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Monitoring and Evaluation of Marine Engineering Equipment and Structures—2nd Edition
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Monitoring and Evaluation of Marine Engineering Equipment and Structures—2nd Edition

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

Deadline for manuscript submissions: 15 June 2026 | Viewed by 3468

Special Issue Editors

Dr. Ziguang Jia

E-Mail Website
Guest Editor
School of Chemical Engineering, Ocean and Life Sciences, Dalian University of Technology, Panjin 124221, China
Interests: marine structure monitoring and assessment; fiber optic sensing technology; pipeline integrity management
Special Issues, Collections and Topics in MDPI journals
Dr. Peng Zhang

E-Mail Website
Guest Editor
College of Transportation Engineering, Dalian Maritime University, Dalian, China
Interests: marine engineering structures; structural health monitoring; structural vibration control
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yi Huang

E-Mail Website
Guest Editor
School of Naval Architecture and Ocean Engineering, Dalian University of Technology, Dalian 116024, China
Interests: structural analysis and environmental intensity of ships and marine structures corrosion protection for ships and marine engineering; damage evolution mechanism and life cycle safety management of offshore platform structures
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Following the success of the first edition, the second edition of this Special Issue, “Monitoring and Evaluation of Marine Engineering Equipment and Structures—2nd Edition,” aims to capture the latest advances in marine structural health monitoring (SHM)—a field now central to deep-sea oil/gas exploitation, large-scale offshore wind development, and low-carbon marine infrastructure goals.

Marine structures such as floating wind turbines, deep-sea platforms, and subsea pipelines face growing challenges: traditional threats of corrosion, dynamic loads, and fatigue are now compounded by deep-sea high-pressure environments, extreme weather like super typhoons and polar cold surges, and the need to align structural safety with carbon neutrality. Ensuring their integrity is key to preventing disasters, cutting costs, and unlocking blue energy.

Innovations are reshaping the field: digital twin technology enables real-time virtual structural mapping; AI—especially deep and transfer learning—improves early damage detection; and novel sensing tools (high-resistance fiber optics, low-power IoT networks) and green corrosion protection systems refine monitoring-maintenance loops.

This Special Issue seeks high-impact research (original articles, reviews, case studies) to bridge academia and industry. We prioritize practical relevance and technological novelty, with research areas including the following:

  • Deep-sea oil/gas platform and floating wind turbine monitoring, maintenance and safety assessment;
  • Stability and dynamic response analysis under extreme marine conditions;
  • Fatigue and fracture mechanics of marine structures in multi-factor environments;
  • Advanced sensing via fiber optics, low-power IoT and smart material self-sensing;
  • Marine corrosion monitoring and eco-friendly protection systems;
  • Digital twin-driven virtual-real fusion and lifecycle management;
  • AI and big data for early damage identification and residual life prediction;
  • Vibration control and modal identification of flexible marine structures;
  • Sensor deployment optimization and multi-sensor fusion for large-scale structures;
  • Polar marine biofouling assessment and ice-structure interaction analysis;
  • Lifetime extension techniques for aging marine structures;
  • Synergistic monitoring of structural health and energy efficiency for low-carbon marine equipment.

Authors are encouraged to highlight their work’s significance to fundamental knowledge or industrial problem-solving. We look forward to your contributions.

Dr. Ziguang Jia
Dr. Peng Zhang
Prof. Dr. Yi Huang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

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 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

  • artificial intelligence
  • digital twin
  • marine structural monitoring
  • modal identification
  • sensor deployment
  • corrosion protection
  • load and response identification
  • lifecycle management
  • offshore wind turbine
  • deep-sea platform

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Related Special Issue

Published Papers (4 papers)

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Research

19 pages, 2804 KB  
Article
BOTDR Monitoring of Tensile State in Three-Core Fiber-Optic Composite Submarine Cables with a Three-Layer Mechanical Structure and Dual-Threshold Sensing Model
by Manting Guo, Yanpeng Hao, Yashuang Zheng, Busheng Chen, Xin Yu and Licheng Li
J. Mar. Sci. Eng. 2026, 14(4), 390; https://doi.org/10.3390/jmse14040390 - 19 Feb 2026
Viewed by 531
Abstract
Submarine cables are critical components for power transmission in offshore wind farms, making their condition monitoring paramount for ensuring operational reliability. Addressing unclear strain transfer and underdeveloped Brillouin optical time-domain reflectometry (BOTDR) sensing models for three-core fiber-optic composite submarine cables, this study investigated [...] Read more.
Submarine cables are critical components for power transmission in offshore wind farms, making their condition monitoring paramount for ensuring operational reliability. Addressing unclear strain transfer and underdeveloped Brillouin optical time-domain reflectometry (BOTDR) sensing models for three-core fiber-optic composite submarine cables, this study investigated a 66 kV cable and clarified a BOTDR monitoring principle based on the three-layer mechanical structure. Using the external optical unit’s average Brillouin shift for temperature compensation, four characteristic parameters (Δvy, Δvp, vm, vF) were analyzed. The results show the optical unit’s tensile strain-induced Brillouin shift exhibits periodic distribution along the cable. The stable average peak vF achieved a correlation coefficient of 0.98 with tensile load Fi. A dual-threshold sensing model was established: no shift response below F0 = 90 kN (7.84% Rated Tensile Strength (RTS)); strong linear correlation between vF and Fi beyond Fm = 110 kN (9.58% RTS) with a tensile sensitivity coefficient of 0.03788 MHz/kN. This study provides key BOTDR technical support for submarine cable tensile monitoring in complex marine environments. Full article
(This article belongs to the Special Issue Monitoring and Evaluation of Marine Engineering Equipment and Structures—2nd Edition)
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23 pages, 3672 KB  
Article
Structural Model Updating Method of Medium-Deep Water Jacket Platform Based on Sensitivity Clustering
by Zongfeng Zhang, Shuqing Wang, Liang Wang, Jingze Ma, Tongyan Cheng, Zepeng Zheng and Yufeng Jiang
J. Mar. Sci. Eng. 2026, 14(4), 375; https://doi.org/10.3390/jmse14040375 - 15 Feb 2026
Viewed by 486
Abstract
As offshore oil and gas exploration extends into deeper waters and platforms operate over extended service lives, accurate and timely structural health monitoring of deepwater jacket platforms becomes increasingly critical. Rapid construction of a high-fidelity numerical twin model is therefore essential, yet existing [...] Read more.
As offshore oil and gas exploration extends into deeper waters and platforms operate over extended service lives, accurate and timely structural health monitoring of deepwater jacket platforms becomes increasingly critical. Rapid construction of a high-fidelity numerical twin model is therefore essential, yet existing structural model correction methods are largely designed for simple or low-dimensional systems and are inadequate for large-scale marine structures with high-dimensional parameters. To address these challenges, this study proposes a sensitivity-based clustering method for structural model correction of jacket platforms. First, a novel numerical sensitivity index of correction parameters is established, and these parameters are clustered into representative groups. An optimization objective function based on modal characteristics is then formulated, converting the model correction problem into a minimization problem, which is solved using a multi-objective optimization algorithm. To verify the effectiveness and noise robustness of the proposed method, numerical simulations of a jacket platform in the Weizhou Oilfield were performed. Here, maximum errors are reduced to 0.05% and 0.12% under low- and high-noise conditions, respectively, showing significant reductions in relative errors and improved modal confidence. The proposed methodology effectively enables rapid and accurate updating of numerical twin models for deepwater jacket platforms. Full article
(This article belongs to the Special Issue Monitoring and Evaluation of Marine Engineering Equipment and Structures—2nd Edition)
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24 pages, 4564 KB  
Article
Research on Bearing Fault Diagnosis Method of the FPSO Soft Yoke Mooring System Based on Minimum Entropy Deconvolution
by Yanlin Wang, Jiaxi Zhang, Shanshan Sun, Zheliang Fan, Dayong Zhang, Ziguang Jia, Peng Zhang and Yi Huang
J. Mar. Sci. Eng. 2026, 14(2), 235; https://doi.org/10.3390/jmse14020235 - 22 Jan 2026
Viewed by 391
Abstract
The Soft Yoke Mooring (SYM) system is a critical single-point mooring method for Floating Production Storage and Offloading systems (FPSOs) in shallow waters. Its articulated thrust roller bearing operates long-term in harsh marine environments, making it prone to failure and difficult to diagnose. [...] Read more.
The Soft Yoke Mooring (SYM) system is a critical single-point mooring method for Floating Production Storage and Offloading systems (FPSOs) in shallow waters. Its articulated thrust roller bearing operates long-term in harsh marine environments, making it prone to failure and difficult to diagnose. To address the issues of non-stationary signals and fault features submerged in strong noise caused by the bearing’s non-rotational oscillatory motion, this paper proposes an adaptive improved diagnosis scheme based on Minimum Entropy Deconvolution (MED). By optimizing Finite Impulse Response (FIR) filter parameters to adapt to the oscillatory operating conditions and combining joint analysis of time-domain indicators and envelope spectra, precise identification of bearing faults is achieved. Research shows that this method effectively enhances fault impact components. After MED processing, the kurtosis value of the fault signal can be significantly increased from approximately 2.6 to over 8.6. Its effectiveness in noisy environments was verified through simulation. Experiments conducted on a 1:10 scale soft yoke model demonstrated that the MED denoising and filtering signal analysis method can effectively identify damage in the thrust roller bearing of the SYM system under marine conditions characterized by high noise and complex frequencies. This study provides an efficient and reliable method for fault diagnosis of non-rotational oscillatory bearings in complex marine environments, holding significant engineering application value. Full article
(This article belongs to the Special Issue Monitoring and Evaluation of Marine Engineering Equipment and Structures—2nd Edition)
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25 pages, 5215 KB  
Article
Explainable Predictive Maintenance of Marine Engines Using a Hybrid BiLSTM-Attention-Kolmogorov Arnold Network
by Alexandros S. Kalafatelis, Georgios Levis, Anastasios Giannopoulos, Nikolaos Tsoulakos and Panagiotis Trakadas
J. Mar. Sci. Eng. 2026, 14(1), 32; https://doi.org/10.3390/jmse14010032 - 24 Dec 2025
Cited by 1 | Viewed by 1376
Abstract
Predictive maintenance for marine engines requires forecasts that are both accurate and technically interpretable. This work introduces BEACON, a hybrid architecture that combines a bidirectional long short-term memory encoder with attention pooling, a Kolmogorov Arnold network and a lightweight multilayer perceptron for cylinder-level [...] Read more.
Predictive maintenance for marine engines requires forecasts that are both accurate and technically interpretable. This work introduces BEACON, a hybrid architecture that combines a bidirectional long short-term memory encoder with attention pooling, a Kolmogorov Arnold network and a lightweight multilayer perceptron for cylinder-level exhaust gas temperature forecasting, evaluated in both centralized and federated learning settings. On operational data from a bulk carrier, BEACON outperformed strong state-of-the-art baselines, achieving an RMSE of 0.5905, MAE of 0.4713 and R2 of approximately 0.95, while producing interpretable response curves and stable SHAP rankings across engine load regimes. A second contribution is the explicit evaluation of explanation stability in a federated learning setting, where BEACON maintained competitive accuracy and attained mean Spearman correlations above 0.8 between client-specific SHAP rankings, whereas baseline models exhibited substantially lower agreement. These results indicate that the proposed hybrid design provides an accurate and explanation-stable foundation for privacy-aware predictive maintenance of marine engines. Full article
(This article belongs to the Special Issue Monitoring and Evaluation of Marine Engineering Equipment and Structures—2nd Edition)
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