基于多重分形谱的光纤周界振动信号识别
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摘要
为了有效识别光纤周界系统的振动信号,提出一种多重分形谱参数和改进概率神经网络相结合的光纤振动信号识别方法.该方法能够避免特征提取过程中需要选择经验阈值和模式识别过程中需要确定平滑因子的不足.首先,检验分析光纤振动信号多重分形的存在性和有效性.然后,计算和提取光纤振动信号的多重分形谱参数,构成能够准确描述信号非线性和复杂性特性的特征向量.最后,采用改进的概率神经网络算法进行自适应地学习和分类,实现对不同光纤振动信号的识别.采用现场实验采集的四种振动信号对该方法进行验证,结果表明,平均识别率达到96.25%,识别时间为1.63s.该方法在正确识别率方面优于传统的概率神经网络算法.
To effectively identify the vibration signals of the fiber optic perimeter system,a method was presented,which combines the multi-fractal spectrum parameters with the improved probabilistic neural network.This method could avoid the shortcomings of experience threshold selecting in extracting features and smoothing factor determining in the process of pattern recognition.First of all,the existence and validity of multi-fractal in optical fiber vibration signals were examined and analyzed.Then,the multi-fractal spectrum parameters of the fiber vibration signals were calculated and extracted to form the feature vectors which could accurately describe the nonlinear and complexity of the signals.Finally,the improved probabilistic neural network algorithm was used for adaptive learning and classification to realize the identification of different optical fiber vibration signals.Four kinds of vibration signals collected from field tests were used to verify the method and the results show that the average recognition rate reaches 96.25%and the recognition time is 1.63 s.This method is superior to the traditional probabilistic neural network algorithm in terms of correct recognition rate.
To effectively identify the vibration signals of the fiber optic perimeter system,a method was presented,which combines the multi-fractal spectrum parameters with the improved probabilistic neural network.This method could avoid the shortcomings of experience threshold selecting in extracting features and smoothing factor determining in the process of pattern recognition.First of all,the existence and validity of multi-fractal in optical fiber vibration signals were examined and analyzed.Then,the multi-fractal spectrum parameters of the fiber vibration signals were calculated and extracted to form the feature vectors which could accurately describe the nonlinear and complexity of the signals.Finally,the improved probabilistic neural network algorithm was used for adaptive learning and classification to realize the identification of different optical fiber vibration signals.Four kinds of vibration signals collected from field tests were used to verify the method and the results show that the average recognition rate reaches 96.25%and the recognition time is 1.63 s.This method is superior to the traditional probabilistic neural network algorithm in terms of correct recognition rate.
引文
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[18]NOURI R,JAFARI M R,ARIAN M,et al.Correlation between Cu mineralization and major faults using multifractal modelling in the Tarom area(NW Iran)[J].Geologica Carpathica,2013,64(5):409-416.
[19]CHU Qing-qing,XIAO Han,LU Yong,et al.Gear fault diagnosis based on multifractal theory and neural network[J].Journal of Vibration&Shock,2015,34(21):15-18.褚青青,肖涵,吕勇,等.基于多重分形理论与神经网络的齿轮故障诊断[J].振动与冲击,2015,34(21):15-18.
[20]ZHOU Ai-wu,ZHAI Zeng-hui,LIU Hui-ting.Improved BP neural network based on simulated annealing[J].Microelectronics&Computer,2016,33(4):144-147.周爱武,翟增辉,刘慧婷.基于模拟退火算法改进的BP神经网络算法[J].微电子学与计算机,2016,33(4):144-147.
[21]YOU Li-hui,WU Jing-jing,WANG Yao,et al.Optimized BP neural network based on simulated annealing algorithm for pH value prediction[J].Chinese Journal of Sensors&Actuators,2014,27(12):1643-1648.尤丽华,吴静静,王瑶,等.基于模拟退火优化BP神经网络的pH值预测[J].传感技术学报,2014,(12):1643-1648.
[22]WANG Zhuo,WANG Tian-ran,YUAN Ming-zhe,et al.Research on working condition recognition in cement rotary kiln using multifractal method[J].Chinese Journal of Scientific Instrument,2009,30(4):711-716.王卓,王天然,苑明哲,等.基于多重分形的水泥回转窑工况识别研究[J].仪器仪表学报,2009,30(4):711-716.
[23]XIONG Xing-long,CUI Ya-feng,YANG Li-xiang,et al.A new method for signal recognition of the fiber-optic alarm system around airport[J].Journal of Optoelectronics·Laser,2017,28(9):985-991.熊兴隆,崔雅峰,杨立香,等.一种机场环境光纤预警系统的信号识别新算法[J].光电子·激光,2017,28(9):985-991.
[24]JIANG Li-hui,GAI Jing-yan,WANG Wei-bo,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.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.
[25]WANG Si-yuan,LOU Shu-qin,LIANG Sheng,et al.Pattern recognition method of fiber distributed disturbance sensing system based on M-Z interferometer[J].Infrared&Laser Engineering,2014,43(8):2613-2618.王思远,娄淑琴,梁生,等.M-Z干涉仪型光纤分布式扰动传感系统模式识别方法[J].红外与激光工程,2014,43(8):2613-2618.
[2]PEI Li,WENG Si-jun,WU Liang-yin,et al.Progress in optical fiber laser sensing system[J].Chinese Journal of Lasers,2016,43(7):0700001.裴丽,翁思俊,吴良英,等.光纤激光传感系统的研究进展[J].中国激光,2016,43(7):0700001.
[3]REN Zhong-jie,CUI Ke,LI Jiang-xin,et al.Michelson-interferometer-based all-fiber optical perimeter security system by utilizing binary rectangular pulse phase modulation[J].Acta Optica Sinica,2017,37(12):1206004.任仲杰,崔珂,李建欣,等.基于二元矩形脉冲相位调制的迈克耳孙干涉型全光纤周界安防系统[J].光学学报,2017,37(12):1206004.
[4]MA C,LIU T,LIU K,et al.Long-range distributed fiber vibration sensor using an asymmetric dual Mach-Zehnder interferometers[J].Journal of Lightwave Technology,2016,34(9):2235-2239.
[5]JIANG J,AN J,LIU K,et al.A fast positioning algorithm for the asymmetric dual Mach-Zehnder interferometric infrared fiber vibration sensor[J].Infrared Physics&Technology,2017,85:359-363.
[6]ZHANG L,WANG D N,LIU J,et al.Simultaneous refractive index and temperature sensing with precise sensing location[J].IEEE Photonics Technology Letters,2016,28(8):891-894.
[7]LIU K,TIAN M,JIANG J,et al.An improved positioning algorithm in a long-range asymmetric perimeter security system[J].Journal of Lightwave Technology,2016,34(22):5278-5283.
[8]ZOU Bo-xian,MIAO Jun,XU Shao-wu,et al.Research on vibration signal recognition of optical fiber based on ELMalgorithm[J].Computer Engineering&Applications,2017,53(16):126-133.邹柏贤,苗军,许少武,等.基于ELM算法的光纤振动信号识别研究[J].计算机工程与应用,2017,53(16):126-133.
[9]MAHMOUD S S,VISAGATHILAGAR Y,KATSIFOLIS J.Real-time distributed fiber optic sensor for security systems:performance,event classification and nuisance mitigation[J].Photonic Sensors,2012,2(3):225-236.
[10]YANG Zheng-li,SUN Shu-fang.The signal identification of optical fiber perimeter security system based on wavelet energy entropy[J].Journal of Optoelectronics·Laser,2016,27(12):1328-1333.杨正理,孙书芳.基于小波能量熵的光纤周界安防系统信号识别[J].光电子·激光,2016,27(12):1328-1333.
[11]LI Kai-yan,ZHAO Xing-qun,SUN Xiao-han,et al.A regular composite feature extraction method for vibration signal pattern recognition in optical fiber link system[J].Acta Physica Sinica,2015,64(5):054304.李凯彦,赵兴群,孙小菡,等.一种用于光纤链路振动信号模式识别的规整化复合特征提取方法[J].物理学报,2015,64(5):054304.
[12]ZHU Cheng-hui,WANG Jian-ping,LI Qi-yue,et al.Recognition and localization of intrusion vibration signal based on time-frequency characteristics in optical fiber perimeter security[J].Chinese Journal of Lasers,2016,43(6):0610001.朱程辉,王建平,李奇越,等.基于时频特征的光纤周界入侵振动信号识别与定位[J].中国激光,2016,43(6):0610001.
[13]ZHANG Yan-jun,LIU Wen-zhe,FU Xing-hu,et al.An extraction and recognition method of the distributed optical fiber vibration signal based on EMD-AWPP and HOSA-SVM algorithm[J].Spectroscopy and Spectral Analysis,2016,36(2):577-582.张燕君,刘文哲,付兴虎,等.基于EMD-AWPP和HOSA-SVM算法的分布式光纤振动入侵信号的特征提取与识别[J].光谱学与光谱分析,2016,36(2):577-582.
[14]RAMAN M R G,SOMU N,KIRTHIVASAN K,et al.A hypergraph and arithmetic residue-based probabilistic neural network for classification in intrusion detection systems[J].Neural Networks,2017,92:89-97.
[15]LOBODA I,OLIVARES R M A.Gas turbine fault diagnosis using probabilistic neuralnetworks[J].International Journal of Turbo&Jet-Engines,2015,32(2):175-191.
[16]LI Zhao-fei,CHAI Yi,LI Hua-feng.Fault feature extraction method of vibration signals based on multi-fractal[J].Journal of Data Acquisition&Processing,2013,28(1):38-44.李兆飞,柴毅,李华锋.多重分形的振动信号故障特征提取方法[J].数据采集与处理,2013,28(1):38-44.
[17]GUO Xing-ming,ZHANG Wen-ying,YUAN Zhi-hui,et al.Heart sound recognition based on EMD correlation dimension and multi-fractals pectrum[J].Chinese Journal of Scientific Instrument,2014,35(4):827-833.郭兴明,张文英,袁志会,等.基于EMD关联维数和多重分形谱的心音识别[J].仪器仪表学报,2014,35(4):827-833.
[18]NOURI R,JAFARI M R,ARIAN M,et al.Correlation between Cu mineralization and major faults using multifractal modelling in the Tarom area(NW Iran)[J].Geologica Carpathica,2013,64(5):409-416.
[19]CHU Qing-qing,XIAO Han,LU Yong,et al.Gear fault diagnosis based on multifractal theory and neural network[J].Journal of Vibration&Shock,2015,34(21):15-18.褚青青,肖涵,吕勇,等.基于多重分形理论与神经网络的齿轮故障诊断[J].振动与冲击,2015,34(21):15-18.
[20]ZHOU Ai-wu,ZHAI Zeng-hui,LIU Hui-ting.Improved BP neural network based on simulated annealing[J].Microelectronics&Computer,2016,33(4):144-147.周爱武,翟增辉,刘慧婷.基于模拟退火算法改进的BP神经网络算法[J].微电子学与计算机,2016,33(4):144-147.
[21]YOU Li-hui,WU Jing-jing,WANG Yao,et al.Optimized BP neural network based on simulated annealing algorithm for pH value prediction[J].Chinese Journal of Sensors&Actuators,2014,27(12):1643-1648.尤丽华,吴静静,王瑶,等.基于模拟退火优化BP神经网络的pH值预测[J].传感技术学报,2014,(12):1643-1648.
[22]WANG Zhuo,WANG Tian-ran,YUAN Ming-zhe,et al.Research on working condition recognition in cement rotary kiln using multifractal method[J].Chinese Journal of Scientific Instrument,2009,30(4):711-716.王卓,王天然,苑明哲,等.基于多重分形的水泥回转窑工况识别研究[J].仪器仪表学报,2009,30(4):711-716.
[23]XIONG Xing-long,CUI Ya-feng,YANG Li-xiang,et al.A new method for signal recognition of the fiber-optic alarm system around airport[J].Journal of Optoelectronics·Laser,2017,28(9):985-991.熊兴隆,崔雅峰,杨立香,等.一种机场环境光纤预警系统的信号识别新算法[J].光电子·激光,2017,28(9):985-991.
[24]JIANG Li-hui,GAI Jing-yan,WANG Wei-bo,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.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.
[25]WANG Si-yuan,LOU Shu-qin,LIANG Sheng,et al.Pattern recognition method of fiber distributed disturbance sensing system based on M-Z interferometer[J].Infrared&Laser Engineering,2014,43(8):2613-2618.王思远,娄淑琴,梁生,等.M-Z干涉仪型光纤分布式扰动传感系统模式识别方法[J].红外与激光工程,2014,43(8):2613-2618.