Digital signal processing and application of Φ-OTDR system

Zhang Chi; Zou Ningmu; Song Jinyu; Tong Shuai; Yao Yuanyuan; Ding Zhewen; Wang Feng; Zhang Yixin; Zhang Xuping

doi:10.12086/oee.2023.220088

Article navigation > Opto-Electronic Engineering > 2023 Vol. 50 > No. 2 > 220088

Next Article Previous Article

Zhang C, Zou N M, Song J Y, et al. Digital signal processing and application of Φ-OTDR system[J]. Opto-Electron Eng, 2023, 50(2): 220088. doi: 10.12086/oee.2023.220088

Citation:

Zhang C, Zou N M, Song J Y, et al. Digital signal processing and application of Φ-OTDR system[J]. Opto-Electron Eng, 2023, 50(2): 220088. doi: 10.12086/oee.2023.220088

Digital signal processing and application of Φ-OTDR system

1.
Key Laboratory of Intelligent Optical Sensing and Manipulation, Ministry of Education, Nanjing University, Nanjing, Jiangsu 210093, China
2.
College of Engineering and Applied Sciences, Nanjing University, Nanjing, Jiangsu 210093, China
3.
Shenzhen Research Institute, Nanjing University, Shenzhen, Guangdong 581000, China
4.
Ingram School of Engineering, Texas State University, San Marcos, TX 78666, USA

Fund Project: National Natural Science Foundation of China (U2001601, 62175100, 61975076), Inner Mongolia Autonomous Region Key Technology Research Project (2019GG374), Fundamental Research Fees for Central Colleges and Universities (0213-14380202), and Shenzhen Science and Technology Innovation Fund (YFJGJS1.0).

More Information

^*Corresponding authors: zyixin@nju.edu.cn; xpzhang@nju.edu.cn

Received Date 19 May 2022

Revised Date 16 September 2022

Accepted Date 16 September 2022

Published Date 25 February 2023

Abstract

Abstract

The phase-sensitive optical time-domain reflectometry (Φ-OTDR) sensing system has the characteristics of high dynamic response and high sensitivity, and has great potential in the field of large-scale engineering structural health monitoring. The instrumentation level and engineering application of Φ-OTDR systems depend to a large extent on digital signal processing (DSP) technology. This paper compares and analyzes the main digital signal processing methods and technologies of Φ-OTDR systems in signal quantization, demodulation, noise suppression, and pattern recognition in recent years. The importance and method of combining digital signal processing with industry background knowledge in engineering applications are expounded, and the development status and trend of the digital signal processing methods in Φ-OTDR systems are summarized and prospected.
- phase-sensitive optical time domain reflectometry (Φ-OTDR) /
- heterodyne coherent detection /
- digital signal processing (DSP) /
- power line health monitoring

FullText(HTML)

References

[1]	张旭苹. 全分布式光纤传感技术[M]. 北京: 科学出版社, 2013. Google Scholar Zhang X P. Fully Distributed Fiber Optic Sensing Technology[M]. Beijing: Science Press, 2013 Google Scholar
[2]	张旭苹, 丁哲文, 洪瑞, 等. 相位敏感光时域反射分布式光纤传感技术[J]. 光学学报, 2021, 41(1): 0106004. doi: 10.3788/AOS202141.0106004 CrossRef Google Scholar Zhang X P, Ding Z W, Hong R, et al. Phase sensitive optical time-domain reflective distributed optical fiber sensing technology[J]. Acta Opt Sin, 2021, 41(1): 0106004. doi: 10.3788/AOS202141.0106004 CrossRef Google Scholar
[3]	Taylor H F, Lee C E. Apparatus and method for fiber optic intrusion sensing: US07/737449[P]. 1993-03-16. Google Scholar
[4]	An Y, Feng X, Li J, et al. Two-beam phase-sensitive optical time domain reflectometer based on Jones matrix modeling[J]. Opt Eng, 2013, 52(9): 094102. doi: 10.1117/1.OE.52.9.094102 CrossRef Google Scholar
[5]	He H J, Shao L Y, Li H C, et al. SNR enhancement in phase-sensitive OTDR with adaptive 2-D bilateral filtering algorithm[J]. IEEE Photonics J, 2017, 9(3): 6802610. doi: 10.1109/JPHOT.2017.2700894 CrossRef Google Scholar
[6]	He H J, Yan L S, Qian H, et al. Suppression of the interference fading in phase-sensitive OTDR with phase-shift transform[J]. J Lightw Technol, 2021, 39(1): 295−302. doi: 10.1109/JLT.2020.3023699 CrossRef Google Scholar
[7]	Shan Y Y, Ji W B, Wang Q, et al. Performance optimization for phase-sensitive OTDR sensing system based on multi-spatial resolution analysis[J]. Sensors, 2019, 19(1): 83. doi: 10.3390/s19010083 CrossRef Google Scholar
[8]	Zabihi M, Chen Y S, Zhou T, et al. Continuous fading suppression method for Φ-OTDR systems using optimum tracking over multiple probe frequencies[J]. J Lightw Technol, 2019, 37(14): 3602−3610. doi: 10.1109/JLT.2019.2918353 CrossRef Google Scholar
[9]	Zhang Y X, Xu Y M, Shan Y Y, et al. Polarization dependence of phase-sensitive optical time-domain reflectometry and its suppression method based on orthogonal-state of polarization pulse pair[J]. Opt Eng, 2016, 55(7): 074109. doi: 10.1117/1.OE.55.7.074109 CrossRef Google Scholar
[10]	单媛媛. 基于Φ-OTDR的分布式光纤振动传感系统关键技术研究[D]. 南京: 南京大学, 2019. Google Scholar Shan Y Y. The key technology research of distributed optical fiber vibration sensor based on Φ-OTDR[D]. Nanjing: Nanjing University, 2019. Google Scholar
[11]	Awwad E, Dorize C, Guerrier S, et al. Detection-localization-identification of vibrations over long distance SSMF with coherent Δϕ -OTDR[J]. J Lightw Technol, 2020, 38(12): 3089−3095. doi: 10.1109/JLT.2020.2993167 CrossRef Google Scholar
[12]	LI H, Fan C Z, Liu T, et al. Time-slot multiplexing based bandwidth enhancement for fiber distributed acoustic sensing[J]. Sci China Inf Sci, 2022, 65(1): 119303. doi: 10.1007/s11432-020-3199-x CrossRef Google Scholar
[13]	Iida D, Toge K, Manabe T. High-frequency distributed acoustic sensing faster than repetition limit with frequency-multiplexed phase-OTDR[C]//Proceedings of 2016 Optical Fiber Communications Conference and Exhibition, 2016. Google Scholar
[14]	Li S, Qin Z G, Liu Z J, et al. Long-distance Φ-OTDR with a flexible frequency response based on time division multiplexing[J]. Opt Express, 2021, 29(21): 32833−32841. doi: 10.1364/OE.435883 CrossRef Google Scholar
[15]	Liu S Q, Yu F H, Hong R, et al. Advances in phase-sensitive optical time-domain reflectometry[J]. Opto-Electron Adv, 2022, 5(3): 200078. doi: 10.29026/oea.2022.200078 CrossRef Google Scholar
[16]	吴慧娟, 刘欣雨, 饶云江. 基于Φ-OTDR的光纤分布式传感信号处理及应用[J]. 激光与光电子学进展, 2021, 58(13): 1306003. doi: 10.3788/LOP202158.1306003 CrossRef Google Scholar Wu H J, Liu X Y, Rao Y J. Processing and application of fiber optic distributed sensing signal based on Φ-OTDR[J]. Laser Optoelectron Prog, 2021, 58(13): 1306003. doi: 10.3788/LOP202158.1306003 CrossRef Google Scholar
[17]	Wang B Z, Ba D X, Chu Q, et al. High-sensitivity distributed dynamic strain sensing by combining Rayleigh and Brillouin scattering[J]. Opto-Electron Adv, 2020, 3(12): 200013. doi: 10.29026/oea.2020.200013 CrossRef Google Scholar
[18]	胡洲畅. Φ-OTDR传感技术在铁路安全监测中的应用[D]. 合肥: 中国科学技术大学, https://doi.org/10.27517/d.cnki.gzkju.2021.001637. Google Scholar Hu Z C. Φ-OTDR sensing technology application in railway safety monitoring[D]. Hefei: University of Science and Technology of China, 2021. https://doi.org/10.27517/d.cnki.gzkju.2021.001637. Google Scholar
[19]	张丽娜, 任亚玲, 林融冰, 等. 分布式光纤声波传感器及其在天然地震学研究中的应用[J]. 地球物理学进展, 2020, 35(1): 65−71. doi: 10.6038/pg2020DD0384 CrossRef Google Scholar Zhang L N, Ren Y L, Lin R B, et al. Distributed acoustic sensing system and its application for seismological studies[J]. Prog Geophys, 2020, 35(1): 65−71. doi: 10.6038/pg2020DD0384 CrossRef Google Scholar
[20]	周小慧, 陈伟, 杨江峰, 等. DAS技术在油气地球物理中的应用综述[J]. 地球物理学进展, 2021, 36(1): 338−350. doi: 10.6038/pg2021DD0472 CrossRef Google Scholar Zhou X H, Chen W, Yang J F, et al. Application review of DAS technology in oil and gas geophysics[J]. Prog Geophys, 2021, 36(1): 338−350. doi: 10.6038/pg2021DD0472 CrossRef Google Scholar
[21]	王鹏飞, 董齐, 刘昕, 等. 基于Φ-OTDR的煤层气管线外界入侵振动检测系统[J]. 传感技术学报, 2019, 32(1): 144−149. doi: 10.3969/j.issn.1004-1699.2019.01.025 CrossRef Google Scholar Wang P F, Dong Q, Liu X, et al. Coalbed methane transport pipeline intrusion detection system based on Φ-OTDR[J]. Chin J Sens Actuat, 2019, 32(1): 144−149. doi: 10.3969/j.issn.1004-1699.2019.01.025 CrossRef Google Scholar
[22]	吴庥伟, 吴慧娟, 饶云江, 等. 基于多种小波分解方法综合判决的低误报率分布式光纤围栏入侵监测系统[J]. 光子学报, 2011, 40(11): 1692−1696. doi: 10.3788/gzxb20114011.1692 CrossRef Google Scholar Wu X W, Wu H J, Rao Y J, et al. Low misstatement rate distributed optical fiber fence intrusion detection system by variety of wavelet decomposition method[J]. Acta Photonica Sin, 2011, 40(11): 1692−1696. doi: 10.3788/gzxb20114011.1692 CrossRef Google Scholar
[23]	Zhong X, Gao X C, Deng H X, et al. Pulse-width multiplexing ϕ-OTDR for nuisance-alarm rate reduction[J]. Sensors, 2018, 18(10): 3509. doi: 10.3390/s18103509 CrossRef Google Scholar
[24]	Yu X H, Zhou D L, Lu B, et al. Phase-sensitive optical time domain reflectometer for distributed fence-perimeter intrusion detection[J]. Proceedings of SPIE, 2015, 9679: 96790S. doi: 10.1117/12.2199685 CrossRef Google Scholar
[25]	王照勇, 潘政清, 叶青, 等. 用于光纤围栏入侵告警的频谱分析快速模式识别[J]. 中国激光, 2015, 42(4): 0405010. doi: 10.3788/CJL201542.0405010 CrossRef Google Scholar Wang Z Y, Pan Z Q, Ye Q, et al. Fast pattern recognition based on frequency spectrum analysis used for intrusion alarming in optical fiber fence[J]. Chin J Lasers, 2015, 42(4): 0405010. doi: 10.3788/CJL201542.0405010 CrossRef Google Scholar
[26]	何祖源, 刘庆文. 光纤分布式声波传感器原理与应用[J]. 激光与光电子学进展, 2021, 58(13): 1306001. doi: 10.3788/LOP202158.1306001 CrossRef Google Scholar He Z Y, Liu Q W. Principles and applications of optical fiber distributed acoustic sensors[J]. Laser Optoelectron Prog, 2021, 58(13): 1306001. doi: 10.3788/LOP202158.1306001 CrossRef Google Scholar
[27]	马皓钰, 王夏霄, 马福, 等. Φ-OTDR型分布式光纤声波传感器研究进展[J]. 激光与光电子学进展, 2020, 57(13): 130005. doi: 10.3788/LOP57.130005 CrossRef Google Scholar Ma H Y, Wang X X, Ma F, et al. Research progress of Φ-OTDR distributed optical fiber acoustic sensor[J]. Laser Optoelectron Prog, 2020, 57(13): 130005. doi: 10.3788/LOP57.130005 CrossRef Google Scholar
[28]	施羿, 封皓, 曾周末. Φ-OTDR型分布式全光纤传感器研究进展[J]. 自动化仪表, 2017, 38(7): 70−74,79. doi: 10.16086/j.cnki.issn1000-0380.201707018 CrossRef Google Scholar Shi Y, Feng H, Zeng Z M. Research progress of distributed optical fiber sensors based on Φ-OTDR structure[J]. Process Autom Instrum, 2017, 38(7): 70−74,79. doi: 10.16086/j.cnki.issn1000-0380.201707018 CrossRef Google Scholar
[29]	文科, 王荣. 插卡式OTDR的设计与实现[J]. 飞通光电子技术, 2003, 3(2): 118−121. Google Scholar Wen K, Wang R. Design and implementation of plug-in OTDR[J]. Photon Technol, 2003, 3(2): 118−121. Google Scholar
[30]	李辉, 李蔚, 张慧娟. 分布式光纤传感检测在DSP下的设计与实现[EB/OL]. 北京: 中国科技论文在线. [2009-11-12]. http://www.paper.edu.cn/releasepaper/content/200911-353. Google Scholar
[31]	王刚, 周伟, 李进武. 分布式光纤传感系统的全数字信号处理[J]. 光通信技术, 2009, 33(3): 18−19. doi: 10.3969/j.issn.1002-5561.2009.03.006 CrossRef Google Scholar Wang G, Zhou W, Li J W. Digital signal processing techniques in the practical distributed fiber sensor system[J]. Opt Commun Technol, 2009, 33(3): 18−19. doi: 10.3969/j.issn.1002-5561.2009.03.006 CrossRef Google Scholar
[32]	吴晨平. 基于DSP的OTDR信号处理[D]. 成都: 电子科技大学, 2007. Google Scholar Wu C P. DSP-based OTDR signal processing[D]. Chengdu: University of Electronic Science and Technology of China, 2007. Google Scholar
[33]	张旭苹, 张益昕, 王峰, 等. 相位敏感型光时域反射传感系统光学背景噪声的产生机理及其抑制方法[J]. 物理学报, 2017, 66(7): 070707. doi: 10.7498/aps.66.070707 CrossRef Google Scholar Zhang X P, Zhang Y X, Wang F, et al. The mechanism and suppression methods of optical background noise in phase-sensitive optical time domain reflectometry[J]. Acta Phys Sin, 2017, 66(7): 070707. doi: 10.7498/aps.66.070707 CrossRef Google Scholar
[34]	Gabai H, Eyal A. On the sensitivity of distributed acoustic sensing[J]. Opt Lett, 2016, 41(24): 5648−5651. doi: 10.1364/OL.41.005648 CrossRef Google Scholar
[35]	Shang Y, Yang Y H, Wang C, et al. Optical fiber distributed acoustic sensing based on the self-interference of Rayleigh backscattering[J]. Measurement, 2016, 79: 222−227. doi: 10.1016/j.measurement.2015.09.042 CrossRef Google Scholar
[36]	张宇昊. 基于分布式声场传感的地震勘探仪关键技术研究[D]. 南京: 南京大学, 2020. https://doi.org/10.27235/d.cnki.gnjiu.2020.000868. Google Scholar Zhang Y H. Research on key technologies of seismic exploration instrument based on distributed acoustic sensing[D]. Nanjing: Nanjing University, 2020. https://doi.org/10.27235/d.cnki.gnjiu.2020.000868. Google Scholar
[37]	Furukawa S, Tanaka T, Koyamada Y, et al. High dynamic range coherent OTDR for fault location in optical amplifier systems[C]//Proceedings of the 10th Anniversary. IMTC/94. Advanced Technologies in I & M. 1994 IEEE Instrumentation and Measurement Technolgy Conference, 1994. https://doi.org/10.1109/IMTC.1994.352115. Google Scholar
[38]	Haykin S S. Communication Systems[M]. New York: John Wiley & Sons, 1978. Google Scholar
[39]	Jiang F, Lu Z X, Cai F D, et al. Low computational cost distributed acoustic sensing using analog I/Q demodulation[J]. Sensors (Basel), 2019, 19(17): 3753. doi: 10.3390/s19173753 CrossRef Google Scholar
[40]	马杰, 黎敏, 吕海飞, 等. 欠采样下外差干涉系统数字正交解调法[J]. 激光与光电子学进展, 2021, 58(23): 2306002. doi: 10.3788/LOP202158.2306002 CrossRef Google Scholar Ma J, Li M, Lü H F, et al. Undersampling digital orthogonal demodulation method for heterodyne interference system[J]. Laser Optoelectron Prog, 2021, 58(23): 2306002. doi: 10.3788/LOP202158.2306002 CrossRef Google Scholar
[41]	Wang Z N, Zhang L, Wang S, et al. Coherent Φ-OTDR based on I/Q demodulation and homodyne detection[J]. Opt Express, 2016, 24(2): 853−858. doi: 10.1364/OE.24.000853 CrossRef Google Scholar
[42]	Wu Y Q, Gan J L, Li Q Y, et al. Distributed fiber voice sensor based on phase-sensitive optical time-domain reflectometry[J]. IEEE Photonics J, 2015, 7(6): 6803810. doi: 10.1109/JPHOT.2015.2499539 CrossRef Google Scholar
[43]	Fan X Y, Yang G Y, Wang S, et al. Distributed fiber-optic vibration sensing based on phase extraction from optical reflectometry[J]. J Lightw Technol, 2017, 35(16): 3281−3288. doi: 10.1109/JLT.2016.2604859 CrossRef Google Scholar
[44]	Yang G Y, Fan X Y, Wang S, et al. Long-range distributed vibration sensing based on phase extraction from phase-sensitive OTDR[J]. IEEE Photonics J, 2016, 8(3): 6802412. doi: 10.1109/JPHOT.2016.2552820 CrossRef Google Scholar
[45]	朱帆. 相位敏感型光时域反射传感系统性能增强研究[D]. 南京: 南京大学, 2015. Google Scholar Zhu F. Performance enhancement study of phase-sensitive optical time domain reflection sensing system[D]. Nanjing: Nanjing University, 2015. Google Scholar
[46]	陆存波. 基于Hilbert变换的单边带调制系统设计与实现[J]. 电子设计工程, 2016, 24(12): 138−140,145. doi: 10.3969/j.issn.1674-6236.2016.12.040 CrossRef Google Scholar Lu C B. Based on Hilbert transformation single sideband modulation system design and realization[J]. Electron Des Eng, 2016, 24(12): 138−140,145. doi: 10.3969/j.issn.1674-6236.2016.12.040 CrossRef Google Scholar
[47]	邹宁睦, 熊菲, 梁蕾, 等. 一种分布式光纤振动传感系统和解调方法: CN202110931428.8[P]. 2021-08-13. Google Scholar Zou N M, Xiong F, Liang L, et al. A distributed fiber optic vibration sensing system and demodulation method: CN202110931428.8[P]. 2021-08-13. Google Scholar
[48]	牛纪辉. 相位敏感型光时域反射传感系统的信号处理技术研究[D]. 南京: 南京大学, 2018. Google Scholar Niu J H. Research on signal processing technology of Φ-OTDR sensing system[D]. Nanjing: Nanjing University, 2018. Google Scholar
[49]	熊兴隆, 魏永兴, 张琬童, 等. 基于自适应噪声完备经验模态分解的Φ-OTDR信号去噪算法[J]. 半导体光电, 2018, 39(4): 600−606. doi: 10.16818/j.issn1001-5868.2018.04.031 CrossRef Google Scholar Xiong X L, Wei Y X, Zhang W T, et al. De-noising algorithm of Φ-OTDR signal based on complete ensemble empirical mode decomposition with adaptive noise[J]. Semicond Optoelectron, 2018, 39(4): 600−606. doi: 10.16818/j.issn1001-5868.2018.04.031 CrossRef Google Scholar
[50]	Ju Z W, Yu Z J, Hou Q K, et al. Low-noise and high-sensitivity Φ-OTDR based on an optimized dual-pulse heterodyne detection scheme[J]. Appl Opt, 2020, 59(7): 1864−1870. doi: 10.1364/AO.383303 CrossRef Google Scholar
[51]	Zhang X P, Zheng Y Y, Zhang C, et al. A fading tolerant phase-sensitive optical time domain reflectometry based on phasing-locking structure[J]. Electronics, 2021, 10(5): 535. doi: 10.3390/electronics10050535 CrossRef Google Scholar
[52]	Healey P. Fading in heterodyne OTDR[J]. Electron Lett, 1984, 20(1): 30−32. doi: 10.1049/el:19840022 CrossRef Google Scholar
[53]	Liokumovich L B, Ushakov N A, Kotov O I, et al. Fundamentals of optical fiber sensing schemes based on coherent optical time domain reflectometry: signal model under static fiber conditions[J]. J Lightw Technol, 2015, 33(17): 3660−3671. doi: 10.1109/JLT.2015.2449085 CrossRef Google Scholar
[54]	Park J, Lee W, Taylor H F. Fiber optic intrusion sensor with the configuration of an optical time-domain reflectometer using coherent interference of Rayleigh backscattering[J]. Proceedings of SPIE, 1998, 3555: 49−56. doi: 10.1117/12.318220 CrossRef Google Scholar
[55]	Zhou J, Pan Z Q, Ye Q, et al. Characteristics and explanations of interference fading of a ϕ -OTDR with a multi-frequency source[J]. J Lightw Technol, 2013, 31(17): 2947−2954. doi: 10.1109/JLT.2013.2275179 CrossRef Google Scholar
[56]	Wu H J, Xiao S K, Li X Y, et al. Separation and determination of the disturbing signals in phase-sensitive optical time domain reflectometry (Φ-OTDR)[J]. J Lightw Technol, 2015, 33(15): 3156−3162. doi: 10.1109/JLT.2015.2421953 CrossRef Google Scholar
[57]	Pang F F, He M T, Liu H H, et al. A fading-discrimination method for distributed vibration sensor using coherent detection of φ-OTDR[J]. IEEE Photonics Technol Lett, 2016, 28(23): 2752−2755. doi: 10.1109/LPT.2016.2616023 CrossRef Google Scholar
[58]	Zhang X P, Wang Q, Xiong F, et al. Performance enhancement method for phase-sensitive optical time-domain reflectometer system based on suppression of fading induced false alarms[J]. Opt Eng, 2020, 59(4): 046101. doi: 10.1117/1.OE.59.4.046101 CrossRef Google Scholar
[59]	Hartog A A, Liokumovich L B, Ushakov N A, et al. The use of multi-frequency acquisition to significantly improve the quality of fibre-optic distributed vibration sensing[C]//Proceedings of the 78th EAGE Conference and Exhibition 2016, 2016. https://doi.org/10.3997/2214-4609.201600685. Google Scholar
[60]	Hartog A H, Liokumovich L B, Ushakov N A, et al. The use of multi-frequency acquisition to significantly improve the quality of fibre-optic-distributed vibration sensing[J]. Geophys Prospect, 2018, 66(S1): 192−202. doi: 10.1111/1365-2478.12612 CrossRef Google Scholar
[61]	Zhang Y X, Liu J X, Xiong F, et al. A space-division multiplexing method for fading noise suppression in the Φ-OTDR system[J]. Sensors, 2021, 21(5): 1694. doi: 10.3390/s21051694 CrossRef Google Scholar
[62]	Juarez J C, Taylor H F. Polarization discrimination in a phase-sensitive optical time-domain reflectometer intrusion-sensor system[J]. Opt Lett, 2005, 30(24): 3284−3286. doi: 10.1364/OL.30.003284 CrossRef Google Scholar
[63]	Qin Z G, Chen L, Bao X Y. Wavelet denoising method for improving detection performance of distributed vibration sensor[J]. IEEE Photonics Technol Lett, 2012, 24(7): 542−544. doi: 10.1109/LPT.2011.2182643 CrossRef Google Scholar
[64]	孙廷玺, 徐龙海, 王升, 等. 基于偏振分集技术的分布式光纤声波传感系统[J]. 光通信技术, 2020, 44(8): 5−9. doi: 10.13921/j.cnki.issn1002-5561.2020.08.002 CrossRef Google Scholar Sun T X, Xu L H, Wang S, et al. Distributed optical fiber acoustic sensing system based on polarization diversity technology[J]. Opt Commun Technol, 2020, 44(8): 5−9. doi: 10.13921/j.cnki.issn1002-5561.2020.08.002 CrossRef Google Scholar
[65]	张旭苹, 陈晓红, 梁蕾, 等. 长距离海缆在线监测改进型C-OTDR系统[J]. 光学学报, 2021, 41(13): 1306001. doi: 10.3788/AOS202141.1306001 CrossRef Google Scholar Zhang X P, Chen X H, Liang L, et al. Enhanced C-OTDR-based online monitoring scheme for long-distance submarine cables[J]. Acta Opt Sin, 2021, 41(13): 1306001. doi: 10.3788/AOS202141.1306001 CrossRef Google Scholar
[66]	Dean T, Cuny T, Hartog A H. The effect of gauge length on axially incident P-waves measured using fibre optic distributed vibration sensing[J]. Geophys Prospect, 2017, 65(1): 184−193. doi: 10.1111/1365-2478.12419 CrossRef Google Scholar
[67]	Zhang X P, Cao L, Shan Y Y, et al. Performance optimization for a phase-sensitive optical time-domain reflectometry based on multiscale matched filtering[J]. Opt Eng, 2019, 58(5): 056114. doi: 10.1117/1.OE.58.5.056114 CrossRef Google Scholar
[68]	饶云江, 吴敏, 冉曾令, 等. 基于准分布式FBG传感器的光纤入侵报警系统[J]. 传感技术学报, 2007, 20(5): 998−1002. doi: 10.3969/j.issn.1004-1699.2007.05.011 CrossRef Google Scholar Rao Y J, Wu M, Ran Z L, et al. A fiber-optic intrusion alarm system based on quasi-distributed fbg sensors[J]. Chin J Sens Actuators, 2007, 20(5): 998−1002. doi: 10.3969/j.issn.1004-1699.2007.05.011 CrossRef Google Scholar
[69]	张颜, 娄淑琴, 梁生, 等. 基于多特征参量的φ-OTDR分布式光纤扰动传感系统模式识别研究[J]. 中国激光, 2015, 42(11): 1105005. doi: 10.3788/CJL201542.1105005 CrossRef Google Scholar Zhang Y, Lou S Q, Liang S, et al. Study of pattern recognition based on multi-characteristic parameters for φ-OTDR distributed optical fiber sensing system[J]. Chin J Lasers, 2015, 42(11): 1105005. doi: 10.3788/CJL201542.1105005 CrossRef Google Scholar
[70]	Wu H J, Liu X R, Xiao Y, et al. A dynamic time sequence recognition and knowledge mining method based on the hidden markov models (HMMs) for pipeline safety monitoring with Φ-OTDR[J]. J Lightw Technol, 2019, 37(19): 4991−5000. doi: 10.1109/JLT.2019.2926745 CrossRef Google Scholar
[71]	Zhang Y, Wang S, Hu Y Z. Research on noise reduction of Φ-OTDR signal based on blind source separation algorithm[J]. IOP Conf Ser Earth Environ Sci, 2020, 440: 022074. doi: 10.1088/1755-1315/440/2/022074 CrossRef Google Scholar
[72]	Papp A, Wiesmeyr C, Litzenberger M, et al. A real-time algorithm for train position monitoring using optical time-domain reflectometry[C]//Proceedings of 2016 IEEE International Conference on Intelligent Rail Transportation, 2016. https://doi.org/10.1109/ICIRT.2016.7588715. Google Scholar
[73]	邹东伯, 刘海, 赵亮, 等. 分布式光纤振动传感信号识别的研究[J]. 激光技术, 2016, 40(1): 86−89. doi: 10.7510/jgjs.issn.1001-3806.2016.01.019 CrossRef Google Scholar Zou D B, Liu H, Zhao L, et al. Research of signal recognition of distributed optical fiber vibration sensors[J]. Laser Technol, 2016, 40(1): 86−89. doi: 10.7510/jgjs.issn.1001-3806.2016.01.019 CrossRef Google Scholar
[74]	Tejedor J, Martins H F, Piote D, et al. Toward prevention of pipeline integrity threats using a smart fiber-optic surveillance system[J]. J Light Technol, 2016, 34(19): 4445−4453. doi: 10.1109/JLT.2016.2542981 CrossRef Google Scholar
[75]	Hu Y Z, Meng Z, Ai X B, et al. Performance enhancement of the location and recognition of a Φ-OTDR system using CEEMDAN-KL and AMNBP[J]. Appl Sci, 2020, 10(9): 3047. doi: 10.3390/app10093047 CrossRef Google Scholar
[76]	Parker T, Shatalin S, Farhadiroushan M. Distributed acoustic sensing - a new tool for seismic applications[J]. First Break, 2014, 32(2). doi: 10.3997/1365-2397.2013034. CrossRef Google Scholar
[77]	Ajo-Franklin J B, Dou S, Lindsey N J, et al. Distributed acoustic sensing using dark fiber for near-surface characterization and broadband seismic event detection[J]. Sci Rep, 2019, 9(1): 1328. doi: 10.1038/s41598-018-36675-8 CrossRef Google Scholar
[78]	周桐. 分布式声场传感系统中高性能模式识别算法的研究[D]. 南京: 南京大学, 2020. https://doi.org/10.27235/d.cnki.gnjiu.2020.001052. Google Scholar Zhou T. Research on high performance pattern recognition algorithm in distributed fiber acoustic sensing system[D]. Nanjing: Nanjing University, 2020. https://doi.org/10.27235/d.cnki.gnjiu.2020.001052. Google Scholar
[79]	张润, 王永滨. 机器学习及其算法和发展研究[J]. 中国传媒大学学报(自然科学版), 2016, 23(2): 10−18,24. doi: 10.3969/j.issn.1673-4793.2016.02.002 CrossRef Google Scholar Zhang R, Wang Y B. Research on machine learning with algorithm and development[J]. J Commun Univ China (Sci Technol), 2016, 23(2): 10−18,24. doi: 10.3969/j.issn.1673-4793.2016.02.002 CrossRef Google Scholar
[80]	张俊楠, 娄淑琴, 梁生. 基于SVM算法的φ-OTDR分布式光纤扰动传感系统模式识别研究[J]. 红外与激光工程, 2017, 46(4): 0422003. doi: 10.3788/IRLA201746.0422003 CrossRef Google Scholar Zhang J N, Lou S Q, Liang S. Study of pattern recognition based on SVM algorithm for φ-OTDR distributed optical fiber disturbance sensing system[J]. Infrared Laser Eng, 2017, 46(4): 0422003. doi: 10.3788/IRLA201746.0422003 CrossRef Google Scholar
[81]	Qian N. On the momentum term in gradient descent learning algorithms[J]. Neural Netw, 1999, 12(1): 145−151. doi: 10.1016/S0893-6080(98)00116-6 CrossRef Google Scholar
[82]	Xu H Y, Zhang Z, Zhang X W. Signal recognition basing on optical fiber vibration sensor[J]. Appl Mech Mater, 2013, 347–350: 743−747. doi: 10.4028/www.scientific.net/AMM.347-350.743 CrossRef Google Scholar
[83]	Burges C J C. A tutorial on support vector machines for pattern recognition[J]. Data Min Knowl Disc, 1998, 2(2): 121−167. doi: 10.1023/A:1009715923555 CrossRef Google Scholar
[84]	Liu T, Li H, He T, et al. Ultra-high resolution strain sensor network assisted with an LS-SVM based hysteresis model[J]. Opto-Electron Adv, 2021, 4(5): 200037. doi: 10.29026/oea.2021.200037 CrossRef Google Scholar
[85]	谢鑫, 吴慧娟, 饶云江. 一种基于光纤布喇格光栅振动传感器的光纤围栏入侵监测系统及其模式识别[J]. 光子学报, 2014, 43(5): 0506005. doi: 10.3788/gzxb20144305.0506005 CrossRef Google Scholar Xie X, Wu H J, Rao Y J. A fiber-optical perimeter intrusion detection system based on the fiber bragg grating vibration sensors and its identification method[J]. Acta Photonica Sin, 2014, 43(5): 0506005. doi: 10.3788/gzxb20144305.0506005 CrossRef Google Scholar
[86]	Wang Z D, Lou S Q, Wang X, et al. Multi-branch long short-time memory convolution neural network for event identification in fiber-optic distributed disturbance sensor based on φ-OTDR[J]. Infrared Phys Technol, 2020, 109: 103414. doi: 10.1016/j.infrared.2020.103414 CrossRef Google Scholar
[87]	Jiang F, Zhang Z H, Lu Z X, et al. High-fidelity acoustic signal enhancement for phase-OTDR using supervised learning[J]. Opt Express, 2021, 29(21): 33467−33480. doi: 10.1364/OE.439646 CrossRef Google Scholar
[88]	Fang G S, Xu T W, Feng S W, et al. Phase-sensitive optical time domain reflectometer based on phase-generated carrier algorithm[J]. J Lightw Technol, 2015, 33(13): 2811−2816. doi: 10.1109/JLT.2015.2414416 CrossRef Google Scholar
[89]	曲洪权, 夏雨, 毕福昆. 一种基于改进型SVM算法的光纤入侵信号识别研究[J]. 北方工业大学学报, 2017, 29(2): 33−38. doi: 10.3969/j.issn.1001-5477.2017.02.006 CrossRef Google Scholar Qu H Q, Xia Y, Bi F K. An improved SVM method to recognize harmful intrusion signal for optical fiber pre-warning system[J]. J North China Univ Technol, 2017, 29(2): 33−38. doi: 10.3969/j.issn.1001-5477.2017.02.006 CrossRef Google Scholar
[90]	Wu H J, Chen J P, Liu X R, et al. One-dimensional CNN-based intelligent recognition of vibrations in pipeline monitoring with DAS[J]. J Lightw Technol, 2019, 37(7): 4359−4366. doi: 10.1109/JLT.2019.2923839 CrossRef Google Scholar
[91]	Si Y, Wang Y Y, Wang L Y, et al. Multi-event classification for Φ-OTDR distributed optical fiber sensing system using deep learning and support vector machine[J]. Optik, 2020, 221: 165373. doi: 10.1016/j.ijleo.2020.165373 CrossRef Google Scholar
[92]	刘涛, 冯学斌, 刘彬, 等. OPGW光纤余长控制及寿命影响分析[J]. 电力信息与通信技术, 2017, 15(9): 8−12. doi: 10.16543/j.2095-641x.electric.power.ict.2017.09.002 CrossRef Google Scholar Liu T, Feng X B, Liu B, et al. Optical fiber excess length control and life impact analysis for OPGW[J]. Electric Power Inf Commun Technol, 2017, 15(9): 8−12. doi: 10.16543/j.2095-641x.electric.power.ict.2017.09.002 CrossRef Google Scholar
[93]	张益昕, 陈可楠, 张旭苹, 等. 一种基于相位敏感型光时域反射系统的OPGW覆冰监测系统及方法: CN110686626B.[P]. 2021-03-19. Google Scholar Zhang Y X, Chen K N, Zhang X P, et al. An OPGW icing monitoring system and method based on phase-sensitive light time domain reflection system: CN110686626B[P]. 2021-03-19. Google Scholar
[94]	Ding Z W, Zhang X P, Zou N M, et al. Phi-OTDR based on-line monitoring of overhead power transmission line[J]. J Lightw Technol, 2021, 39(15): 5163−5169. doi: 10.1109/JLT.2021.3078747 CrossRef Google Scholar
[95]	Ding Z W, Zou N M, Zhang C, et al. Self-optimized vibration localization based on distributed acoustic sensing and existing underground optical cables[J]. J Lightw Technol, 2022, 40(3): 844−854. doi: 10.1109/JLT.2021.3122738 CrossRef Google Scholar
[96]	Kundu T. Acoustic source localization[J]. Ultrasonics, 2014, 54(1): 25−38. doi: 10.1016/j.ultras.2013.06.009 CrossRef Google Scholar
[97]	Li X Y, Deng Z D, Rauchenstein L T, et al. Contributed Review: source-localization algorithms and applications using time of arrival and time difference of arrival measurements[J]. Rev Sci Instrum, 2016, 87(4): 041502. doi: 10.1063/1.4947001 CrossRef Google Scholar
[98]	Patwari N, Ash J N, Kyperountas S, et al. Locating the nodes: cooperative localization in wireless sensor networks[J]. IEEE Signal Proc Mag, 2005, 22(4): 54−69. doi: 10.1109/MSP.2005.1458287 CrossRef Google Scholar
[99]	Yang K, An J P, Bu X Y, et al. Constrained total least-squares location algorithm using time-difference-of-arrival measurements[J]. IEEE Trans Veh Technol, 2010, 59(3): 1558−1562. doi: 10.1109/TVT.2009.2037509 CrossRef Google Scholar

Overview

Overview

The phase-sensitive optical time-domain reflectometry (Φ-OTDR) sensing system has the characteristics of high dynamic response and high sensitivity, and has great application potential in the field of large-scale engineering structural health monitoring. The instrumentation level and engineering application of Φ-OTDR systems depend to a large extent on digital signal processing (DSP) technology. For the Φ-OTDR system, the tasks of digital signal processing mainly include three aspects. First, the demodulation of Rayleigh's backscattered light phase information should be completed accurately and efficiently. It is necessary to understand the relationship between the phase difference and the sound field signal. Then, it is necessary to reasonably set the core parameters of the Φ-OTDR system in the digital-to-analog conversion to obtain the RBS signal quickly and accurately. After that, it is necessary to select an appropriate demodulation method for demodulation. Second, all kinds of noise floor of the sensing system itself should be analyzed and suppressed. Since the noise floor of the sensing system itself is inevitable, analyzing and suppressing it is the key to improve the signal-to-noise ratio of the system. The drift of the laser center frequency, the local birefringence change of the fiber, and the nonlinear correspondence between the fiber strain and the interference intensity will all introduce corresponding noise to the system. Among the many types of noise, the coherent fading brought by the system will cause the system SNR to continue to deteriorate and randomly form detection blind spots; the polarization-related noise caused by the external environment will affect the Φ-OTDR system's ability to perceive multiple disturbance events. Third, reliable feature extraction and pattern recognition strategies should be quickly selected to improve the accuracy and intelligence of system reconstruction disturbance events. In engineering applications, various monitoring objects and time-varying background noise make it difficult to describe vibration events by accurate mathematical models. In particular, when Φ-OTDR is used in new scenarios, it needs to be able to quickly establish a corresponding analysis model based on industry knowledge, and minimize the degree of manual participation in it. Therefore, efficient and reliable object feature extraction methods, pattern recognition algorithms, and machine learning strategies are urgently needed. In view of the above problems, this paper summarizes the main digital signal processing methods and technologies of the Φ-OTDR system in recent years in the digitization of optoelectronic signals, the demodulation of phase information, the suppression of system noise, and the pattern recognition of detected objects. Two application cases of transmission line condition monitoring and buried cable breakage early warning illustrate the digital signal processing skills in the design of engineering application schemes.