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: 25 June 2026 | Viewed by 3840

Special Issue Editors


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Guest Editor
Departamento de Engenharia Marítima, Escola Superior Náutica Infante D. Henrique, 2770-058 Oeiras, Portugal
Interests: intelligent fault diagnosis of maritime equipment

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Guest Editor
Departament de Ciència i Enginyeria Nàutiques, Universitat Politecnica de Catalunya, 08034 Barcelona, Spain
Interests: maritime transport; liner shipping; meteorology; short sea shipping

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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
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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-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
  • fault diagnosis
  • deep learning
  • digital twins
  • equipment monitoring
  • radio navigation
  • AIS
  • meteodata

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Published Papers (2 papers)

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Research

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 (registering DOI) - 8 May 2026
Viewed by 288
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)
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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 2 | Viewed by 1669
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)
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