Subsea power cable health management using machine learning analysis of low frequency wide band sonar data

Tang, W., Brown, K., Mitchell, D., Blanche, J. and Flynn, D. (2023) Subsea power cable health management using machine learning analysis of low frequency wide band sonar data. Energies, 16(17), 6172. (doi: 10.3390/en16176172)

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.

• Item Type: Articles
• Additional Information: This research was funded by the EPSRC project on HOME-Offshore (grant EP/P009743/1). The experiments were supported by Hydrason Ltd. in the provision of their sensing technology and the Ocean Systems Laboratory in Heriot Watt University. The authors also want to acknowledge the support of JDR Cable Systems Ltd. and European Marine Energy Centre (EMEC) in the provision of cable samples. The APC was funded by Heriot Watt University.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Blanche, Dr Jamie and Mitchell, Mr Daniel and Flynn, Professor David and Tang, Mr Wenshuo

Creator Roles

Tang, W.: Methodology, Software, Validation, Data curation, Writing – original draft, Visualization
Mitchell, D.: Software, Investigation
Blanche, J.: Formal analysis
Flynn, D.: Conceptualization, Formal analysis, Writing – review and editing, Supervision, Project administration, Funding acquisition

• Authors: Tang, W., Brown, K., Mitchell, D., Blanche, J., and Flynn, D.
• College/School: College of Science and Engineering > School of Engineering; College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity; College of Science and Engineering > School of Engineering > Systems Power and Energy
• Journal Name: Energies
• Publisher: MDPI
• ISSN: 1996-1073
• ISSN (Online): 1996-1073
• Copyright Holders: Copyright © 2023 by the authors
• First Published: First published in Energies 16(17):6172
• Publisher Policy: Reproduced under a Creative Commons licence