基于VMD-GAF-MCNN的多传感融合海缆裸露状态识别

王伟; 汪文东; 游鹏; 肖泽鑫; 安博文; 崔桂艳

doi:10.13756/j.gtxyj.2025.240060

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基于VMD-GAF-MCNN的多传感融合海缆裸露状态识别

光纤光缆技术与应用 | 更新时间：2025-06-18

- 基于VMD-GAF-MCNN的多传感融合海缆裸露状态识别
- Identification of Exposed Submarine Cable Status based on Optimized VMD-GAF-MCNN for Multi Sensor Fusion
- 光通信研究 2025年第3期
- 作者机构：
  
  1.国家能源集团东台海上风电有限责任公司，江苏　东台　 224200
  2.国华（江苏）风电有限公司，江苏　东台　 224200
  3.上海安馨信息科技有限公司，上海　 201306
- 作者简介：
  
  王伟（1989-），男，安徽芜湖人。工程师，学士，主要研究方向为海上风电等新能源产业生产运维及安全管理。
  安博文，教授。E-mail：1363216917@qq.com
- 基金信息：
- DOI：10.13756/j.gtxyj.2025.240060
  中图分类号： TN929.11
- 收稿日期：2024-03-13，
  
  修回日期：2024-04-10，
  
  纸质出版日期：2025-06-10
- 稿件说明：
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王伟，汪文东，游鹏，等. 基于VMD-GAF-MCNN的多传感融合海缆裸露状态识别[J]. 光通信研究，2025(3): 240060.

Wang W, Wang W D, You P, et al. Identification of Exposed Submarine Cable Status based on Optimized VMD-GAF-MCNN for Multi Sensor Fusion[J]. Study on Optical Communications, 2025(3): 240060.
王伟，汪文东，游鹏，等. 基于VMD-GAF-MCNN的多传感融合海缆裸露状态识别[J]. 光通信研究，2025(3): 240060. DOI： 10.13756/j.gtxyj.2025.240060.

Wang W, Wang W D, You P, et al. Identification of Exposed Submarine Cable Status based on Optimized VMD-GAF-MCNN for Multi Sensor Fusion[J]. Study on Optical Communications, 2025(3): 240060. DOI： 10.13756/j.gtxyj.2025.240060.

摘要

【目的】

桩基冲刷及海缆登陆段的冲刷监测常采用多波束（侧扫）声呐方法进行扫描测量，根据扫测图像评判冲刷程度，但该方法缺乏实时性，仅在运维时间才获取相关数据。目前海缆冲刷监测常采用温度和振动分析，但现场采集的分布式光纤传感信号具有非平稳、非线性且易受噪声干扰的特点，同时存在单一传感器提取特征信息不完备的问题，文章提出了一种基于优化变分模态分解（VMD）、格拉姆角场（GAF）和多尺度卷积神经网络（MCNN）的多传感融合海缆裸露状态识别方法，提高了登陆段海缆裸露状态识别的准确率。

【方法】

文章所提优化VMD-GAF-MCNN使用信号处理和深度学习的方法实现。首先，提出一种基于希尔伯特变换（HT）计算包络峰度最大值的VMD模数优化方法，利用优化VMD对分布式光纤传感信号进行分解，并利用相关系数法筛选本征模态分量（IMF），提取各阶IMF与原始信号相关系数最大的IMF分量；其次，提出一种GAF二维图像编码方法，利用GAF将分布式光纤测温系统（DTS）和分布式光纤声学传感系统（DAS）采集的光纤温度和振动信号筛选出的IMF分量编码成二维图像；最后，设计MCNN结构，随机划分训练集和测试集，将训练集输入网络进行训练，测试集验证网络的有效性，实现海缆裸露状态识别。

【结果】

利用现场采集的海缆光纤温度和振动数据进行验证，测试准确率达到99.85%，相较于单一传感器分别提高了0.95%和0.85%。

【结论】

文章所提方法能够准确识别登陆段海缆裸露状态，为制定海缆运维策略提供技术支撑。

Abstract

【Objective】

The multi-beam (side scan) sonar method is commonly employed to scan and measure the erosion of pile foundations and the landing sections of submarine cables. The degree of erosion is assessed based on the scanned images; however

this method lacks real-time capability

and relevant data are only obtained during maintenance operations. Currently

temperature analysis and vibration analysis are frequently used for monitoring the erosion of submarine cables. However

the distributed optical fiber sensing signals collected on-site exhibit characteristics of non-stationarity

non-linearity

and susceptibility to noise interference. Additionally

there is an issue of incomplete feature information extracted by a single sensor. To address these challenges

a multi-sensor fusion method for recognizing the bare state of submarine cables is proposed. This method leverages optimized Variational Mode Decomposition (VMD)

Gramian Angular Field (GAF)

and Multi-scale Convolutional Neural Networks (MCNN) to enhance the accuracy of recognizing the bare state of submarine cables in the landing section.

【Methods】

The optimized VMD-GAF-MCNN method proposed in this paper integrates signal processing and deep learning techniques. Firstly

an optimized VMD method based on Hilbert Transform (HT) is introduced to calculate the maximum envelope kurtosis. This optimized VMD is used to decompose the distributed optical fiber sensing signal

and the correlation coefficient method is employed to screen the Intrinsic Mode Functions (IMFs). The IMF component with the highest correlation coefficient to the original signal is extracted. Secondly

a two-dimensional image encoding method using GAF is proposed. The selected IMF components from the optical fiber temperature and vibration signals

collected by Distributed Temperature Sensing (DTS) and Distributed Acoustic Sensing (DAS)

are encoded into two-dimensional images using GAF. Finally

a MCNN structure is designed

with the training and test sets randomly divided. The training set is used to train the network

while the test set verifies the network's effectiveness in identifying the exposed state of the submarine cable.

【Results】

The verification was conducted using on-site collected temperature and vibration data from submarine cable fibers. The test accuracy achieved was 99.85%

which is 0.95% and 0.85% higher than that obtained using a single sensor

respectively.

【Conclusion】

The method proposed in this paper is capable of accurately identifying the exposed state of the submarine cable in the landing section

thereby providing robust technical support for developing operation and maintenance strategies for submarine cables.

关键词

Keywords

references

侯帅 , 王毅松 , 朱闻博 , 等 . 高压海底电缆监测技术与应用综述 [J ] . 南方电网技术 , 2023 , 17 ( 5 ): 49 - 58 .

Hou S , Wang Y S , Zhu W B , et al . Review of High Voltage Submarine Cable Monitoring Technology and Applications [J ] . Southern Power System Technology , 2023 , 17 ( 5 ): 49 - 58 .

曹文琛 , 张涛 , 郑宇恒 , 等 . 不同地形管道光缆信号综合分析研究 [J ] . 光通信研究 , 2022 ( 2 ): 35 - 39 .

Cao W C , Zhang T , Zheng Y H , et al . Synthetic Analysis and Research of Pipeline Optical Cable Signal in Different Terrain [J ] . Study on Optical Communications , 2022 ( 2 ): 35 - 39 .

Huang N E , Shen Z , Long S R , et al . The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-stationary Time Series Analysis [J ] . Proceedings of the Royal Society of London Series A , 1998 , 454 ( 1971 ): 903 - 998 .

Dragomiretskiy K , Zosso D . Variational Mode Decomposition [J ] . IEEE Transactions on Signal Processing , 2014 , 62 ( 3 ): 531 - 544 .

Febrian R , Halim B M , Christina M , et al . Facial Expression Recognition Using Bidirectional LSTM-CNN [J ] . Procedia Computer Science , 2023 , 216 : 39 - 47 .

Wang M , Feng H , Qi D , et al . Φ-OTDR Pattern Recognition based on CNN-LSTM [J ] . Optik , 2023 , 272 : 170380 .

贺志将 , 李前 , 王彦超 , 等 . 基于VMD-LSTM的奶牛动态称量算法 [J ] . 农业机械学报 , 2022 , 53 ( S2 ): 234 - 240 .

He Z J , Li Q , Wang Y C , et al . Cow Dynamic Weighing Algorithm based on VMD-LSTM [J ] . Transactions of the Chinese Society for Agricultural Machinery , 2022 , 53 ( S2 ): 234 - 240 .

Zheng S , Zhong Q , Chen X , et al . The Rail Surface Defects Recognition via Operating Service Rail Vehicle Vibrations [J ] . Machines , 2022 , 10 ( 9 ): 796 .

谷玉海 , 朱腾腾 , 饶文军 , 等 . 基于EMD二值化图像和CNN的滚动轴承故障诊断 [J ] . 振动.测试与诊断 , 2021 , 41 ( 1 ): 105 - 113 .

Gu Y H , Zhu T T , Rao W J , et al . Fault Diagnosis for Rolling Bearing based on EMD Binarization Image and CNN [J ] . Journal of Vibration, Measurement & Diagnosis , 2021 , 41 ( 1 ): 105 - 113 .

周天 , 司吉坤 , 杜伟东 , 等 . 采用GAF-D3Net深度学习网络的水下目标有源识别方法 [J ] . 声学学报 , 2023 , 48 ( 5 ): 950 - 958 .

Zhou T , Si J K , Du W D , et al . Active Recognition of Underwater Targets Using GAF-D3Net Deep Learning Network [J ] . Acta Acustica , 2023 , 48 ( 5 ): 950 - 958 .

Zhong Q , Xu E , Shi Y , et al . Fault Diagnosis of the Hydraulic Valve Using a Novel Semi-supervised Learning Method based on Multi-sensor Information Fusion [J ] . Mechanical Systems and Signal Processing , 2023 , 189 : 110093 .

刘子惠 , 胡凯 , 郑新龙 , 等 . 海底光纤复合缆机械损伤有限元仿真分析综述 [J ] . 光通信研究 , 2023 ( 2 ): 45 - 54 .

Liu Z H , Hu K , Zheng X L , et al . Overview of Finite Element Simulation Analysis for Mechanical Damage of Submarine Optical Fiber Composite Power Cable [J ] . Study on Optical Communications , 2023 ( 2 ): 45 - 54 .

Sun J , Gu X , He J , et al . A Robust Approach of Multi-sensor Fusion for Fault Diagnosis Using Convolution Neural Network [J ] . Journal of Dynamics, Monitoring and Diagnostics , 2022 , 1 ( 2 ): 103 - 110 .

Cui J , Zhong Q , Zheng S , et al . A Lightweight Model for Bearing Fault Diagnosis based on Gramian Angular Field and Coordinate Attention [J ] . Machines , 2022 , 10 ( 4 ): 282 .

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