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基于BS-1DCNN的海缆振动信号识别

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光纤振动信号是非线性的,传统的非线性振动信号识别方法通常需要信号分析和特征选择,既耗时又复杂.本文提出一种光纤振动信号识别新方法,可以直接提取特征,对原始信号进行分类,简化识别过程.本方法用支持向量机代替Softmax分类器,优化一维卷积神经网络(one-dimensional convolution neural network,1DCNN),以提高 1DCNN结果在小样本条件下的稳定性.采用鸟群算法(bird swarm algorithm,BSA)对支持向量机(support vector machine,SVM)参数进行了优化,有效地提高识别精度.将本文提出的BS-1DCNN方法与1DCNN、VMD-GA-SVM、VMD-PSO-SVM、VMD-BSA-SVM共 4 种方法进行比较,结果表明,BS-1DCNN在识别准确率和测试时间方面性能表现良好.该算法能有效提高海缆振动信号识别率,且在不同样本比例下均能达到较好的识别效果.
Submarine cable vibration signal recognition based on BS-1DCNN
Optical fiber vibration signals are nonlinear.Conventional nonlinear vibration signals recognition methods usually require signal analysis and features selection,both time-consuming and complex.In this paper,we propose a new method for optical fiber vibration signals recognition that can directly extract features,classify original signals and simplify the recognition process.In our method,the one-dimensional convolutional neural network(1DCNN)is im-proved by replacing the Softmax classifier with a support vector machine,so as to improve the stability of 1DCNN res-ults under small sample conditions.Moreover,the bird swarm algorithm(BSA)is applied to optimize the support vector machine(SVM)parameters,improving the recognition accuracy effectively.The performance of the proposed method is compared with that of other four methods,namely 1DCNN,variational mode decomposition(VMD)and SVM optim-ized by genetic algorithm(VMD-GA-SVM),VMD and SVM optimized by particle swarm optimization(VMD-PSO-SVM),VMD and SVM optimized by bird wwarm algorithm(VMD-BSA-SVM).The results show that our BS-1DCNN method performs better in accuracy and timeliness and the recognition accuracy is satisfactory.The algorithm can effect-ively improve the recognition rate of marine cable vibration signals,and can achieve better recognition effect under dif-ferent sample proportions.

vibration signalfault identificationbird swarm optimizationone-dimensional convolutional neural net-worksupport vector machinefeature selectionparameter optimizationsupport vector machine

尚秋峰、郭家兴、黄达

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华北电力大学 电子与通信工程系,河北 保定 071003

华北电力大学 河北省电力物联网技术重点实验室,河北 保定 071003

华北电力大学 保定市光纤传感与光通信技术重点实验室,河北 保定 071003

振动信号 故障识别 鸟群优化 一维卷积神经网络 支持向量机 特征选择 参数优化 支持向量机

国家自然科学基金项目国家自然科学基金项目河北省自然科学基金项目

61775057E2019502179

2024

10.11992/tis.202210006

智能系统学报
中国人工智能学会　哈尔滨工程大学

智能系统学报

CSTPCD北大核心
影响因子：0.672
ISSN：1673-4785
年,卷(期)：2024.19(4)
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