Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.6
3.2
Journals
JMSE
Special Issues
Marine Equipment Intelligent Fault Diagnosis
share announcement

Marine Equipment Intelligent Fault Diagnosis

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 (25 June 2026) | Viewed by 5026

Editors

Dr. Luis F. Mendonça

E-Mail Website
Guest Editor
Departamento de Engenharia Marítima, Escola Superior Náutica Infante D. Henrique, 2770-058 Oeiras, Portugal
Interests: intelligent fault diagnosis of maritime equipment
Dr. Francesc Xavier Martínez De Osés

E-Mail Website
Guest Editor
Nautical Science and Engineering Department, Universitat Politècnica de Catalunya, 08003 Barcelona, Spain
Interests: navigation; safety of navigation; meteorology; sustainability; MSP
Special Issues, Collections and Topics in MDPI journals
Dr. Susana Vieira

E-Mail Website
Guest Editor
Departamento de Engenharia Mecânica, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal
Interests: artificial intelligence; soft computing; feature selection; fuzzy modelling; optimization; metaheuristics; computational intelligence; knowledge data discovery
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Marine equipment has significant operational requirements. Consequently, the automatic and intelligent diagnosis of faults in such equipment is crucial for its efficient operation. This allows for more effective and sustainable maintenance decisions. Recent scientific advances in intelligent decision-making have the potential to drive significant technical progress. When applied to marine equipment, these advances can yield economic, environmental, and safety benefits, among others. Authors are invited to submit original research and development papers that promote the intelligent maintenance of marine equipment using automatic diagnosis and intelligent fault decision techniques. This Special Issue welcomes, but is not limited to, the following topics:

  • Automatic and intelligent fault diagnosis;
  • Intelligent maintenance;
  • Fault-tolerant control.

Dr. Luis F. Mendonça
Dr. Francesc Xavier Martínez De Osés
Dr. Susana Vieira
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-anonymized 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
  • fault diagnosis
  • deep learning
  • digital twins
  • equipment monitoring
  • radio navigation
  • AIS
  • meteodata

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.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

18 pages, 2562 KB  
Article
Predictive Modelling of Maritime Radar Data Using Transformer Architecture
by Bjorna Qesaraku and Jan Steckel
J. Mar. Sci. Eng. 2026, 14(16), 1482; https://doi.org/10.3390/jmse14161482 - 11 Aug 2026
Viewed by 232
Abstract
Predicting vessel motion and environmental dynamics is essential for safe operation of autonomous maritime navigation systems. Transformer-based models have achieved strong results in AIS-trajectory forecasting and in anticipating future sonar observations, however, their use in maritime radar frame prediction has received little attention, [...] Read more.
Predicting vessel motion and environmental dynamics is essential for safe operation of autonomous maritime navigation systems. Transformer-based models have achieved strong results in AIS-trajectory forecasting and in anticipating future sonar observations, however, their use in maritime radar frame prediction has received little attention, despite radar being a key sensing modality in challenging weather and visibility conditions. In an effort to address this gap, this paper introduces a transformer architecture for predicting future maritime radar frames from sequences of past X-band observations and vessel ego-motion derived from GNSS, adapting the EchoPT paradigm originally developed for simulated in-air sonar imagery to the real-world MOANA dataset. We detail the model architecture and evaluate its prediction performance under both single-frame and autoregressive settings on held-out test data, and benchmark the model against persistence and rigid geometric warp references. A complementary failure mode analysis links the observed prediction errors to specific architectural and dataset choices, providing concrete directions for further research. Full article
(This article belongs to the Special Issue Marine Equipment Intelligent Fault Diagnosis)
►▼ Show Figures

Figure 1

31 pages, 8749 KB  
Article
A Modified Constrained Groove Pressing Process (MCGP) for Enhanced Strength and Microstructural Refinement of Deoxidized High-Phosphorus (DHP) Copper Sheets: Potential Implications for Marine Component Reliability
by Mohsen Forouzanmehr, Mohammad Reza Dashtbayazi, Kazem Reza Kashyzadeh and Mahmoud Chizari
J. Mar. Sci. Eng. 2026, 14(16), 1455; https://doi.org/10.3390/jmse14161455 - 7 Aug 2026
Viewed by 226
Abstract
Deoxidized high-phosphorus (DHP) copper is widely used in marine heat-exchangers and seawater piping, where long-term structural reliability demands both high strength and a deformation-tolerant microstructure to resist damage initiation. Constrained groove pressing (CGP) is a scalable severe plastic deformation route for metallic sheets; [...] Read more.
Deoxidized high-phosphorus (DHP) copper is widely used in marine heat-exchangers and seawater piping, where long-term structural reliability demands both high strength and a deformation-tolerant microstructure to resist damage initiation. Constrained groove pressing (CGP) is a scalable severe plastic deformation route for metallic sheets; however, the sharp trapezoidal junctions of the conventional die impose parasitic bending strains that produce sinusoidal in-plane hardness variations and anisotropic properties. This study introduces a modified CGP (MCGP) process in which the sharp crest and valley of each 45° tooth are replaced by tangent circular arcs (R1 = 1.6 mm at the crest, R2 = 4.8 mm at the valley), removing geometric discontinuities while exactly preserving the groove angle, pitch, and die envelope for drop-in compatibility with existing equipment. DHP copper sheets processed by conventional CGP and MCGP were systematically compared using optical microscopy, SEM, XRD, microhardness, tensile testing, and finite-element analysis. MCGP delivered exceptional mechanical performance: yield strength of 281.19 MPa, ultimate tensile strength of 451.94 MPa (96.4% above the as-received state and 23.8% above conventional CGP), mean hardness of 131.38 HV, and the finest apparent (instrument-uncalibrated) coherent diffraction-domain size of 22.75 nm. Finite-element modelling revealed a lower peak equivalent plastic strain with a more continuously distributed through-thickness deformation path, despite an unchanged nominal grooving strain (≈0.56). Notably, while the modified die redistributes deformation rather than amplifying the nominal strain, the measured through-thickness hardness inhomogeneity factor increased from 7.14% to 21.97% due to strain concentration in the mid-thickness region, indicating that full homogenisation requires further arc-radius optimisation. Nevertheless, the substantial gains in strength and microstructural refinement demonstrate that MCGP offers a promising processing route for producing DHP copper components with enhanced strength and refined microstructures, which may contribute to improved damage tolerance. However, it is acknowledged that direct tests on seawater corrosion, corrosion fatigue, and thermal cycling were not performed in this study; the implications for marine service life are inferred from the established literature on the benefits of grain refinement for corrosion and fatigue resistance. Future work incorporating marine environmental performance tests is recommended to validate these implications. Full article
(This article belongs to the Special Issue Marine Equipment Intelligent Fault Diagnosis)
►▼ Show Figures

Figure 1

25 pages, 4080 KB  
Article
A Maintenance-Aware Temporal Contrastive Autoencoder for Health Index Learning of Marine Turbochargers Under Real-Ship Operation
by Tianfeng Fang, Zhongfan Li, Xinbo Zhu and Yifan Liu
J. Mar. Sci. Eng. 2026, 14(10), 873; https://doi.org/10.3390/jmse14100873 - 8 May 2026
Viewed by 515
Abstract
Health monitoring of marine turbochargers under real-ship operation is complicated by operating-condition variability, recurrent online cleaning, and limited fault labels. This study presents a maintenance-aware temporal contrastive autoencoder (TCCL-AE) for health index (HI) learning from multivariate real-ship monitoring data. The framework aims to [...] Read more.
Health monitoring of marine turbochargers under real-ship operation is complicated by operating-condition variability, recurrent online cleaning, and limited fault labels. This study presents a maintenance-aware temporal contrastive autoencoder (TCCL-AE) for health index (HI) learning from multivariate real-ship monitoring data. The framework aims to learn an HI that tracks degradation while reducing sensitivity to short-term operating-condition fluctuations by incorporating maintenance information into latent-state evolution and introducing temporal contrastive learning. The model includes a temporal encoder for window-level feature extraction, a latent decomposition module for separating degradation-related and condition-related information, and a Health Coupling Module for representing maintenance-induced recovery. The training objective combines temporal contrastive learning, observation reconstruction, and maintenance consistency. Experiments on multi-voyage real-ship data indicate that the learned HI reflects long-term degradation evolution and maintenance-related recovery, while remaining comparatively smooth under variable operating conditions. The resulting HI provides a continuous representation for condition tracking and maintenance-related interpretation during long-horizon monitoring. Full article
(This article belongs to the Special Issue Marine Equipment Intelligent Fault Diagnosis)
►▼ Show Figures

Figure 1

20 pages, 1043 KB  
Article
Multi-Criteria Decision-Making Algorithm Selection and Adaptation for Performance Improvement of Two Stroke Marine Diesel Engines
by Hla Gharib and György Kovács
J. Mar. Sci. Eng. 2025, 13(10), 1916; https://doi.org/10.3390/jmse13101916 - 5 Oct 2025
Cited by 3 | Viewed by 1883
Abstract
Selecting an appropriate Multi-Criteria Decision-Making (MCDM) algorithm for optimizing marine diesel engine operation presents a complex challenge due to the diversity in mathematical formulations, normalization schemes, and trade-off resolutions across methods. This study systematically evaluates fourteen MCDM algorithms, which are grouped into five [...] Read more.
Selecting an appropriate Multi-Criteria Decision-Making (MCDM) algorithm for optimizing marine diesel engine operation presents a complex challenge due to the diversity in mathematical formulations, normalization schemes, and trade-off resolutions across methods. This study systematically evaluates fourteen MCDM algorithms, which are grouped into five primary methodological categories: Scoring-Based, Distance-Based, Pairwise Comparison, Outranking, and Hybrid/Intelligent System-Based methods. The goal is to identify the most suitable algorithm for real-time performance optimization of two stroke marine diesel engines. Using Diesel-RK software, calibrated for marine diesel applications, simulations were performed on a variant of the MAN-B&W-S60-MC-C8-8 engine. A refined five-dimensional parameter space was constructed by systematically varying five key control variables: Start of Injection (SOI), Dwell Time, Fuel Mass Fraction, Fuel Rail Pressure, and Exhaust Valve Timing. A subset of 4454 high-potential alternatives was systematically evaluated according to three equally important criteria: Specific Fuel Consumption (SFC), Nitrogen Oxides (NOx), and Particulate Matter (PM). The MCDM algorithms were evaluated based on ranking consistency and stability. Among them, Proximity Indexed Value (PIV), Integrated Simple Weighted Sum Product (WISP), and TriMetric Fusion (TMF) emerged as the most stable and consistently aligned with the overall consensus. These methods reliably identified optimal engine control strategies with minimal sensitivity to normalization, making them the most suitable candidates for integration into automated marine engine decision-support systems. The results underscore the importance of algorithm selection and provide a rigorous basis for establishing MCDM in emission-constrained maritime environments. This study is the first comprehensive, simulation-based evaluation of fourteen MCDM algorithms applied specifically to the optimization of two stroke marine diesel engines using Diesel-RK software. Full article
(This article belongs to the Special Issue Marine Equipment Intelligent Fault Diagnosis)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop