Abstract
Optical fiber pre-warning system (OFPS) is widely utilized in pipeline transport fields. The intrusions of OFPS need to be located. In this system, the original signals consist of noises, interferences, and intrusion signals. Here, noises are background and harmless interferences possessing with high power, and the intrusion signals are the main target of detection in this system. Hence, the study stresses on extracting the intrusion signals from the total ones. The proposed method can be divided into two parts, constant false alarm rate (CFAR) and dilation and erosion (DE). The former is applied to eliminate noises, and the latter is to remove interferences. According to some researches, the feature of noise background accords with the CFAR spatial detection. Furthermore, the detection results after CFAR can be presented as a binary image of time and space. Besides, interferences are relatively disconnected. Consequently, they can be eliminated by DE which is introduced from the image processing. To sum up, this novel method is based on CFAR and DE which can eliminate noises and interferences effectively. Moreover, it performs a brilliant detection performance. A series of tests were developed in Men Tou Gou of Beijing, China, and the reliability of proposed method can be verified by these tests.
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References
W. Liang, L. Lu, and L. Zhang, “Coupling relations and early-warning for ‘equipment chain’ in long-distance pipeline,” Mechanical Systems and Signal Processing, 2013, 41(1): 335–347.
J. Kang and Z. H Zou, “Time prediction model for pipeline leakage based on grey relational analysis,” Physics Procedia, 2010, 25(2): 2019–2024.
T. Zhang, Y. Tan, H. Yang, J. Zhao, and X. Zhang, “Locating gas pipeline leakage based on stimulus-response method,” Energy Procedia, 2014, 61: 207–210.
Q. Fu, H. J. Wang, and F. Qiu, “Pipeline leak detection based on fiber optic early-warning system,” Procedia Engineering, 2010, 7: 88–93.
Z. Qu, S. Jin, and Y. Zhou, “Study on the distributed optical fiber pipeline leakage pre-warning system and the method of signal analysis,” in Proceedings of the 6th International Pipeline Conference, Calgary, Canada, pp. 677–681, 2006.
W. Liang, L. Zhang, Q. Xu, and C. Yan, “Gas pipeline leakage detection based on acoustic technology,” Engineering Failure Analysis, 2013, 31(6): 1–7.
Z. Qu, H. Feng, Z. Zeng, J. Zhuge, and S. Jin, “A SVM-based pipeline leakage detection and pre-warning system,” Measurement, 2010, 43(4): 513–519.
Q. Lv, L. Li, H. Wang, Q. Li, and X. Zhong, “Influences of laser on fiber-optical distributed disturbance sensor based on-OTDR,” Infrared and Laser Engineering, 2014,12(43): 3919–3923.
H. F. Martins, S. Martin-Lopez, P. Corredera, and M. L. Filograno, “Coherent noise reduction in high visibility phase-Sensitive optical time domain reflectometer for distributed sensing of ultrasonic waves,” Journal of Lightwave Technology, 2013, 31(23): 3631–3637.
Q. Li, C. Zhang, L. Li, and X. Zhong, “Localization mechanisms and location methods of the disturbance sensor based on phase-sensitive OTDR,” Optik-Interntional Journal for Light and Electron Optics, 2014, 125(9): 2099–2103.
Q. Lin, C. Zhang, and C. Li, “Fiber-optic distributed sensor based on phase-sensitive OTDR and wavelet packet transform for multiple disturbances location,” Optik-Interntional Journal for Light and Electron Optics, 2014, 125(24): 7235–7238.
A. R. Bahrampour and F. Maaoumi, “Resolution enhancement in long pulse OTDR for application in structural health monitoring,” Optical Fiber Technology, 2010, 16(24): 240–249.
L. Lu, Y. Song, X. Zhang, and F. Zhu, “Frequency division multiplexing OTDR with fast signal processing,” Optics and Laser Technology, 2012, 44(7): 2206–2209.
Z. Qin, “Distributed optical fiber vibration sensor based on Rayleigh backscattering,” Ph.D. dissertation, University of Ottawa, Ottawa, 2013.
H. Rohling, “Radar CFAR thresholding in clutter and multiple target situations,” IEEE Transactions on Aerospace and Electronic Systems, 1983, AES-19(4): 608–621.
M. E. Smith and P. K. Varshney, “Intelligent CFAR processor based on data variability,” IEEE Transactions on Aerospace and Electronic Systems, 2000, 36(3): 837–847.
S. Ward, M. Bélanger, D. Donovan, A. Horsman, and N. Carrier, “Automatic censored CFAR detection for nonhomogeneous environments,” IEEE Transactions on Aerospace and Electronic Systems, 1992, 28(1): 286–304.
R. Zhang, W. Sheng, and X Ma, “Improved switching CFAR detector for non-homogeneous environments,” Signal Processing, 2013, 93(1): 35–48.
G. V. Weinberg, “Management of interference in Pareto CFAR processes using adaptive test cell analysis,” Signal Processing, 2014, 104: 264–273.
B. Shi, C. Hao, C. Hou, X. Ma, and C Peng, “Parametric Rao test for multichannel adaptive detection of range-spread target in partially homogeneous environments,” Signal Processing 2015, 108: 421–429.
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Qiu, Z., Zheng, T., Qu, H. et al. A new detection method based on CFAR and DE for OFPS. Photonic Sens 6, 261–267 (2016). https://doi.org/10.1007/s13320-016-0342-8
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DOI: https://doi.org/10.1007/s13320-016-0342-8