基于BS-1DCNN的海缆振动信号识别

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Title:

Submarine cable vibration signal recognition based on BS-1DCNN

作者:

尚秋峰^1,2,3, 郭家兴¹, 黄达¹

1. 华北电力大学 电子与通信工程系, 河北 保定 071003;
2. 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;
3. 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

Author(s):

SHANG Qiufeng^1,2,3, GUO Jiaxing¹, HUANG Da¹

1. Department of Electronic and Communication Engineering, North China Electric Power University, Baoding 071003, China;
2. Hebei Key Laboratory of Power Internet of Things Technology, North China Electric Power University, Baoding 071003, China;
3. Baoding Key Laboratory of Optical Fiber Sensing and Optical Communication Technology, North China Electric Power University, Baoding 071003, China

关键词:

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

Keywords:

vibration signal; fault identification; bird swarm optimization; one-dimensional convolutional neural network; support vector machine; feature selection; parameter optimization; support vector machine

分类号:

TP309

DOI:

10.11992/tis.202210006

摘要:

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。

Abstract:

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 improved by replacing the Softmax classifier with a support vector machine, so as to improve the stability of 1DCNN results 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 optimized 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 effectively improve the recognition rate of marine cable vibration signals, and can achieve better recognition effect under different sample proportions.