光谱学与光谱分析 |
|
|
|
|
|
An Extraction and Recognition Method of the Distributed Optical Fiber Vibration Signal Based on EMD-AWPP and HOSA-SVM Algorithm |
ZHANG Yan-jun1, 2, LIU Wen-zhe1, FU Xing-hu1, 2*, BI Wei-hong1, 2 |
1. School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China 2. The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, Qinhuangdao 066004, China |
|
|
Abstract Given that the traditional signal processing methods can not effectively distinguish the different vibration intrusion signal, a feature extraction and recognition method of the vibration information is proposed based on EMD-AWPP and HOSA-SVM, using for high precision signal recognition of distributed fiber optic intrusion detection system. When dealing with different types of vibration, the method firstly utilizes the adaptive wavelet processing algorithm based on empirical mode decomposition effect to reduce the abnormal value influence of sensing signal and improve the accuracy of signal feature extraction. Not only the low frequency part of the signal is decomposed, but also the high frequency part the details of the signal disposed better by time-frequency localization process. Secondly, it uses the bispectrum and bicoherence spectrum to accurately extract the feature vector which contains different types of intrusion vibration. Finally, based on the BPNN reference model, the recognition parameters of SVM after the implementation of the particle swarm optimization can distinguish signals of different intrusion vibration, which endows the identification model stronger adaptive and self-learning ability. It overcomes the shortcomings, such as easy to fall into local optimum. The simulation experiment results showed that this new method can effectively extract the feature vector of sensing information, eliminate the influence of random noise and reduce the effects of outliers for different types of invasion source. The predicted category identifies with the output category and the accurate rate of vibration identification can reach above 95%. So it is better than BPNN recognition algorithm and improves the accuracy of the information analysis effectively.
|
Received: 2014-11-25
Accepted: 2015-03-21
|
|
Corresponding Authors:
FU Xing-hu
E-mail: fuxinghu@ysu.edu.cn
|
|
[1] Wu Huijuan, Rao Yunjiang, Tang Cheng, et al. Sensors and Actuators A: Physical, 2011, 167(2): 548. [2] Zhou Zhengxian, Zhuang Songlin. Optics Communications, 2014, 33: 1. [3] Wei Pu, Shan Xuekang, Sun Xiaohan. Optical Fiber Technology, 2013, 19(1): 47. [4] Mahmoud S S, Visagathilagar Y, Katsifolis J. Photonic Sensors, 2012, 2(3): 225. [5] RAO Yun-jiang, WU Min, RAN Zeng-ling, et al(饶云江,吴 敏,冉曾令,等). Chinese Journal of Sensors and Actuators(传感技术学报), 2007, 20(5): 45. [6] YU Xiao-mang, LUO Guang-ming, ZHU Zhen-min, et al(喻骁芒,罗光明,朱珍民,等). Opto-Electronic Engineering(光电工程), 2014, 41(1): 36. [7] Marco Leo, David Looney, Tiziana D’Orazio et al. IEEE Transactions on Instrumentation and Measurement,2012, 61(1): 221. [8] David Looney, Danilo P. IEEE Transactions on Signal Processing. 2009, 57(4): 1626. [9] Shao Renping, Hu Wentao, Wang Yayun,et al. Measurement, 2014, 54: 118. [10] Hu Qiao, He Zhengjia, Zhang Zhousuo et al. Mechanical Systems and Signal Processing, 2007, 21: 688. [11] LIU Heng-bing, HAN Shi-qin, LIU Jing(刘恒冰,韩世勤,刘 晶). Computer Engineering and Applications(计算机工程与应用), 2007, 43(24): 72. [12] Jia Xiaoning, Yang Hang, Ma Siliang,et al. Optics and Lasers in Engineering, 2014, 57: 28. [13] Kuang Chua Chuaa, Vinod Chandran, U Rajendra Acharyaa,et al. Medical Engineering & Physics, 2010, 32: 679. [14] Liang B, Iwnicki S D, Zhao Y. Mechanical Systems and Signal Processing, 2013, 39: 342. [15] QIU Su, JIN Wei-qi, SONG Zheng(裘 溯,金伟其,宋 铮). Optical Technique(光学技术), 2011, 37(3): 351. [16] Helena G Ramos, Tiago Rocha, Jakub Král,et al. Measurement, 2014, 54: 201. [17] Cheng Weifei, Guang Chenbai. Mechanical Systems and Signal Processing,2014, 49: 196. [18] Sun Jiedi, Xiao Qiyang, Wenb Jiangtao,et al. Measurement, 2014, 55: 434. [19] Behrang M A, Assareh E, Noghrehabadi A R,et al. Energy, 2011, 36: 3036. |
[1] |
WANG Zhen-ni1, KANG Zhi-wei1*, LIU Jin2, ZHANG Jie2. A Solar Spectral Doppler Redshift Velocity Measurement Method Based on Adaptive EMD-NDFT[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3475-3482. |
[2] |
WANG Ren-jie1, 2, FENG Peng1*, YANG Xing3, AN Le3, HUANG Pan1, LUO Yan1, HE Peng1, TANG Bin1, 2*. A Denoising Algorithm for Ultraviolet-Visible Spectrum Based on
CEEMDAN and Dual-Tree Complex Wavelet Transform[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 976-983. |
[3] |
CUI Xiao-rong, SHEN Tao*, HUANG Jian-lu, SUN Bin-bin. Infrared Mid-Wave and Long-Wave Image Fusion Based on FABEMD and Improved Local Energy Window[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(07): 2043-2049. |
[4] |
LI Ming1, 2, LI Yan-bing3, ZHANG Qiao-chu2, SHI Yu-tao2, CUI Fei-peng2, ZHAO Ying1, 2. Research on Spark Spectrum Signal Processing Based on Ensemble Empirical Mode Decomposition[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(06): 1923-1928. |
[5] |
ZHANG Hui1, ZHANG Li-juan1,2, WANG Yu-tian1, SHANG Feng-kai1*, ZHANG Yan1, SUN Yang-yang1, WANG Xuan-rui1, WANG Shu-tao1. Determination of PAHs in Water by Using EEMD and SWATLD Coupled with Three-Dimensional Fluorescence Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(08): 2595-2601. |
[6] |
LI Jing1, GUAN Ye-peng1, 2*, LI Wei-dong3, LUO Hong-jie4. Ancient Ceramic Kiln Non-Destructive Identification Based on Multi-Wavelength Diffuse Reflectance Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(01): 166-171. |
[7] |
YANG Ke-ming1, WANG Guo-ping1,2, FU Ping-jie1, ZHANG Wei1, WANG Xiao-feng1. A Model on Extracting the Pollution Information of Heavy Metal Copper Ion Based on the Soil Spectra Analyzed by HHT in Time-Frequency[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(02): 564-569. |
[8] |
LU Min1, LI Xiao-xia1,2*, SHANG Li-ping2, DENG Hu1,2 . Research on the Method of Improving Terahertz Frequency Resolution Based on Empirical Mode Decomposition [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2016, 36(09): 2732-2735. |
[9] |
WU Fang1, JIANG Xi-ping1*, YU Han-wen2, XIU Lian-cun3. Application of Empirical Mode Decomposition and Independent Component Analysis for the Interpretation of Rock-Mineral Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2016, 36(05): 1592-1597. |
[10] |
ZHAO Xiao-yu1, FANG Yi-ming2, TAN Feng1, TONG Liang3, ZHAI Zhe4 . EMD Time-Frequency Analysis of Raman Spectrum and NIR [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2016, 36(02): 424-429. |
[11] |
HAN Qing-yang, ZHOU Peng-ji . Research on the Method of Eliminating Noise and Background in the Meantime in Detecting Ethanol Contention Based on Raman Spectra [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2015, 35(12): 3406-3409. |
[12] |
ZHA Yu-tong1, LIU Guang-da1*, ZHOU Run-dong1, ZHANG Xiao-feng1, NIU Jun-qi2, YU Yong1, WANG Wei1 . EEMD-ICA Applied in Signal Extraction in Functional Near-Infrared Spectroscopy [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2015, 35(10): 2746-2751. |
[13] |
ZHANG Yan-jun1, 2, LIU Wen-zhe1, FU Xing-hu1, 2*, BI Wei-hong1, 2 . A Brillouin Scattering Spectrum Feature Extraction Based on Flies Optimization Algorithm with Adaptive Mutation and Generalized Regression Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2015, 35(10): 2916-2923. |
[14] |
ZHANG Yan-jun1, 2, LIU Wen-zhe1, FU Xing-hu1, 2*, BI Wei-hong1, 2 . The High Precision Analysis Research of Multichannel BOTDR Scattering Spectral Information Based on the TTDF and CNS Algorithm [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2015, 35(07): 1802-1807. |
[15] |
ZHAO Xiao-yu1,2, FANG Yi-ming1, TAN Feng2, WANG Zhi-gang3, TONG Liang4 . Adaptive “3R” De-Noising Algorithm Based on Near Infrared Bi-Spectrum [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2015, 35(04): 1146-1150. |
|
|
|
|