基于BOTDR监测数据的光纤复合海底电缆状态预测

为了判断光纤复合海底电缆状态的发展趋势,及时发出故障预警信号,提出了基于加权最小二乘支持向量机(WLS-SVM)的海缆BOTDR监测数据多步预测模型。利用Birge-Massart策略计算实测数据小波分解后不同尺度上的阈值,使用软阈值法消除随机噪声对预测准确性的影响;在混沌序列分析的基础上,采用G-P算法进行相空间重构,确定最佳嵌入维数,同时验证频移时间序列的混沌特性;将重构相空间中的相点馈入到WLSSVM模型完成递归多步预测;最后对海缆两个典型位置处测点进行了频移6步预测。结果表明,递归6步预测的最大平均相对误差为1.80%,具有比标准支持向量机预测结果更高的预测精度和更好的适用性。

Multi#$NBSstep prediction of the state of submarine cable based on WLS-SVM

In order to judge the development trend of the state of optical fiberScomposite submarine cable and send timely the fault warning signals, the multiSstep forecast modelSof the distributed fiber-optic data based on weighted least squares support vector machine(WLS-SVM) is proposed. HierarchicalSthreshold method is used to calculateSthe threshold on different scales which is derived from wavelet decomposition of the measured data. SoftSthreshold method can be used to eliminate the influence ofSrandomSnoise onSthe accuracy of prediction. The phase space is reconstructed using G-P algorithm based on chaotic sequence analysis, then the optimalSembedding dimension can be determined and the chaotic characteristicsSof time seriesSof frequencySshift can be verified.STheSphase pointSof reconstructed phase spaceSis fed to theSWLS-SVM model to proceed recursiveSmulti-step prediction. Finally, 6 step prediction of frequency shift in two typical position of cable is carried out. The resultsSshow thatSthe maximum average relative error of recursiveS6Sstep predictionSis 1.80%,Swhich it hasShigher prediction accuracySand better applicability compared with theSstandardSsupport vector machine method.