基于VMD-BSA-SVM的海缆振动信号识别方法

Submarine Cable Vibration Signal Identification Method Based on VMD-BSA-SVM

光电复合海缆状态在线监测及故障识别是保障跨海输电和通信传输正常运行的关键。为了避免直接去噪导致的信号失真影响目标特征的提取,利用变分模态分解(VMD)算法直接从含噪的振动信号中提取特征。基于布里渊光时域分析仪的海缆振动信号模拟实验系统获得锚砸、冲刷、摩擦三种工况下的海缆振动信号。取三类振动信号各200组,利用VMD算法获得本征模态函数分量,并将各个分量的能量、能量熵、峭度组合作为特征向量。将80%的特征向量作为训练集,20%的特征向量作为测试集,并输入基于鸟群算法(BSA)的支持向量机(SVM)中进行分类。实验结果表明,相比其他SVM,BSA-SVM的分类准确率更高,可达到99.17%,且运行时间较短。

Online monitoring and fault identification of submarine cable are fundamental technology for ensuring the normal operation of crosssea transmission and communication transmission.To avoid signal distortion due to direct denoising,which affects the extraction of target features,in this paper,the variational mode decomposition(VMD)algorithm is applied to extract features directly from noisy vibration signals.Using the Brillouin optical time domain analysis experimental system for monitoring the submarine cable vibration,the vibration signals of submarine cable under the conditions of anchoring,scouring,and friction are obtained.Three types of vibration signals are divided into 200 groups,and the intrinsic mode function components are obtained using the VMD algorithm.Furthermore,the energy,energy entropy,and kurtosis combinations of each component are obtained as eigenvectors.Using 80%and 20%of the feature vectors as the training and test sets,respectively,the data are classified by inputting them into the support vector machine(SVM)based on the bird swarm algorithm(BSA).The experimental results show that compared with other SVMs,the classification accuracy of BSASVM is higher,reaching 99.17%,and the running time is shorter.