The application of machine learning and low frequency sonar for subsea power cable integrity evaluation

Wenshuo Tang, David Flynn, Keith Brown, Robu Valentin, Xinyu Zhao

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

2 Citations (Scopus)

Abstract

Subsea power cables are essential assets for the electrical transmission and distribution networks. They are crucial in ensuring the security of electricity supply and supporting the global expansion in offshore renewable energy generation. After reviewing historical data on subsea cable failure modes, we established that existing monitoring systems do not account for over 70% of subsea cable failure modes. The current technologies focus on electrical failure modes and subsea cable asset management strategies are typically reactive or time based, with inspection limited to diver and/or ROV supported video footage which has several limitations, such as requiring good visibility, access to the cable, challenges in locating the cable and inability to identify failure modes at the interface of the seabed. To overcome these limitations, we propose an innovative sensor technology that can provide the in-situ integrity analysis of the subsea cable. In this paper, we applied low frequency sonar technology to undertake detailed and in-situ assessment of subsea cable integrity. Specifically, in our work, a wideband low frequency (LF) sonar scanning system is manufactured to collect acoustic response from different subsea power cable samples with different inner structure and external failure modes. In addition, accelerated life cycle testing was conducted by manually introduce controlled stages of corrosion and abrasion to the cables to obtain integrity data at various cable degradation levels. Seminal results provide a detailed library of LF sonar responses to cable type and failure mode variations. The results of preliminary data analysis demonstrate the ability to distinguish subsea cables by differences in diameter and cable types and achieve an overall 95%+ accuracy rate to detect different cable degradation stages.

Original languageEnglish
Title of host publicationOCEANS 2019 MTS/IEEE Seattle, OCEANS 2019
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISBN (Electronic)9780578576183
DOIs
Publication statusPublished - 2019
Externally publishedYes
Event2019 OCEANS MTS/IEEE Seattle, OCEANS 2019 - Seattle, United States
Duration: 27 Oct 201931 Oct 2019

Publication series

NameOCEANS 2019 MTS/IEEE Seattle, OCEANS 2019

Conference

Conference2019 OCEANS MTS/IEEE Seattle, OCEANS 2019
CountryUnited States
CitySeattle
Period27/10/1931/10/19

Keywords

  • Cable condition monitoring
  • Life cycle test
  • Machine learning classification
  • Sonar

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