Intelligent Solutions for Marine Operations

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: 25 July 2025 | Viewed by 323

Special Issue Editors


E-Mail Website
Guest Editor
Grupo de Investigación ARIES, Universidad Nebrija, 28015 Madrid, Spain
Interests: marine machinery; smart maintenance; smart transportation; applied artificial intelligence; operations research; smart supply chain; sustainability research
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Faculty of Marine Engineering, Maritime University of Szczecin, 70-500 Szczecin, Poland
Interests: diesel engines; operation and maintenance; reliability and safety; energy conversion; failure prevention; directed innovations; marine systems; machinery; propulsion systems; TRIZ; inventics; modelling; fault prediction; condition monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The maritime industry is currently undergoing a digital transformation that is revolutionising marine operations by introducing intelligent solutions powered by cutting-edge technologies. These technologies are driving the integration of the Internet of Ships and digital engineering, including digital twins, Artificial Intelligence (AI), big data, and cloud computing, into the maritime environment to enhance efficiency and innovation in marine operations.

This Special Issue aims to explore original research on the development and application of intelligent solutions for maritime operations, introducing novel contributions in areas such as predictive maintenance, cyber–physical systems, autonomous and unmanned marine systems, AI-accelerated computational fluid dynamics, and green maritime technologies. Authors are invited to submit original research and development work that advances intelligent solutions for marine operations.

The scope of this Special Issue includes, but is not limited to, topics such as the following:

  • Application of Artificial Intelligence in marine operations. Predictive maintenance of vessels and offshore structures, intelligent weather forecasting, and route optimisation.
  • Internet of Ships. Smart sensors and real-time monitoring, digital twins, cyber–physical systems in marine operations, and cybersecurity.
  • Smart applications in autonomous and unmanned marine systems. AI-powered autonomous surface and underwater vehicles, collision avoidance, and path optimisation algorithms.
  • AI-accelerated computational fluid dynamics. Hybrid AI-CFD models for ship design and real-time performance optimisation.
  • Green maritime technologies. Data-driven models for fuel efficiency optimisation and emissions reduction, and renewable energy integration in marine operations.

Dr. Christian Velasco-Gallego
Prof. Dr. Leszek Chybowski
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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • Artificial Intelligence
  • predictive maintenance
  • Internet of Ships
  • smart sensors
  • digital twins
  • cyber–physical systems
  • computational fluid dynamics
  • green maritime technologies
  • maritime autonomous surface ships
  • fuel efficiency optimisation

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

18 pages, 8566 KiB  
Article
Machine Learning-Based Mooring Failure Detection for FPSOs: A Two-Step ANN Approach
by Omar Jebari, Do-Soo Kwon, Sung-Jae Kim, Chungkuk Jin and Moohyun Kim
J. Mar. Sci. Eng. 2025, 13(4), 791; https://doi.org/10.3390/jmse13040791 - 16 Apr 2025
Viewed by 209
Abstract
This study presents a two-step artificial neural network (ANN) approach for detecting mooring failures in a spread-moored floating production storage and offloading (FPSO) vessel using platform motion data. Synthetic statistical data generated from time-domain simulations were utilized as input features. The first-step ANN [...] Read more.
This study presents a two-step artificial neural network (ANN) approach for detecting mooring failures in a spread-moored floating production storage and offloading (FPSO) vessel using platform motion data. Synthetic statistical data generated from time-domain simulations were utilized as input features. The first-step ANN determines whether the mooring system is intact or a failure has occurred within a specific mooring group. If a failure is detected, the second-step ANN identifies the exact failed mooring line within the group. Hyperparameter optimization was performed using Bayesian and random search methods, and multiple input variable sets were evaluated. The results indicate that the mean values of platform motions, particularly surge and yaw, play a crucial role in accurately identifying mooring failures. Additionally, selecting the top 10 features based on mutual information can be a way to improve detection accuracy. The proposed two-step ANN approach outperformed the single-step ANN method, achieving higher classification accuracy and reducing misclassification between mooring lines. These findings demonstrate the potential of machine learning for near-real-time mooring integrity monitoring, offering a practical and efficient alternative to traditional inspection methods. Full article
(This article belongs to the Special Issue Intelligent Solutions for Marine Operations)
Show Figures

Figure 1

Back to TopTop