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Fault Detection and Isolation Methods in Subsea Observation Networks
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 a...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7571193/ https://www.ncbi.nlm.nih.gov/pubmed/32942675 http://dx.doi.org/10.3390/s20185273 |
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author | Xiao, Sa Yao, Jiajie Chen, Yanhu Li, Dejun Zhang, Feng Wu, Yong |
author_facet | Xiao, Sa Yao, Jiajie Chen, Yanhu Li, Dejun Zhang, Feng Wu, Yong |
author_sort | Xiao, Sa |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7571193 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75711932020-10-28 Fault Detection and Isolation Methods in Subsea Observation Networks Xiao, Sa Yao, Jiajie Chen, Yanhu Li, Dejun Zhang, Feng Wu, Yong Sensors (Basel) Article 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. MDPI 2020-09-15 /pmc/articles/PMC7571193/ /pubmed/32942675 http://dx.doi.org/10.3390/s20185273 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Xiao, Sa Yao, Jiajie Chen, Yanhu Li, Dejun Zhang, Feng Wu, Yong Fault Detection and Isolation Methods in Subsea Observation Networks |
title | Fault Detection and Isolation Methods in Subsea Observation Networks |
title_full | Fault Detection and Isolation Methods in Subsea Observation Networks |
title_fullStr | Fault Detection and Isolation Methods in Subsea Observation Networks |
title_full_unstemmed | Fault Detection and Isolation Methods in Subsea Observation Networks |
title_short | Fault Detection and Isolation Methods in Subsea Observation Networks |
title_sort | fault detection and isolation methods in subsea observation networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7571193/ https://www.ncbi.nlm.nih.gov/pubmed/32942675 http://dx.doi.org/10.3390/s20185273 |
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