[23] 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.
[24] T. Kailath, Modern signal processing, 1985. [25] 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.
[26] 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.
[1] P. Healey, “Review of long wavelength single-mode optical fiber
reflectometry techniques,” Journal of lightwave technology, vol. 3, no. 4,
pp. 876–886, 1985.
[2] 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.
[3] M. Barnoski, M. Rourke, S. Jensen, and R. Melville, “Optical time
domain reflectometer,” Applied optics, vol. 16, no. 9, pp. 2375–2379,
1977.
[4] 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.
[5] 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.
[6] M. D. Jones, “Using simplex codes to improve otdr sensitivity,” IEEE
Photonics Technology Letters, vol. 5, no. 7, pp. 822–824, 1993.
[7] 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.
[8] ——, “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.
[9] 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.
[10] J. Moura, “Detection and characterisation of events with an otdr.”
[11] 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.
[12] 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.
[13] 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.
[14] 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.
[15] 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.
[16] 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.
[17] 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.
[18] 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.
[19] 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.
[20] 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.
[21] 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.
[22] 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.
[23] 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.
[24] T. Kailath, Modern signal processing, 1985. [25] 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.
[26] 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.
@article{"International Journal of Information, Control and Computer Sciences:78800", author = "Bo Liu and Qingshan Kong and Weiqing Huang", title = "Labview-Based System for Fiber Links Events Detection", abstract = "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.
", keywords = "Empirical mode decomposition (EMD), events
detection, Gabor transform, optical time domain reflectometer
(OTDR), wavelet threshold denoising.", volume = "13", number = "5", pages = "295-7", }
