Biofouling Detection and Extent Estimation in Ocean Energy Harvesting Systems

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Marine Biology".

Deadline for manuscript submissions: 20 April 2025 | Viewed by 1682

Special Issue Editors


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Guest Editor
Institut de Recherche Dupuy de Lôme (UMR CNRS 6027 IRDL), University of Brest, 29238 Brest, France
Interests: fault detection and diagnosis; failure prognosis; cyberattack detection; fault-resilient control; machine learning
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Guest Editor
Electrical Engineering, University Paris Sud, Paris, France
Interests: electrical drives; fault diagnosis; fault tolerant control; renewable energy

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Guest Editor
Department of Electrical Engineering, Shanghai Maritime University, Shanghai 201306, China
Interests: fault diagnosis; fault tolerance fault detection; control systems; control theory; tidal and wave power
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Ocean energy is a promising renewable energy source due to its high energy density, stability, and predictability. However, biofouling remains a significant challenge, affecting the performance and longevity of the deployed systems.

This Special Issue aims to explore the latest advancements, emerging trends, and challenges in biofouling detection and its extent estimation for predictive maintenance purposes. Researchers and practitioners in the fields of renewable energy, marine biology, data science, and related disciplines are encouraged to contribute to this Special Issue. The submitted papers should provide insightful analyses and practical implications and highlight promising advancements in biofouling detection and estimation for tidal stream turbines.

Best,

Prof. Dr. Mohamed Benbouzid
Prof. Dr. Demba Diallo
Prof. Dr. Tianzhen Wang
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • ocean energy
  • biofouling
  • predictive maintenance

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Published Papers (1 paper)

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Research

14 pages, 1496 KiB  
Article
B-FLOWS: Biofouling Focused Learning and Observation for Wide-Area Surveillance in Tidal Stream Turbines
by Haroon Rashid, Houssem Habbouche, Yassine Amirat, Abdeslam Mamoune, Hosna Titah-Benbouzid and Mohamed Benbouzid
J. Mar. Sci. Eng. 2024, 12(10), 1828; https://doi.org/10.3390/jmse12101828 - 13 Oct 2024
Viewed by 1207
Abstract
Biofouling, the accumulation of marine organisms on submerged surfaces, presents significant operational challenges across various marine industries. Traditional detection methods are labor intensive and costly, necessitating the development of automated systems for efficient monitoring. The study presented in this paper focuses on detecting [...] Read more.
Biofouling, the accumulation of marine organisms on submerged surfaces, presents significant operational challenges across various marine industries. Traditional detection methods are labor intensive and costly, necessitating the development of automated systems for efficient monitoring. The study presented in this paper focuses on detecting biofouling on tidal stream turbine blades using camera-based monitoring. The process begins with dividing the video into a series of images, which are then annotated to identify and select the bounding boxes containing objects to be detected. These annotated images are used to train YOLO version 8 to detect biofouled and clean blades in the images. The proposed approach is evaluated using metrics that demonstrate the superiority of this YOLO version compared to previous ones. To address the issue of misdetection, a data augmentation approach is proposed and tested across different YOLO versions, showing its effectiveness in improving detection quality and robustness. Full article
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