Xiao Sa, Yao Jiajie, Chen Yanhu, Li Dejun, Zhang Feng, Wu Yong
State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China.
Sensors (Basel). 2020 Sep 15;20(18):5273. doi: 10.3390/s20185273.
Subsea observation networks have gradually become the main means of deep-sea exploration. The reliability of the observation network is greatly affected by the severe undersea conditions. This study mainly focuses on theoretical research and the experimental platform verification of high-impedance and open-circuit fault detection for an underwater observation network. With the aid of deep learning, we perform the fault detection and prediction of the network operation. For the high-impedance and open-circuit fault detection of submarine cables, the entire system is modeled and simulated, and the voltage and current values of the operating nodes under different fault types are collected. Numerous calibrated data samples are supervised by a deep learning algorithm, and a fault location system model is built in the laboratory to verify the feasibility and superiority of the scheme. This paper also studies the fault isolation of the observation network, focusing on the communication protocol and the design of the fault isolation system. Experimental results verify the effectiveness of the proposed algorithm for the location and prediction of high-impedance and open-circuit faults, and the feasibility of the fault isolation system has also been verified. Moreover, the proposed methods greatly improve the reliability of undersea observation network systems.
海底观测网络已逐渐成为深海探测的主要手段。观测网络的可靠性受到恶劣海底条件的极大影响。本研究主要聚焦于水下观测网络高阻抗和开路故障检测的理论研究及实验平台验证。借助深度学习，我们对网络运行进行故障检测与预测。针对海底电缆的高阻抗和开路故障检测，对整个系统进行建模与仿真，并采集不同故障类型下运行节点的电压和电流值。大量校准数据样本由深度学习算法进行监督，在实验室构建故障定位系统模型以验证该方案的可行性和优越性。本文还研究了观测网络的故障隔离，重点关注通信协议和故障隔离系统的设计。实验结果验证了所提算法对高阻抗和开路故障定位及预测的有效性，同时也验证了故障隔离系统的可行性。此外，所提方法极大地提高了海底观测网络系统的可靠性。
