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Applications of Artificial Intelligence in Marine Machinery
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Applications of Artificial Intelligence in Marine Machinery

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: closed (10 October 2024) | Viewed by 6973

Special Issue Editors

Dr. Christian Velasco-Gallego

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
Dr. Iraklis Lazakis

E-Mail Website
Guest Editor
Department of Naval Architecture, Ocean & Marine Engineering, University of Strathclyde, Glasgow G4 0LZ, UK
Interests: ship operations; systems maintenance and reliability; ship systems condition monitoring; risk analysis tools and methodologies; system criticality assessment; shipyard manufacturing and productivity; asset management; wind–wave–tidal energy devices installation; operation and maintenance
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The maritime sector has often been considered a sector conservative sector in terms of embracing change. Nonetheless, in recent years, there has been increased attention paid to the utilization of artificial intelligence due to its undeniable benefits and the opportunities associated with its application. The development and implementation of machine learning and, more specifically, deep learning, has experienced substantial achievements in marine machinery, with significant milestones attained in the areas of knowledge of smart maintenance and performance optimization, for instance.

Cutting-edge Research

  • Smart Maintenance. Recent studies have demonstrated the capability of employing artificial intelligence to determine the current and future health of marine machinery, and thus guaranteeing the potential application of prescriptive maintenance.
  • Fuel Efficiency. Researchers have employed artificial intelligence in an attempt to both optimize fuel usage and combustion processes so that the fuel costs and the environmental impact can be minimized.
  • Performance Optimisation. State-of-the-art methodologies have been introduced to facilitate the application of artificial intelligence to ensure the optimal performance of marine machinery.

In recent years, there has been an increase in the application of artificial intelligence (AI) within the maritime industry. Artificial intelligence has enabled machinery systems and equipment to perform complex tasks, leading to an optimization of the processes and an enhancement in terms of decision-making capabilities. These advances in technological developments and further applied research have facilitated the maritime industry to advance towards a more optimal performance, monitoring, and management of marine machinery.

This Special Issue aims to explore original research regarding the application of artificial intelligence in marine systems and equipment in order to address real-world problems. As such, authors are invited to submit original research and development work that facilitates the advancement of applied artificial intelligence in the maritime industry and, in particular, marine machinery and equipment. The scope of this Special Issue includes, but is not limited to, topics such as:

  • smart maintenance;
  • intelligent condition monitoring;
  • performance optimization;
  • fuel consumption optimization;
  • emission control;
  • reliability engineering.

Dr. Christian Velasco-Gallego
Dr. Iraklis Lazakis
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

  • marine machinery
  • artificial intelligence
  • smart maintenance
  • intelligent condition monitoring
  • performance optimization
  • fuel consumption optimization
  • emission control
  • reliability engineering
  • machine learning
  • deep learning

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

Published Papers (4 papers)

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Research

12 pages, 1474 KiB  
Article
A Data-Driven Model for Rapid CII Prediction
by Markus Mühmer, Alessandro La Ferlita, Evangelos Geber, Sören Ehlers, Emanuel Di Nardo, Ould El Moctar and Angelo Ciaramella
J. Mar. Sci. Eng. 2024, 12(11), 2048; https://doi.org/10.3390/jmse12112048 - 12 Nov 2024
Cited by 1 | Viewed by 911
Abstract
The shipping industry plays a crucial role in global trade, but it also contributes significantly to environmental pollution, particularly in regard to carbon emissions. The Carbon Intensity Indicator (CII) was introduced with the objective of reducing emissions in the shipping sector. The lack [...] Read more.
The shipping industry plays a crucial role in global trade, but it also contributes significantly to environmental pollution, particularly in regard to carbon emissions. The Carbon Intensity Indicator (CII) was introduced with the objective of reducing emissions in the shipping sector. The lack of familiarity with the carbon performance is a common issue among vessel operator. To address this aspect, the development of methods that can accurately predict the CII for ships is of paramount importance. This paper presents a novel and simplified approach to predicting the CII for ships, which makes use of data-driven modelling techniques. The proposed method considers a restricted set of parameters, including operational data (draft and speed) and environmental conditions, such as wind speed and direction, to provide an accurate prediction of the CII factor. This approach extends the state of research by applying Deep Neural Networks (DNNs) to provide an accurate CII prediction with a deviation of less than 6% over a considered time frame consisting of different operating states (cruising and maneuvering mode). The result is achieved by using a limited amount of training data, which enables ship owners to obtain a rapid estimation of their yearly rating prior to receiving the annual CII evaluation. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Marine Machinery)
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21 pages, 6139 KiB  
Article
Development of a Hierarchical Clustering Method for Anomaly Identification and Labelling of Marine Machinery Data
by Christian Velasco-Gallego, Iraklis Lazakis and Nieves Cubo-Mateo
J. Mar. Sci. Eng. 2024, 12(10), 1792; https://doi.org/10.3390/jmse12101792 - 9 Oct 2024
Viewed by 1181
Abstract
The application of artificial intelligence models for the fault diagnosis of marine machinery increased expeditiously within the shipping industry. This relates to the effectiveness of artificial intelligence in capturing fault patterns in marine systems that are becoming more complex and where the application [...] Read more.
The application of artificial intelligence models for the fault diagnosis of marine machinery increased expeditiously within the shipping industry. This relates to the effectiveness of artificial intelligence in capturing fault patterns in marine systems that are becoming more complex and where the application of traditional methods is becoming unfeasible. However, despite these advances, the lack of fault labelling data is still a major concern due to confidentiality issues, and lack of appropriate data, for instance. In this study, a method based on histogram similarity and hierarchical clustering is proposed as an attempt to label the distinct anomalies and faults that occur in the dataset so that supervised learning can then be implemented. To validate the proposed methodology, a case study on a main engine of a tanker vessel is considered. The results indicate that the method can be a preliminary option to classify and label distinct types of faults and anomalies that may appear in the dataset, as the model achieved an accuracy of approximately 95% for the case study presented. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Marine Machinery)
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14 pages, 558 KiB  
Article
Fleet Repositioning, Flag Switching, Transportation Scheduling, and Speed Optimization for Tanker Shipping Firms
by Yiwei Wu, Jieming Chen, Yao Lu and Shuaian Wang
J. Mar. Sci. Eng. 2024, 12(7), 1072; https://doi.org/10.3390/jmse12071072 - 26 Jun 2024
Viewed by 1499
Abstract
In response to the European Union (EU)’s sanctions on Russian oil products, tanker shipping firms may adopt two strategies to reoptimize their shipping networks. The first strategy is to switch the flag states of tankers that are not eligible to operate on certain [...] Read more.
In response to the European Union (EU)’s sanctions on Russian oil products, tanker shipping firms may adopt two strategies to reoptimize their shipping networks. The first strategy is to switch the flag states of tankers that are not eligible to operate on certain routes. The second strategy is to reposition tankers based on their flag states, i.e., moving those tankers that are eligible from other groups to specified routes. To help tanker shipping firms minimize the total operating cost during the planning horizon in the context of EU oil sanctions, including costs of fleet repositioning, flag switching, and fuel, this study investigates an integrated problem of fleet repositioning, flag switching, transportation scheduling, and speed optimization considering the dynamic relationships among fuel consumption, speed, and load. By formulating the problem as a nonlinear integer programming model and applying various linearization techniques to convert the nonlinear model into a linear optimization model solvable by off-the-shelf linear optimization solvers, this study demonstrates the practical application potential of the proposed model, with the longest solution time of less than two hours for a numerical instance with seven routes. Furthermore, through sensitivity analyses on important factors including unit fuel prices, crude oil transportation demand, and the tanker repositioning cost, this study provides managerial insights into the operations management of tanker shipping firms. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Marine Machinery)
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27 pages, 8479 KiB  
Article
Developing an Artificial Intelligence-Based Method for Predicting the Trajectory of Surface Drifting Buoys Using a Hybrid Multi-Layer Neural Network Model
by Miaomiao Song, Wei Hu, Shixuan Liu, Shizhe Chen, Xiao Fu, Jiming Zhang, Wenqing Li and Yuzhe Xu
J. Mar. Sci. Eng. 2024, 12(6), 958; https://doi.org/10.3390/jmse12060958 - 7 Jun 2024
Cited by 2 | Viewed by 1602
Abstract
Accurately predicting the long-term trajectory of a surface drifting buoy (SDB) is challenging. This paper proposes a promising solution to the SDB trajectory prediction based on artificial intelligence (AI) technologies. Initially, a scalable mathematical model for trajectory prediction is developed, transforming the challenge [...] Read more.
Accurately predicting the long-term trajectory of a surface drifting buoy (SDB) is challenging. This paper proposes a promising solution to the SDB trajectory prediction based on artificial intelligence (AI) technologies. Initially, a scalable mathematical model for trajectory prediction is developed, transforming the challenge of predicting trajectory points into predicting velocities in eastward and northward directions. Subsequently, a four-layer trajectory prediction calculation framework (FLTPCF) is established, outlining a complete workflow for the real-time online training of marine environment data and SDBs’ trajectory prediction. Thirdly, for facilitating accurate long-term trajectory prediction, a hybrid artificial neural network trajectory prediction model, named CNN–BiGRU–Attention, integrates a Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Unit (BiGRU), and Attention mechanism (AM), tuned for spatiotemporal feature extraction and extended time-series reasoning. Extensive experiments, including ablation studies, comparative analyses with state-of-the-art models like BiLSTM and Transformer, evaluations against numerical methods, and adaptability tests, were conducted for justifying the CNN–BiGRU–Attention model. The results highlight the CNN–BiGRU–Attention model’s excellent convergence, accuracy, and generalization capabilities in predicting 24, 48, and 72 h trajectories for SDBs with varying drogue statuses and under different sea conditions. This work has great potential to promote the intelligent degree of marine environmental monitoring. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Marine Machinery)
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