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摘要:
相位敏感光时域反射(Φ-OTDR)传感系统具有高动态响应、高灵敏等特点，在大型工程结构健康监测领域具有巨大的应用潜力。而Φ-OTDR系统仪器化水平和工程应用很大程度上取决于数字信号处理(DSP)技术。本文对比分析了近年来Φ-OTDR系统在信号的量化、解调、抑噪以及模式识别上主要的数字信号处理方法和技术，并通过架空输电线路状态监测、埋地电缆外破预警两个应用实例，阐述了工程应用中数字信号处理与行业背景知识相结合的重要性和方法，并对Φ-OTDR系统中数字信号处理方法的发展现状和趋势进行了总结与展望。
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关键词:
- 相位敏感光时域反射(Φ-OTDR) /
- 外差相干探测 /
- 数字信号处理(DSP) /
- 输电线健康监测
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.
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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.
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图 1 外部声场对光纤影响示意图
Figure 1. Influence of an external sound field on the optical fiberl fiber
图 7 (a) 弧垂的实测值与估计值对比；(b) 振动时架空输电线最低点相对平衡位置的位移；(c) 覆冰厚度估计
Figure 7. (a) Comparison between measured value and estimated value of sag; (b) Displacement of relative equilibrium position at the lowest point of overhead transmission line during vibration; (c) Ice thickness estimation
图 10 外场实验。(a) 置信区间的二维高斯分布；(b) 置信区间的等高线图
Figure 10. Outfield experiment. (a) Two dimensional Gaussian distribution of confidence interval; (b) Contour plot of confidence interval
图 11 位置估计误差。(a) 单阵列；(b) 垂直正交阵列
Figure 11. Position estimation error. (a) Single array; (b) Vertical orthogonal array
表 1 Φ-OTDR系统中的特征提取、分类模型及事件识别
Table 1. Feature extraction, classification model and event recognition in Φ-OTDR
特征提取方法 提取特征种类 分类模型 事件识别 年份 参考文献 小波变换 时域特征+频域特征 距离法 人奔跑、车行驶及人车联合行进 2007 [68] 自适应动态阈值 时域特征 BP神经网络 光纤围栏入侵 2014 [85] 自动提取 频域特征 融合分类器 入侵事件 2015 [88] 多特征参量 频域特征 距离法 应力破坏、攀爬、浇水以及轻度碾压 2015 [69] STFT 频域特征 EDFS 踢墙、踹墙及原地跑 2015 [25] 归一化系数 频域特征 SVM 列车跟踪 2016 [72] 基于梅尔倒谱系数 频域特征 距离法 入侵、大雨 2016 [73] STFT 频域特征 距离法 管道威胁监测 2016 [74] 多特征参量 频域特征+时域特征 SVM 踩压、浇水及敲击 2017 [80] 多特征参量 频域特征+时域特征 SVM 行走、镐刨及挖掘机作业 2017 [89] 过滤法 形态特征 融合分类器 挖掘机施工、人员走动及木棒夯击 2018 [48] 短时单元多域特征提取 时域特征+频率特征 HMM 地埋管道沿线5中事件 2019 [70] 分帧处理时域信号+小波变换 时域特征+频域特征+形态特征 1-D CNN+SVM 数种石油管道典型事件 2019 [90] 去噪分帧 时域特征 FastICA 有无噪声干扰的对比事件 2020 [71] 自适应方法 频域特征+时域特征 融合分类器 挖掘、敲击、人员走动及背景噪声 2020 [75] 多特征参量 形态特征 SVM 工程车辆行驶、落石、人为夯筑
及挖土机挖掘2020 [78] 多分支层和可学习的LSTM层 时域特征 MLSTM-CNN 水、爬、敲、压 2020 [86] 卷积 形态特征 CNN+ SVM 8 种事件 2020 [91] 数值模拟 时域特征 端到端的卷积中性网络 PZT振动、管道敲击和语音 2021 [87] 
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