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Advanced Structural Health Monitoring for Energy Performance and Safety in Marine Renewable Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A: Sustainable Energy".

Deadline for manuscript submissions: 25 August 2025 | Viewed by 354

Special Issue Editors


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Guest Editor
College of Engineering, Ocean University of China, Qingdao, China
Interests: marine engineering; structural health monitoring; structural safety assessment; dynamic analysis; damage detection; system identification; signal processing; model updating; offshore platform; offshore wind turbine; marine risers; machine learning; deep learning

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Guest Editor
College of Marine Science and Technology, China University of Geosciences, Wuhan 430074, China
Interests: dynamic analysis of floating offshore wind turbine; structural health monitoring; modal analysis; structural damage identification
School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, China
Interests: structural health monitoring and digital twin; virtual hybrid model experiment in ocean engineering; fluid–structure interaction of marine strctures; renewable energy systems; machine learning

Special Issue Information

Dear Colleagues,

Marine renewable systems—such as offshore wind turbines, floating photovoltaic platforms, wave energy converters, deep-sea risers, and submarine pipelines—play a vital role in the sustainable exploitation of ocean energy resources. Ensuring their operational safety and energy performance is essential for the long-term reliability and efficiency of marine energy infrastructure.

Structural health monitoring (SHM) offers a powerful solution for the real-time assessment of system integrity and performance by capturing critical data on structural behavior and external loads. Effective SHM not only helps detect damage and deterioration but also supports energy efficiency by enabling timely maintenance, optimized operation, and extended service life.

However, marine environments pose unique challenges for SHM, such as harsh environmental conditions, limited sensing coverage, and complex dynamic interactions. To address these, advanced data-driven techniques—especially those based on machine learning and hybrid physical–data models—are being increasingly adopted for accurate condition assessment and decision support.

This Special Issue invites contributions that explore innovative SHM methods for improving the energy performance, safety, and operational reliability of marine renewable systems. We particularly encourage research that integrates intelligent data analysis, monitoring technologies, and system-level insights to enhance energy utilization and structural resilience.

Topics of interest include, but are not limited to, the following:

  • Experimental and field tests of marine engineering structures and renewable energy systems.
  • Quick and accurate structural response simulation and analysis.
  • Machine and deep learning-aided condition assessment.
  • Data-driven and physics-informed SHM methods.
  • Surrogate models and digital twins.
  • System and load identification techniques.
  • Damage identification techniques robust to various uncertainties.
  • Uncertainty quantification in SHM.
  • Value quantification and new function of SHM.
  • New trends and challenges in machine learning-aided SHM.
  • SHM-related data acquisition equipment.
  • Offshore operation optimization based on monitored data.
  • Energy performance monitoring.

Prof. Dr. Mingqiang Xu
Prof. Dr. Hongchao Lu
Dr. Haojie Ren
Guest Editors

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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. Energies is an international peer-reviewed open access semimonthly 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

  • structural health monitoring
  • marine structure
  • renewable energy system
  • dynamic response analysis
  • damage detection
  • safety assessment
  • system identification
  • machine learning
  • energy performance monitoring
  • condition-based maintenance
  • digital twin
  • lifetime energy output
  • offshore operation optimization

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Published Papers (1 paper)

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Research

19 pages, 2969 KiB  
Article
Damage Detection for Offshore Wind Turbines Subjected to Non-Stationary Ambient Excitations: A Noise-Robust Algorithm Using Partial Measurements
by Ning Yang, Peng Huang, Hongning Ye, Wuhua Zeng, Yusen Liu, Juhuan Zheng and En Lin
Energies 2025, 18(14), 3644; https://doi.org/10.3390/en18143644 - 10 Jul 2025
Abstract
Reliable damage detection in operational offshore wind turbines (OWTs) remains challenging due to the inherent non-stationarity of environmental excitations and signal degradation from noise-contaminated partial measurements. To address these limitations, this study proposes a robust damage detection method for OWTs under non-stationary ambient [...] Read more.
Reliable damage detection in operational offshore wind turbines (OWTs) remains challenging due to the inherent non-stationarity of environmental excitations and signal degradation from noise-contaminated partial measurements. To address these limitations, this study proposes a robust damage detection method for OWTs under non-stationary ambient excitations using partial measurements with strong noise resistance. The method is first developed for a scenario with known non-stationary ambient excitations. By reformulating the time-domain equation of motion in terms of non-stationary cross-correlation functions, structural stiffness parameters are estimated using partially measured acceleration responses through the extended Kalman filter (EKF). To account for the more common case of unknown excitations, the method is enhanced via the extended Kalman filter under unknown input (EKF-UI). This improved approach enables the simultaneous identification of the physical parameters of OWTs and unknown non-stationary ambient excitations through the data fusion of partial acceleration and displacement responses. The proposed method is validated through two numerical cases: a frame structure subjected to known non-stationary ground excitation, followed by an OWT tower under unknown non-stationary wind and wave excitations using limited measurements. The numerical results confirm the method’s capability to accurately identify structural damage even under significant noise contamination, demonstrating its practical potential for OWTs’ damage detection applications. Full article
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