TY - GEN
T1 - Sensing Technologies and Artificial Intelligence for Subsea Power Cable Asset Management
AU - Tang, Wenshuo
AU - Flynn, David
AU - Robu, Valentin
N1 - Funding Information:
ACKNOWLEDGMENT The research reported within this paper were funded by the Engineering Physical Sciences Research Council (EPSRC) Offshore Robotics for the Certification of Assets (ORCA) hub [EP/R026173/1] and EPSRC Holistic Operation and Maintenance for Energy from Offshore Wind Farms (HOME) [EP/P009743/1] projects. Industrial support from the European Marine Energy Centre (EMEC) and JDR cables, in the provision of subsea power cable samples, and Hydrason Ltd in the provision of low frequency sonar instrumentation.
Funding Information:
The research reported within this paper were funded by the Engineering Physical Sciences Research Council (EPSRC) Offshore Robotics for the Certification of Assets (ORCA) hub [EP/R026173/1] and EPSRC Holistic Operation and Maintenance for Energy from Offshore Wind Farms (HOME) [EP/P009743/1] projects. Industrial support from the European Marine Energy Centre (EMEC) and JDR cables, in the provision of subsea power cable samples, and Hydrason Ltd in the provision of low frequency sonar instrumentation.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/7/20
Y1 - 2021/7/20
N2 - In this paper, we present two novel sensor systems to support the integrity analysis and asset management of subsea power cables. Firstly, we provide an example of a customized in-situ monitoring collar for subsea power cable monitoring, representing a first in full displacement monitoring of subsea power cables. Secondly, we provide results from an advanced Low Frequency (LF) sonar system, demonstrating the technology’s ability to differentiate different power cable types and varying levels of degradation. Results from laboratory experiments verify the ability of the monitoring collar to wirelessly monitor cable dynamics and an accuracy of 95% from LF Sonar analysis utilizing the state-of-the-art deep learning method (Convolution Neural Network) for detecting different level of cable damage. Our results provide a new ability to perform in-situ cross sectional analysis of subsea cables, and our monitoring collar concept can provide new information into real-time cable dynamics.
AB - In this paper, we present two novel sensor systems to support the integrity analysis and asset management of subsea power cables. Firstly, we provide an example of a customized in-situ monitoring collar for subsea power cable monitoring, representing a first in full displacement monitoring of subsea power cables. Secondly, we provide results from an advanced Low Frequency (LF) sonar system, demonstrating the technology’s ability to differentiate different power cable types and varying levels of degradation. Results from laboratory experiments verify the ability of the monitoring collar to wirelessly monitor cable dynamics and an accuracy of 95% from LF Sonar analysis utilizing the state-of-the-art deep learning method (Convolution Neural Network) for detecting different level of cable damage. Our results provide a new ability to perform in-situ cross sectional analysis of subsea cables, and our monitoring collar concept can provide new information into real-time cable dynamics.
KW - artificial intelligence
KW - integrity analysis
KW - sonar
KW - subsea cable
UR - http://www.scopus.com/inward/record.url?scp=85114558874&partnerID=8YFLogxK
U2 - 10.1109/ICPHM51084.2021.9486586
DO - 10.1109/ICPHM51084.2021.9486586
M3 - Conference contribution
BT - 2021 IEEE International Conference on Prognostics and Health Management (ICPHM)
PB - IEEE
T2 - 2021 IEEE International Conference on Prognostics and Health Management
Y2 - 7 June 2021 through 9 June 2021
ER -