分布式光纤入侵信号检测与识别

张永康; 尚盈; 王晨; 赵文安; 李常; 曹冰; 王昌

doi:10.12086/oee.2021.200254

分布式光纤入侵信号检测与识别

doi: 10.12086/oee.2021.200254

基金项目:

山东省自然科学基金资助项目 ZR2019QF011

山东省重大科技创新工程项目 2019JZZY010113

山东省重点研发计划 2019GSF111065

山东省高等学校青创科技支持计划 2019KJJ004

详细信息

作者简介:
张永康(1997-)，男，硕士研究生，主要从事分布式光纤传感技术方面的研究。E-mail: ooozyk@163.com

通讯作者:
王昌(1977-)，男，博士，研究员，主要从事智能材料与光纤传感技术方面的研究。E-mail: ch_wangs@163.com

中图分类号: TN29
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出版历程
- 收稿日期: 2020-07-10
- 修回日期: 2020-11-20

Detection and recognition of distributed optical fiber intrusion signal

Funds:

Natural Science Foundation of Shandong Province ZR2019QF011

Science and Technology Innovation Project of Shandong Province - Major Special 2019JZZY010113

Key R & D Program of Shandong Province 2019GSF111065

the Youth Innovation Science and Technology Program of Colleges in Shandong Province 2019KJJ004

More Information

Corresponding author: Wang Chang, E-mail: ch_wangs@163.com

摘要

摘要: 分布式光纤声波传感(DAS)技术通过接收相干瑞利散射光的相位信息来探测声波或振动信号，具有灵敏度高、动态范围广等特性，可利用线性定量测量实现对信号的高保真还原。随着实际应用的需求不断提高，光纤入侵检测领域对事件的定位和识别提出了更高的要求，表现为对入侵事件的准确分类，因此将分布式光纤声波传感技术与模式识别(PR)技术相结合是目前研究的热门，有利于推动分布式光纤传感技术的应用发展。本文总结了近年来在分布式光纤入侵检测的模式识别技术中所应用的特征提取和分类算法的研究进展，回顾了几种实现入侵事件信号识别的特征提取方法及其在不同应用场合面临的特征选择难点，同时对特定事件识别算法的优劣进行分析归纳。
- 分布式声波传感 /
- 模式识别 /
- 特征提取 /
- 分类算法
Abstract: Distributed acoustic sensing (DAS) technology can detect acoustic or vibration signals with high sensitivity and wide dynamic range by receiving the phase information from coherent Rayleigh scattered light. Linear quantization is used to measure high fidelity restoration of the signals. With the increasing demand of practical applications, the optical fiber intrusion detection field has put forward higher requirements for event location and identification, which is manifested as the accurate classification of intrusion events. Therefore, the combination of distributed acoustic sensing and pattern recognition (PR) technology is a hot research topic at present. This is beneficial to promote the application and development of distributed optical fiber sensing technology. The research progress of the pattern recognition technology applied to distributed optical fiber intrusion detection in recent years is summarized in this paper, which can be used for feature extraction and classification algorithm research progress. In this paper, several feature extraction methods for realizing intrusion event signal recognition and their feature selection difficulties in different application situations are reviewed. Meanwhile, the advantages and disadvantages of specific event recognition algorithm are analyzed and summarized.
- distributed acoustic sensing /
- pattern recognition /
- feature extraction /
- classification algorithms

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图 1 DAS系统结构

Figure 1. DAS system structure

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图 2 入侵信号识别流程

Figure 2. Intrusion signal recognition process

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图 3 暴雨期间攀爬事件监测信号。

(a) 时域信号；(b) LC值^[12]

Figure 3. Signal representing a climb during torrential rain as detected. (a) Time domain representations; (b) LC vs. block number^[12]

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图 4 5种事件原始信号。(a) 剪切；(b) 晃动；(c) 攀爬；(d) 敲击；(e) 无入侵^[18]

Figure 4. Original signals of five events. (a) Cutting; (b) Waggling; (c) Climbing; (d) Knocking; (e) No intrusion^[18]

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图 5 5种事件分段过零率。(a) 剪切；(b) 晃动；(c) 攀爬；(d) 敲击；(e) 无入侵^[18]

Figure 5. Segment zero-crossing rates of five events. (a) Cutting; (b) Waggling; (c) Climbing; (d) Knocking; (e) No intrusion^[18]

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图 6 FFT特征提取流程图

Figure 6. FFT feature extraction flow chart

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图 7 MFCC特征提取流程图

Figure 7. MFCC feature extraction flow chart

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图 8 两种窗函数处理4种入侵事件的STFT图。(a)，(c)，(e)，(g) 敲击、摇晃、刮风、下雨经过汉宁窗处理后的时频图；(b)，(d)，(f)，(h) 敲击、摇晃、刮风、下雨经过凯塞窗处理后的时频图^[27]

Figure 8. STFT time-frequency diagrams of two kinds of window functions for processing four intrusion events. (a), (c), (e), (g) Time-frequency diagrams of knocking, shaking, winding, and raining signals after passing through the Hanning window; (b), (d), (f), (h) time-frequency diagrams of knocking, shaking, winding, and raining signals after passing through the Kaiser window^[27]

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图 9 4种入侵信号及其IMF分量。(a) 爬网; (b) 敲击; (c) 晃动; (d) 切割^[29]

Figure 9. Fence invasive signals and their IMF components through EMD. (a) EMD of climbing; (b) EMD of knocking; (c) EMD of waggling; (d) EMD of cutting^[29]

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图 10 4种入侵信号及其峰值特征向量。(a) 爬网; (b) 敲击; (c) 爬网特征向量; (d) 敲击特征向量; (e) 晃动; (f) 切割; (g) 晃动特征向量; (h) 切割特征向量

Figure 10. Signals and their kurtosis eigenvectors of four cases. (a) Climbing signal; (b) Knocking signal; (c) Eigenvectors of climbing; (d) Eigenvectors of knocking; (e) Waggling signal; (f) Cutting signal; (g) Eigenvectors of waggling; (h) Eigenvectors of cutting

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图 11 多尺度分解树。(a) 小波分解；(b) 小波包分解

Figure 11. Multi-scale decomposition tree. (a) Wavelet decomposition; (b) Wavelet packet decomposition

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图 12 三种事件WE分布^[38]

Figure 12. WE distribution for three typical events^[38]

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图 13 三种事件WPE分布^[38]

Figure 13. WPE distribution for three typical events^[38]

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图 14 (a) 车辆经过仿真信号；(b) 车辆经过实验信号^[40]

Figure 14. (a) Calculated signal of vehicle passing; (b) Experimentally measured signal of vehicle passing^[40]

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图 15 DBSCAN算法的核心点和边缘点^[41]

Figure 15. DBSCAN core and outlier points^[41]

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图 16 有向无环图RVM

Figure 16. Directed acyclic graph of RVM^[49]

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图 17 三类事件特征分布^[40]

Figure 17. Feature distribution of three events^[40]

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图 18 三层BP神经网络结构

Figure 18. Three-layer BP neural network structure

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图 19 卷积神经网络典型结构

Figure 19. Typical structure of CNN

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图 20 谱减法后的振动信号。(a) 去噪后敲击信号；(b) 去噪后敲击信号频谱分布^[55]

Figure 20. The effect of spectral subtraction on the vibration signal. (a) The time-domain waveform of the knocking signal after noise reduction; (b) The spectrogram of the knocking signal after noise reduction^[55]

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图 21 优化后的CNN网络结构(红色方块表示卷积运算，蓝色方块表示池化运算)^[56]

Figure 21. The optimized network structure (the red cube denotes convolution operation and the blue cube denotes pooling operation)^[56]

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图 22 5类事件混淆矩阵^[56]

Figure 22. Confusion matrix of five events' classification^[56]

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图 23 生成对抗网络流程图

Figure 23. GAN flow chart

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图 24 不同训练算法测试集的准确率和损失值^[61]

Figure 24. Accuracy and loss of testing datasets at different training algorithms^[61]

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图 25 LSTM网络的循环单元结构

Figure 25. Cyclic unit structure of LSTM network

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表 1 DAS模式识别技术发展历程

Table 1. The development of DAS pattern recognition technology number

	Time	Researchers	Feature extraction	Classification algorithm	Recognition rate/%
1	IEEE, 2009	Qi, et al.	FFT+PSD	PCA+SVM	88.9
2	IEEE, 2010	Mahmoud, et al.	LC	ANN
3	APS, 2014	Wu, et al	SSA	BP	＞90
4	ACPC, 2015	Cao, et al	FFT	SVM	92.62
5	JLT, 2015	Wu, et al	WD	BP	89.19
6	JLT, 2015	Liu, et al	EMD	RBF	85.75
7	Sensors, 2015	Sun, et al	MFE	RVM+GPU	97.8
8	JLT, 2016	Tejedor, et al	STFT	GMM	＞55
9	PS, 2017	Wu, et al	WPD	ANN	94.4
10	ISOP, 2017	Aktas.M, et al.	STFT	2-D CNN	＞93
11	ICOFS, 2018	Shiloh, et al.	RGB	GAN	94
12	JLT, 2019	Wei, et al	CFAR	SCN	94.67
13	JLT, 2019	Wu, et al	WPD	1-D CNN+SVM	96.59
14	OE, 2019	Wang, et al	RGB	DPN+GPU	97
15	MOTL, 2020	Chen, et al	STE+ZCR+MFCC	ALSTM	94.3
16	OE, 2020	Li, et al	STW	ConvLSTM	85.6

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