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Advanced Condition Monitoring and Intelligent Operation & Maintenance Technologies in...
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Advanced Condition Monitoring and Intelligent Operation & Maintenance Technologies in Ships and Offshore Facilities

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: 20 May 2024 | Viewed by 1871

Special Issue Editors

Prof. Dr. Hongpeng Zhang

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Guest Editor
Marine Engineering College, Dalian Maritime University, Dalian 116026, China
Interests: ship mechatronics; smart sensor technology; ship pollution prevention and control technology; microfluidic chip technology
Special Issues, Collections and Topics in MDPI journals
Dr. Xingming Zhang

E-Mail Website
Guest Editor
School of Ocean Engineering, Harbin Institute of Technology (Weihai), Weihai 264209, China
Interests: smart sensor technology; condition monitoring of marine engines; unmanned underwater vehicle technology
Special Issues, Collections and Topics in MDPI journals
Dr. Lin Zeng

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Guest Editor
Bionic Sensing and Intelligence Center, Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Interests: MEMS sensors; marine engineering; mechanical fault diagnosis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, Intelligent Operations Management on ships and offshore facilities has witnessed rapid developments and innovations, which are of great significance for the Intelligent condition monitoring of ship machinery equipment, the remote monitoring and control of large offshore platforms, and optimization and hazard warning for ship navigation routes. For example, intelligent operations management technologies are employed in condition monitoring, fault diagnosis, life expectancy prediction, exhaust emission control, the remote control of offshore platforms, automatic navigation and collision avoidance, and maritime communication and positioning, etc.

This Special Issue aims to highlight the latest advances in marine intelligent operations management technology, including, but not limited to, original research and reviews on the sensing mechanisms, structural design, system modeling and simulation, advanced manufacturing technologies, detection circuits, signal processing, sensor reliability, sensor interfaces, and calibration methods utilized in the sensors employed for the intelligent management of operations, as well as original algorithmic research related to the intelligent management of operations, including fault diagnosis, life expectancy prediction, health status monitoring, and intelligent decision-making. We look forward to receiving your papers.

Prof. Dr. Hongpeng Zhang
Dr. Xingming Zhang
Dr. Lin Zeng
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

  • smart ships
  • offshore facilities
  • intelligent design and manufacture of marine equipment
  • rotating machinery
  • friction and wear
  • advanced materials
  • intelligent monitoring and operation
  • structural safety and reliability
  • artificial intelligence
  • maritime communications
  • localization and object tracking
  • condition monitoring and fault diagnostic
  • exhaust emission control
  • ballast water discharge
  • collision avoidance
  • remote sensors
  • MEMS and NEMS
  • data collection and processing
  • life expectancy prediction

Related Special Issue

Published Papers (3 papers)

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Research

20 pages, 5944 KiB  
Article
Research on Abrasive Particle Target Detection and Feature Extraction for Marine Lubricating Oil
by Chenzhao Bai, Jiaqi Ding, Hongpeng Zhang, Zhiwei Xu, Hanlin Liu, Wei Li, Guobin Li, Yi Wei and Jizhe Wang
J. Mar. Sci. Eng. 2024, 12(4), 677; https://doi.org/10.3390/jmse12040677 - 19 Apr 2024
Viewed by 379
Abstract
The hydraulic oil of marine equipment contains a large number of abrasive contaminants that reflect the operating condition of the equipment. In order to realize the detection of particulate contaminants, this research first proposes a shape-based classification method for oil abrasive particles, designs [...] Read more.
The hydraulic oil of marine equipment contains a large number of abrasive contaminants that reflect the operating condition of the equipment. In order to realize the detection of particulate contaminants, this research first proposes a shape-based classification method for oil abrasive particles, designs an oil abrasive particle collection system, and constructs a new dataset. After that, the research introduces deep learning target detection technology in computer vision, and uses GhostNet to lighten the network structure, the CBAM (Convolutional Block Attention Module) attention mechanism to improve the generalization ability of the model, and the ASPP module to enhance the model sensory wildness, respectively. A lightweight target detection model, WDD, is created for the identification of abrasive particles. In this study, the WDD model is tested against other network models, and the mAP value of WDD reaches 91.2%, which is 4.8% higher than that of YOLOv5s; in addition, the detection speed of the WDD model reaches 55 FPS. Finally, this study uses real ship lubricating oils for validation, and the WDD model still maintains a high level of accuracy. Therefore, the WDD model effectively balances the accuracy and detection speed of marine oil abrasive particle detection, which is superior to other oil abrasive particle detection techniques. Full article
(This article belongs to the Special Issue Advanced Condition Monitoring and Intelligent Operation & Maintenance Technologies in Ships and Offshore Facilities)
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29 pages, 11760 KiB  
Article
An Improved Identification Method of Pipeline Leak Using Acoustic Emission Signal
by Jialin Cui, Meng Zhang, Xianqiang Qu, Jinzhao Zhang and Lin Chen
J. Mar. Sci. Eng. 2024, 12(4), 625; https://doi.org/10.3390/jmse12040625 - 07 Apr 2024
Viewed by 512
Abstract
Pipelines constitute a vital component in offshore oil and gas operations, subjected to prolonged exposure to a range of alternating loads. Safeguarding their integrity, particularly through meticulous leak detection, is essential for ensuring safe and reliable operation. Acoustic emission detection emerges as an [...] Read more.
Pipelines constitute a vital component in offshore oil and gas operations, subjected to prolonged exposure to a range of alternating loads. Safeguarding their integrity, particularly through meticulous leak detection, is essential for ensuring safe and reliable operation. Acoustic emission detection emerges as an effective approach for monitoring pipeline leaks, demanding subsequent rigorous data analysis. Traditional analysis techniques like wavelet analysis, empirical mode decomposition (EMD), variational mode decomposition (VMD), and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) often yield results with considerable randomness, adversely affecting leak detection accuracy. This study introduces an enhanced damage recognition methodology, integrating improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and probabilistic neural networks (PNN) for more accurate pipeline leak identification. This novel approach combines laboratory-acquired acoustic emission signals from leaks with ambient noise signals. Application of ICEEMDAN to these composite signals isolates eight intrinsic mode functions (IMFs), with subsequent time–frequency analysis providing insight into their frequency structures and feature vectors. These vectors are then employed to train a PNN, culminating in a robust neural network model tailored for leak detection. Conduct experimental research on pipeline leakage identification, focusing on the local structure of offshore platforms, experimental research validates the superiority of the ICEEMDAN–PNN model over existing methods like EMD, VMD, and CEEMDAN paired with PNN, particularly in terms of stability, anti-interference capabilities, and detection precision. Notably, even amidst integrated noise, the ICEEMDAN–PNN model maintains a remarkable 98% accuracy rate in identifying pipeline leaks. Full article
(This article belongs to the Special Issue Advanced Condition Monitoring and Intelligent Operation & Maintenance Technologies in Ships and Offshore Facilities)
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17 pages, 4908 KiB  
Article
High-Resistance Connection Fault Diagnosis in Ship Electric Propulsion System Using Res-CBDNN
by Jia-Ling Xie, Wei-Feng Shi, Ting Xue and Yu-Hang Liu
J. Mar. Sci. Eng. 2024, 12(4), 583; https://doi.org/10.3390/jmse12040583 - 29 Mar 2024
Viewed by 487
Abstract
The fault detection and diagnosis of a ship’s electric propulsion system is of great significance to the reliability and safety of large modern ships. The traditional fault diagnosis method based on mathematical models and expert knowledge is limited by the difficulty of establishing [...] Read more.
The fault detection and diagnosis of a ship’s electric propulsion system is of great significance to the reliability and safety of large modern ships. The traditional fault diagnosis method based on mathematical models and expert knowledge is limited by the difficulty of establishing an accurate model of the complex system, and it is easy to cause false alarms. Data-driven methods, such as deep learning, can automatically learn from the mass of data, extract and analyze fault characteristics, and create a more objective distinction system state. A deep learning fault diagnosis model based on ResNet feature extraction capability and bidirectional long-term memory network timing processing capability is proposed to realize fault diagnosis of high resistance connections in ship electric propulsion systems. The results show that the res-convolutional BiLSTM deep neural network (Res-CBDNN) can fully integrate the advantages of the two networks, efficiently process fault current data, and achieve high-performance fault diagnosis. The accuracy of Res-CBDNN can be kept above 85% in a noisy environment, and it can effectively monitor the high resistance connection fault of ship electric propulsion systems. Full article
(This article belongs to the Special Issue Advanced Condition Monitoring and Intelligent Operation & Maintenance Technologies in Ships and Offshore Facilities)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: A computer simulation model for estimating the probability of ship collisions in a bend of routes situation
Authors: Mirko Čorić
Affiliation: Faculty of Maritime Studies, University of Split, 21000 Split, Croatia

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