摘要
为解决分布式相位敏感光时域反射计系统现有事件识别方法对于相似振动信号识别困难这一问题,提出了一种基于多尺度特征融合的相似信号识别方法。在该方法中,原始信号首先通过经验模态分解和小波包分解被分解为不同频率范围内的子信号。随后,分别提取原始信号和子信号的时频特征和近似熵特征,并利用主成分分析法对所提取的特征进行融合。最后,通过构建一个6层轻量反向传播(BP)神经网络分类器,训练分类模型并利用测试集验证模型分类度。该方法对小车经过和行走等相似信号的识别准确率可分别达到98.5%和98.0%,对于敲击和摇晃差异性大的信号的识别准确率可达100%。相比于直接从原始信号中提取特征并结合时频图的卷积神经网络方式,所提方法的综合识别准确率分别提高了8.4%与9.0%,相似信号的识别准确率分别提高了13.5%与12.4%。结果表明,该方法在保证差异性大的信号的高识别准确率的基础上,显著提高了相似信号的识别准确率,对于拓展分布式光纤传感的应用范围有重要的价值。
Objective A phase-sensitive optical time-domain reflectometer(φ-OTDR)system is a front monitoring and early warning technology that can acquire the location of disturbances in space and phase information of disturbances in time.With the advantages of high resolution,wide monitoring range,and strong anti-interference capability,this technology has been widely used in pipeline safety maintenance,intrusion warning,and large-equipment monitoring.However,due to the complex diversity of the application environment,the system suffers from low recognition accuracy and insufficient stability in actual use,particularly when similar signals are recognized in the system application.To solve these problems,this study proposes a similar-signal recognition method based on multiscale feature fusion.This method can effectively improve the recognition accuracy of similar signals while maintaining the recognition accuracy of the base signal.Methods The original signal is first decomposed into sub-signals in different frequency ranges using empirical mode decomposition(EMD)and wavelet packet decomposition(WPD).The original signal and individual sub-signals are then subjected to time-frequency feature extraction and approximate entropy feature extraction.The time-frequency features are used to evaluate the details of the time and frequency variations of the signal,the approximate entropy features are used to evaluate the complexity and regularity of the signal,and the multiscale signal decomposition and multi-feature extraction are used to amplify the feature differences between similar signals.Because the multiscale and multi-feature approach increases the dimensionality of the data,the proposed method utilizes principal component analysis(PCA)to combine high-dimensional features and reduce the dimensionality of system features,thereby improving system efficiency.Finally,the fused features are passed into a lightweight back-propagation(BP)neural network as input variables for signal data processing.Compared to other traditional neu
作者
宋文强
丁哲文
毛邦宁
徐贲
龚华平
康娟
赵春柳
Song Wenqiang;Ding Zhewen;Mao Bangning;Xu Ben;Gong Huaping;Kang Juan;Zhao Chunliu(College of Optical and Electronic Technology,China Jiliang University,Hangzhou 310018,Zhejiang,China)
出处
《中国激光》
EI
CAS
CSCD
北大核心
2024年第6期149-159,共11页
Chinese Journal of Lasers
基金
国家自然科学基金青年科学基金(62305319)。
关键词
光通信
相位敏感光时域反射计
时频特征
近似熵
多尺度特征融合
反向传播神经网络
optical communications
phase-sensitive optical time-domain reflectometer
time-frequency features
approximate entropy
multiscale feature fusion
back propagation neural network
