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Recent Advances on Intelligent Maintenance and Health Management in Ocean...
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Recent Advances on Intelligent Maintenance and Health Management in Ocean Engineering

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 December 2024 | Viewed by 4510

Special Issue Editor

Dr. Zengkai Liu

E-Mail Website
Guest Editor
Qingdao Innovation and Development Base, Harbin Engineering University, Qingdao 266000, China
Interests: reliability; safety; risk assessment; ocean and coastal engineering; machine learning; oil spill
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid advancement of sensing technology, information technology, and decision theory, the engineering systems within the domain of ocean engineering are progressively becoming increasingly intricate. The levels of integration and intelligence of these systems continue to advance, resulting in escalating costs in terms of design, production, and particularly maintenance and assurance. Concurrently, owing to the surge in the number of components and influencing factors, the likelihood of faults and functional failures is gradually also rising. Consequently, the focus of researchers has shifted towards the intelligent maintenance and health management of ocean engineering equipment.

Grounded in considerations of the reliability, safety, and economy of complex systems, there is a growing emphasis on the application of fault prediction and health management strategies. Prognostics and health management (PHM), as an emerging interdisciplinary field and comprehensive set of technologies, is spearheading a transformative phase in the maintenance of and support systems for such equipment. Developing the intelligent maintenance of equipment can ensure the secure, stable, and reliable operation of ocean engineering equipment, enhancing personnel safety and manufacturing sector production efficiency and yielding tangible economic and social benefits.

In recent years, with the swift evolution of artificial intelligence technology and its remarkable advantages in feature extraction, knowledge learning, and intelligence, new avenues have been paved for intelligent operation, maintenance, and health management. The integration of artificial intelligence algorithms into research on intelligent maintenance and health management has achieved remarkable strides.

The Special Issue will provide a platform for researchers and practitioners to exchange their ideas and findings, discuss the latest developments, and explore future directions in the field of intelligent maintenance and health management in ocean engineering. Topics of interest include, but are not limited to:

  • Fault diagnosis;
  • Online monitoring technology;
  • Fault prognosis and health management (PHM);
  • Remaining useful life prediction;
  • Maintenance optimization;
  • Predictive maintenance schemes;
  • Condition-based maintenance schemes;
  • Uncertainty quantification and management in PHM;
  • Reliability assessment;
  • Reliability prediction;
  • Risk analysis;
  • Signals processing approaches;
  • Condition state recognition.

Dr. Zengkai Liu
Guest Editor

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

  • reliability of marine structures
  • intelligent operation and maintenance
  • risk assessment

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Published Papers (3 papers)

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Research

25 pages, 48180 KiB  
Article
Time-Series Explanatory Fault Prediction Framework for Marine Main Engine Using Explainable Artificial Intelligence
by Hong Je-Gal, Young-Seo Park, Seong-Ho Park, Ji-Uk Kim, Jung-Hee Yang, Sewon Kim and Hyun-Suk Lee
J. Mar. Sci. Eng. 2024, 12(8), 1296; https://doi.org/10.3390/jmse12081296 - 31 Jul 2024
Viewed by 968
Abstract
As engine monitoring data has become more complex with an increasing number of sensors, fault prediction based on artificial intelligence (AI) has emerged. Existing fault prediction models using AI significantly improve the accuracy of predictions by effectively handling such complex data, but at [...] Read more.
As engine monitoring data has become more complex with an increasing number of sensors, fault prediction based on artificial intelligence (AI) has emerged. Existing fault prediction models using AI significantly improve the accuracy of predictions by effectively handling such complex data, but at the same time, the problem arises that the AI-based models cannot explain the rationale of their predictions to users. To address this issue, we propose a time-series explanatory fault prediction framework to provide an explainability even when using AI-based fault prediction models. It consists of a data feature reduction process, a fault prediction model training process using long short-term memory, and an interpretation process of the fault prediction model via an explainable AI method. In particular, the proposed framework can explain a fault prediction based on time-series data. Therefore, it indicates which part of the data was significant for the fault prediction not only in terms of sensor type but also in terms of time. Through extensive experiments, we evaluate the proposed framework using various fault data by comparing the prediction performance of fault prediction and by assessing how well the main pre-symptoms of the fault are extracted when predicting a fault. Full article
(This article belongs to the Special Issue Recent Advances on Intelligent Maintenance and Health Management in Ocean Engineering)
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24 pages, 31670 KiB  
Article
Fusion of Multi-Layer Attention Mechanisms and CNN-LSTM for Fault Prediction in Marine Diesel Engines
by Jiawen Sun, Hongxiang Ren, Yating Duan, Xiao Yang, Delong Wang and Haina Tang
J. Mar. Sci. Eng. 2024, 12(6), 990; https://doi.org/10.3390/jmse12060990 - 13 Jun 2024
Cited by 2 | Viewed by 1039
Abstract
Timely and effective maintenance is imperative to minimize operational disruptions and ensure the reliability of marine vessels. However, given the low early warning rates and poor adaptability under complex conditions of previous data-driven fault prediction methods, this paper presents a hybrid deep learning [...] Read more.
Timely and effective maintenance is imperative to minimize operational disruptions and ensure the reliability of marine vessels. However, given the low early warning rates and poor adaptability under complex conditions of previous data-driven fault prediction methods, this paper presents a hybrid deep learning model based on multi-layer attention mechanisms for predicting faults in a marine diesel engine. Specifically, this hybrid model first introduces a Convolutional Neural Network (CNN) and self-attention to extract local features from multi-feature input sequences. Then, we utilize Long Short-Term Memory (LSTM) and multi-head attention to capture global correlations across time steps. Finally, the hybrid deep learning model is integrated with the Exponential Weighted Moving Average (EWMA) to monitor the operational status and predict potential faults in the marine diesel engine. We conducted extensive evaluations using real datasets under three operating conditions. The experimental results indicate that the proposed method outperforms the current state-of-the-art methods. Moreover, ablation studies and visualizations highlight the importance of fusing multi-layer attention, and the results under various operating conditions and application scenarios demonstrate that this method possesses predictive accuracy and broad applicability. Hence, this approach can provide decision support for condition monitoring and predictive maintenance of marine mechanical systems. Full article
(This article belongs to the Special Issue Recent Advances on Intelligent Maintenance and Health Management in Ocean Engineering)
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25 pages, 4906 KiB  
Article
Machine Learning-Based Anomaly Detection on Seawater Temperature Data with Oversampling
by Hangoo Kang, Dongil Kim and Sungsu Lim
J. Mar. Sci. Eng. 2024, 12(5), 807; https://doi.org/10.3390/jmse12050807 - 12 May 2024
Viewed by 1577
Abstract
This study deals with a method for anomaly detection in seawater temperature data using machine learning methods with oversampling techniques. Data were acquired from 2017 to 2023 using a Conductivity–Temperature–Depth (CTD) system in the Pacific Ocean, Indian Ocean, and Sea of Korea. The [...] Read more.
This study deals with a method for anomaly detection in seawater temperature data using machine learning methods with oversampling techniques. Data were acquired from 2017 to 2023 using a Conductivity–Temperature–Depth (CTD) system in the Pacific Ocean, Indian Ocean, and Sea of Korea. The seawater temperature data consist of 1414 profiles including 1218 normal and 196 abnormal profiles. This dataset has an imbalance problem in which the amount of abnormal data is insufficient compared to that of normal data. Therefore, we generated abnormal data with oversampling techniques using duplication, uniform random variable, Synthetic Minority Oversampling Technique (SMOTE), and autoencoder (AE) techniques for the balance of data class, and trained Interquartile Range (IQR)-based, one-class support vector machine (OCSVM), and Multi-Layer Perceptron (MLP) models with a balanced dataset for anomaly detection. In the experimental results, the F1 score of the MLP showed the best performance at 0.882 in the combination of learning data, consisting of 30% of the minor data generated by SMOTE. This result is a 71.4%-point improvement over the F1 score of the IQR-based model, which is the baseline of this study, and is 1.3%-point better than the best-performing model among the models without oversampling data. Full article
(This article belongs to the Special Issue Recent Advances on Intelligent Maintenance and Health Management in Ocean Engineering)
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