Subsea Power Cable Health Management Using Machine Learning Analysis of Low-Frequency Wide-Band Sonar Data

Wenshuo Tang (), Keith Brown, Daniel Mitchell, Jamie Blanche and David Flynn
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Wenshuo Tang: James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK
Keith Brown: The School of Engineering and Physical Sciences (EPS), Heriot Watt University, Edinburgh EH14 4AS, UK
Daniel Mitchell: James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK
Jamie Blanche: James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK
David Flynn: James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK

Energies, 2023, vol. 16, issue 17, 1-17

Abstract: Subsea power cables are critical assets for electrical transmission and distribution networks, and highly relevant to regional, national, and international energy security and decarbonization given the growth in offshore renewable energy generation. Existing condition monitoring techniques are restricted to highly constrained online monitoring systems that only prioritize internal failure modes, representing only 30% of cable failure mechanisms, and has limited capacity to provide precursor indicators of such failures or damages. To overcome these limitations, we propose an innovative fusion prognostics approach that can provide the in situ integrity analysis of the subsea cable. In this paper, we developed low-frequency wide-band sonar (LFWBS) technology to collect acoustic response data from different subsea power cable sample types, with different inner structure configurations, and collate signatures from induced physical failure modes as to obtain integrity data at various cable degradation levels. We demonstrate how a machine learning approach, e.g., SVM, KNN, BP, and CNN algorithms, can be used for integrity analysis under a hybrid, holistic condition monitoring framework. The results of data analysis demonstrate the ability to distinguish subsea cables by differences of 5 mm in diameter and cable types, as well as achieving an overall 95%+ accuracy rate to detect different cable degradation stages. We also present a tailored, hybrid prognostic and health management solution for subsea cables, for cable remaining useful life (RUL) prediction. Our findings addresses a clear capability and knowledge gap in evaluating and forecasting subsea cable RUL. Thus, supporting a more advanced asset management and planning capability for critical subsea power cables.

Keywords: subsea power cable; machine learning; sensing; condition monitoring; asset integrity; health management; low-frequency sonar (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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