基于深度学习的海上通信电缆故障识别研究

打开文本图片集

中图分类号：TP183 文献标志码：A 文章编码：1672-7274（2026）02-0032-03

Research on Fault Recognition of Marine Communication Cables Based on Deep Learning CHE Yonghui

（CNOOC（China） Co.，Ltd.ShenzhenBranch，Shenzhen518o52，China）

Abstract： Undersea communication cables serve as critical infrastructure connecting ofshore platforms to land. Failures often occur in deep-sea regions， where complex environmental conditions limit the efciency of traditional inspection methods，which are also prone to interference from harsh weather and deep-sea pressre.In contrast，deep learning technologyposesses thecapabilitytoprocessmassivesensor data，automaticallylearnand extract complex features， significantlyimproving faultidentificationaccuracyand enablingreal-time monitoringofcableconditions topromptlydetect potentialrisks.To addressthis，this paper proposesa CNN-LSTMfault identification model forofshorecommunication cables incorporating attention mechanisms， aiming enhance the identification accuracy and stability.

Keywords： ofshore communication cables; fault identification; deep learning; attention mechanism

研究背景

海上通信电缆铺设在海底，连接不同的大陆和岛屿，构建起稳定、高速的通信通道，为经济、科技、文化等多个领域的发展提供了坚实支撑。（剩余2895字）