首页|基于PSO-SVM的Φ-OTDR系统模式识别研究

基于PSO-SVM的Φ-OTDR系统模式识别研究

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针对相位敏感光时域反射仪(phase sensitive optical time domain reflectometer,Φ-OTDR)系统中误报率高的问题,提出一种多域特征提取与粒子群算法优化支持向量机(particle swarm optimization-support vector machine,PSO-SVM)相结合的模式识别算法.首先,对原始信号进行差分处理后提取时域特征,并利用小波包分解方法,通过验证不同分解层数下的事件分类准确率,设定最优分解层数为6层,提取差分信号的能量特征.然后以SVM分类器为基础,利用PSO算法优化SVM分类器参数,提高光纤振动信号识别准确率.最后利用Φ-OTDR事件数据集进行验证,实验结果表明,该模式识别算法达到了95.6％的振动事件分类准确率.
Pattern Recognition of Φ-OTDR System Based on PSO-SVM
To tackle with the high nuisance alarm rate in phase sensitive optical time domain reflectometer(Φ-OTDR)system,an algorithm for pattern recognition based on multi-domain functionalities and a vector particle cluster optimization machine(PSO-SVM)was proposed.Firstly,the characteristics of the time-domain differential signal were extracted,and the differential signal was decomposed by wavelet packet.By verifying the accuracy of event classification corresponding to different decomposition layers,the optimal decomposition layer was set to 6 layers to extract the energy characteristics of the signal.Then,based on the SVM classifier,the PSO algorithm was used to optimize the SVM settings to improve the accuracy of optical fiber vibration recognition.Finally,the proposed model reconnaissance algorithm was validated using the Φ-OTDR event dataset.Experimental results show that the suggested model recognition algorithm achieves a classification precision of 95.6％of vibratory events.

phase sensitive optical time domain reflectometer(Φ-OTDR)wavelet packet decompositionparticle swarm algorithm(PSO)support vector machine(SVM)mode recognition

朱宗玖、王宁

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安徽理工大学电气与信息工程学院,淮南 232001

相位敏感光时域反射仪(Φ-OTDR) 小波包分解 粒子群算法(PSO) 支持向量机(SVM) 模式识别

安徽省自然科学基金安徽省高等学校自然科学研究项目

1808085MF169KJ2018A0086

2024

10.12404/j.issn.1671-1815.2302411

科学技术与工程
中国技术经济学会

科学技术与工程

CSTPCD北大核心
影响因子：0.338
ISSN：1671-1815
年,卷(期)：2024.24(12)
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