[1]SHANG Qiufeng,GUO Jiaxing,HUANG Da.Submarine cable vibration signal recognition based on BS-1DCNN[J].CAAI Transactions on Intelligent Systems,2024,19(4):874-884.[doi:10.11992/tis.202210006]
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Submarine cable vibration signal recognition based on BS-1DCNN

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 4 Page number: 874-884 Column: 学术论文—机器感知与模式识别 Public date: 2024-07-05
Title:
Submarine cable vibration signal recognition based on BS-1DCNN
Author(s):
SHANG Qiufeng1; 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
Keywords:
vibration signal; fault identification; bird swarm optimization; one-dimensional convolutional neural network; support vector machine; feature selection; parameter optimization; support vector machine
CLC:
TP309
DOI:
10.11992/tis.202210006
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.
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