Vibration events recognition of optical fiber based on multi-scale 1-D CNN

Wu Jun; Guan Luyang; Bao Ming; Xu Yaohua; Ye Wei

doi:10.12086/oee.2019.180493

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Wu Jun, Guan Luyang, Bao Ming, et al. Vibration events recognition of optical fiber based on multi-scale 1-D CNN[J]. Opto-Electronic Engineering, 2019, 46(5): 180493. doi: 10.12086/oee.2019.180493

Citation:

Wu Jun, Guan Luyang, Bao Ming, et al. Vibration events recognition of optical fiber based on multi-scale 1-D CNN[J]. Opto-Electronic Engineering, 2019, 46(5): 180493. doi: 10.12086/oee.2019.180493

Vibration events recognition of optical fiber based on multi-scale 1-D CNN

1.
Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, Hefei, Anhui 230039, China
2.
Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China
3.
College of Control Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310058, China

Fund Project: Supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDC02040600)

More Information

^*Corresponding author: Guan Luyang, E-mail:guanluyang@mail.ioa.ac.cn

Received Date 21 September 2018

Revised Date 22 March 2019

Published Date 01 May 2019

Abstract

Abstract

A new CNN-based deep neural network, multi-scale one-dimensional convolutional neural network (MS 1-D CNN) was proposed to improve the efficiency and accuracy of vibration event recognition for a phase-sensitive optical time-domain reflectometry (Φ-OTDR) distributed optical fiber vibration sensing system. The raw vibration signals are pre-processed first to remove noise as far as possible. The pre-processing operations include pre-emphasis filtering, normalization and spectral subtraction. The pre-processed signals are used as the inputs of MS 1-D CNN directly. MS 1-D CNN realizes the end-to-end feature extraction of vibration signals and finally recognizes the vibration events by using a fully-connected layer (FC layer) and a Softmax layer. In comparison with two-dimensional convolutional neural network (2-D CNN) and one-dimensional convolutional neural network (1-D CNN), the proposed method balances the time and frequency scales better during feature extraction and reduces the pending parameters of the whole neural network. A vibration recognition experiment was designed to classify the three types of the vibration events including damaging, knocking and interference. The recognition results show that MS 1-D CNN achieves similar recognition performance, over 96 percent, at twice processing speed compared to 2-D CNN. Therefore, it is beneficial to improve the real-timing of vibration monitoring while maintaining the recognition performance.
- distributed optical fiber vibration sensing /
- multi-scale 1-D CNN /
- Φ-OTDR /
- vibration events recognition /
- pattern recognition

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References

[1]	蒋立辉, 盖井艳, 王维波, 等.基于总体平均经验模态分解的光纤周界预警系统模式识别方法[J].光学学报, 2015, 35(10): 1006002. Google Scholar Jiang L H, Gai J Y, Wang W B, et al. Ensemble empirical mode decomposition based event classification method for the fiber-optic intrusion monitoring system[J]. Acta Optica Sinica, 2015, 35(10): 1006002. Google Scholar
[2]	朱程辉, 赵益, 王建平, 等.光纤入侵行为融合特征的集成识别[J].光电工程, 2016, 43(12): 6-12. doi: 10.3969/j.issn.1003-501X.2016.12.002 CrossRef Google Scholar Zhu C H, Zhao Y, Wang J P, et al. Ensemble recognition of fiber intrusion behavior based on blending features[J]. Opto-Electronic Engineering, 2016, 43(12): 6-12. doi: 10.3969/j.issn.1003-501X.2016.12.002 CrossRef Google Scholar
[3]	况洋, 吴昊庭, 张敬栋, 等.分布式多参数光纤传感技术研究进展[J].光电工程, 2018, 45(9): 170678. doi: 10.12086/oee.2018.170678 CrossRef Google Scholar Kuang Y, Wu H T, Zhang J D, et al. Advances of key technologies on distributed fiber system for multi-parameter sensing[J]. Opto-Electronic Engineering, 2018, 45(9): 170678 doi: 10.12086/oee.2018.170678 CrossRef Google Scholar
[4]	Zhu H, Pan C, Sun X H. Vibration pattern recognition and classification in OTDR based distributed optical-fiber vibration sensing system[J]. Proceedings of SPIE, 2014, 9062: 906205. doi: 10.1117/12.2045268 CrossRef Google Scholar
[5]	张颜, 娄淑琴, 梁生, 等.基于多特征参量的Ф-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. 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]. Chinese Journal of Lasers, 2015, 42(11): 1105005. Google Scholar
[6]	李小玉, 吴慧娟, 彭正谱, 等.基于时间序列奇异谱特征的Φ-OTDR扰动检测方法[J].光子学报, 2014, 43(4): 0428001. Google Scholar Li X Y, Wu H J, Peng Z P, et al. A novel time sequence singular spectrum analysis method for Φ-OTDR disturbance detection system[J]. Acta Photonica Sinica, 2014, 43(4): 0428001. Google Scholar
[7]	Xu C J, Guan J J, Bao M, et al. Pattern recognition based on enhanced multifeature parameters for vibration events in Φ-OTDR distributed optical fiber sensing system[J]. Microwave and Optical Technology Letters, 2017, 59(12): 3134-3141. doi: 10.1002/mop.30886 CrossRef Google Scholar
[8]	Xu C J, Guan J J, Bao M, et al. Pattern recognition based on time-frequency analysis and convolutional neural networks for vibrational events in Φ-OTDR[J]. Optical Engineering, 2018, 57(1): 016103. doi: 10.1117/1.OE.57.1.016103 CrossRef Google Scholar
[9]	Ince T, Kiranyaz S, Eren L, et al. Real-time motor fault detection by 1-D convolutional neural networks[J]. IEEE Transactions on Industrial Electronics, 2016, 63(11): 7067-7075. doi: 10.1109/TIE.2016.2582729 CrossRef Google Scholar
[10]	Kiranyaz S, Ince T, Gabbouj M. Real-time patient-specific ECG classification by 1-D convolutional neural networks[J]. IEEE Transactions on Biomedical Engineering, 2016, 63(3): 664-675. doi: 10.1109/TBME.2015.2468589 CrossRef Google Scholar
[11]	Hu G, Wang K J, Peng Y, et al. Deep learning methods for underwater target feature extraction and recognition[J]. Computational Intelligence and Neuroscience, 2018, 2018: 1214301. Google Scholar
[12]	Pan Z Q, Liang K Z, Ye Q, et al. Phase-sensitive OTDR system based on digital coherent detection[J]. Proceedings of SPIE, 2011, 8311: 83110S. doi: 10.1117/12.905657 CrossRef Google Scholar
[13]	Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[OL]. arXiv: 1409.1556 [cs.CV]. Google Scholar
[14]	Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift[C]//Proceedings of the 32nd International Conference on International Conference on Machine Learning, Lille, France, 2015: 448-456.https://arxiv.org/abs/1502.03167 Google Scholar
[15]	Hinton G E, Srivastava N, Krizhevsky A, et al. Improving neural networks by preventing co-adaptation of feature detectors[OL]. arXiv: 1207.0580 [cs.NE]. Google Scholar
[16]	Sutskever I, Martens J, Dahl G, et al. On the importance of initialization and momentum in deep learning[C]//Proceedings of the 30th International Conference on International Conference on Machine Learning, Atlanta, GA, USA, 2013: Ⅲ-1139-Ⅲ-1147.https://www.researchgate.net/publication/286271944_On_the_importance_of_initialization_and_momentum_in_deep_learning Google Scholar
[17]	He K M, Sun J. Convolutional neural networks at constrained time cost[C]//Proceedings of 2015 IEEE conference on computer vision and pattern recognition, Boston, MA, USA, 2015: 5353-5360.http://www.oalib.com/paper/4067545 Google Scholar

Overview

Overview

Overview: As a new type of sensing technology, the phase-sensitive optical time domain reflection (Φ-OTDR) distributed optical fiber sensing technology has the advantages of good environmental tolerance, low energy consumption, high sensitivity, long monitoring distance compared with traditional sensing technology, and has been the emphasis and hotspot of the researches. Among the manifold application fields of Φ-OTDR distributed optical vibration sensing system, identification of vibration events is one of the most popular and advantaged applications. Therefore, efficient and accurate identification of different vibration events is the focus of this paper. The recognition speed of traditional methods is fast, but their recognition rate and robustness are not ideal and strongly dependent on the artificial feature design. Although the method based on two-dimensional convolutional neural network (2-D CNN) has obtained a high recognition rate, the slow recognition speed and feature extraction process hamper its use in real-time systems. In view of the fact that one-dimensional convolutional neural network (1-D CNN) has been used in other real-time identification fields and achieved good results, this paper proposes a multi-scale one-dimensional convolutional neural network (MS 1-D CNN) method which takes the vibration signals as the input of this network and needs not to manually extract features. Feature extraction of the intrusion vibration signals in the MS 1-D CNN takes into account the rich feature information of the signals in time and frequency scales, thus achieves efficient and accurate identification. In order to control the spatial complexity and parameter quantity, three scales and four layers are used in the MS 1-D CNN method. The raw vibration signals are pre-processed firstly to remove noise as far as possible, including pre-emphasis filtering, normalization and spectral subtraction. The pre-processed waveform signals are directly inputted into the MS 1-D CNN, and the recognition results are achieved by using fully-connected layer (FC layer) and Softmax layer. In comparison with the methods based on 2-D CNN and 1-D CNN, the proposed method balances the time and frequency scales well during feature extraction and reduces the number of pending parameters. A vibration recognition experiment was designed to classify the three different vibration events, including damaging, knocking and interference. The recognition results show that MS 1-D CNN achieves similar recognition performance at twice processing speed compared to 2-D CNN. Hence, it is beneficial to improve the real-timing of vibration monitoring while maintaining the recognition performance.