基于VMD与AdaBoost-SCN的海缆振动信号识别方法

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振动与冲击 ›› 2023, Vol. 42 ›› Issue (19) : 231-239.

论文

尚秋峰1，2，3，黄达1，巩彪1

作者信息 +

Submarine cable vibration signal identification method based on VMD and AdaBoost-SCN

SHANG Qiufeng1,2,3, HUANG Da1, GONG Biao1

Author information +

文章历史 +

摘要

海底光缆的在线监测和振动信号识别是保证其正常运行的关键技术。搭建了基于布里渊光时域分析系统，模拟不同工况下的海缆振动信号。针对海缆振动信号信息丰富、信噪比低，使用单一随机配置网络(Stochastic Con¬figuration Network, SCN)模型对信号识别准确率不高的问题，提出了自适应增强(Adaptive Boosting, AdaBoost)算法优化的随机配置网络(AdaBoost-SCN)识别方法。首先用变分模态分解(Varia¬tional Modal Decomposition，VMD)算法分解海缆振动信号，构建特征向量；然后采用AdaBoost-SCN算法对振动信号分类。结果表明，所提方法有着很高的精度，并且具有很强的鲁棒性与泛化能力，提高了布里渊光时域分析系统振动信号识别的有效性。

Abstract

Online monitoring and vibration signal identification of submarine cables are the key technologies to en¬sure their normal operation. Based on brillouin optical time domain analysis system, the vibration signal of submarine cable under different working conditions was simulated. Aiming at the problem that the signal recognition accuracy of the stochastic configuration network (SCN) model is not high, the adaptive boosting (AdaBoost) algorithm optimization (Ada-Boost-SCN) is proposed. Firstly, the variational modal decomposition (VMD) algorithm is used to decompose the vibration signal of the submarine cable and construct the eigenvector. Then, the AdaBoost-SCN algorithm is used to classify the vibration signal. The results show that the proposed method has high accuracy, robustness and generalization ability, which improves the effectiveness of vibration signal recognition in brillouin optical time domain analysis system.

导出引用

尚秋峰1，2，3，黄达1，巩彪1. 基于VMD与AdaBoost-SCN的海缆振动信号识别方法[J]. 振动与冲击, 2023, 42(19): 231-239

SHANG Qiufeng1,2,3, HUANG Da1, GONG Biao1. Submarine cable vibration signal identification method based on VMD and AdaBoost-SCN[J]. Journal of Vibration and Shock, 2023, 42(19): 231-239

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