基于多重分形谱的光纤周界振动信号识别

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1.63 s.该方法在正确识别率方面优于传统的概率神经网络算法.

Abstract

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.

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熊兴隆, 张琬童, 冯磊, 李猛, 马愈昭, 冯帅. 基于多重分形谱的光纤周界振动信号识别[J]. 光子学报, 2019, 48(2): 0206001. XIONG Xing-long, ZHANG Wan-tong, FENG Lei, LI Meng, MA Yu-zhao, FENG Shuai. Optical Fiber Perimeter Vibration Signal Recognition Based on Multifractal Spectrum[J]. ACTA PHOTONICA SINICA, 2019, 48(2): 0206001.

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