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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: 25 September 2024 | Viewed by 7780

Special Issue Editors

Prof. Dr. Hongpeng Zhang

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Guest Editor
Marine Engineering College, Dalian Maritime University, Dalian 116026, China
Interests: marine engineering; mechatronics; smart sensors; microfluidics; marine pollution prevention
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

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Related Special Issue

Published Papers (9 papers)

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Research

29 pages, 8707 KiB  
Article
Opportunity-Maintenance-Based Scheduling Optimization for Ship-Loading Operation Systems in Coal Export Terminals
by Qi Tian, Yun Peng, Xinglu Xu and Wenyuan Wang
J. Mar. Sci. Eng. 2024, 12(8), 1377; https://doi.org/10.3390/jmse12081377 - 12 Aug 2024
Viewed by 237
Abstract
As important nodes of the global coal supply chain, coal export terminals bear the tasks of coal storage, processing, and handling, whose efficiency and stability are of great importance with the growing coal shipping market in recent years. However, poor working conditions of [...] Read more.
As important nodes of the global coal supply chain, coal export terminals bear the tasks of coal storage, processing, and handling, whose efficiency and stability are of great importance with the growing coal shipping market in recent years. However, poor working conditions of the handling equipment in the coal export terminal, together with its relatively fixed layout and poor flexibility, allow frequent equipment failures to seriously affect the ship-loading operations. To solve the problem, this paper constructs a scheduling optimization model for ship-loading operation systems considering equipment maintenance and proposes an opportunity-maintenance-based two-layer algorithm to solve the model. The upper layer aims to optimize the scheduling scheme of the ship-loading operation system under a certain maintenance plan. The lower layer of the algorithm, an opportunity-maintenance-based “equipment-level–flow-level” maintenance optimization method, determines the best equipment maintenance plan. A coal export terminal in China is employed as the case study to verify the effectiveness of the proposed method. The results show that the proposed method can reduce the average dwell time of ships at the terminal by 15.8% and save total scheduling and maintenance costs by 10.3%. This paper shows how to make full use of equipment failure historical data and integrate equipment maintenance schemes into the scheduling problem of the ship-loading operation system, which can effectively reduce the impact of equipment failures on ship-loading operations and provide decision support for coal export terminal management. 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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31 pages, 6880 KiB  
Article
Multi-Dimensional Global Temporal Predictive Model for Multi-State Prediction of Marine Diesel Engines
by Liyong Ma, Siqi Chen, Shuli Jia, Yong Zhang and Hai Du
J. Mar. Sci. Eng. 2024, 12(8), 1370; https://doi.org/10.3390/jmse12081370 - 11 Aug 2024
Viewed by 382
Abstract
The reliability and stability of marine diesel engines are pivotal to the safety and economy of maritime operations. Accurate and efficient prediction of the states of these engines is essential for performance evaluation and operational continuity. This paper introduces a novel hybrid deep [...] Read more.
The reliability and stability of marine diesel engines are pivotal to the safety and economy of maritime operations. Accurate and efficient prediction of the states of these engines is essential for performance evaluation and operational continuity. This paper introduces a novel hybrid deep learning model, the multi-dimensional global temporal predictive (MDGTP) model, designed for synchronous multi-state prediction of marine diesel engines. The model incorporates parallel multi-head attention mechanisms, an enhanced long short-term memory (LSTM) with interleaved residual connections, and gated recurrent units (GRUs). Additionally, we propose a dynamic arithmetic tuna optimization algorithm, which synergizes tuna swarm optimization (TSO), and the arithmetic optimization algorithm (AOA) for hyperparameter optimization, thereby enhancing prediction accuracy. Comparative experiments using actual marine diesel engine data demonstrate that our model outperforms the LSTM, GRU, LSTM–GRU, support vector regression (SVR), random forest (RF), Gaussian process regression (GPR), and back propagation (BP) models, achieving the lowest root mean squared error (RMSE) and mean absolute error (MAE), as well as the highest Pearson correlation coefficient across three sampling periods. Ablation studies confirm the significance of each component in improving prediction accuracy. Our findings validate the efficacy of the proposed MDGTP model for predicting the multi-dimensional operating states of marine diesel engines. 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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24 pages, 14880 KiB  
Article
A New Cross-Domain Motor Fault Diagnosis Method Based on Bimodal Inputs
by Qianming Shang, Tianyao Jin and Mingsheng Chen
J. Mar. Sci. Eng. 2024, 12(8), 1304; https://doi.org/10.3390/jmse12081304 - 1 Aug 2024
Viewed by 359
Abstract
Electric motors are indispensable electrical equipment in ships, with a wide range of applications. They can serve as auxiliary devices for propulsion, such as air compressors, anchor winches, and pumps, and are also used in propulsion systems; ensuring the safe and reliable operation [...] Read more.
Electric motors are indispensable electrical equipment in ships, with a wide range of applications. They can serve as auxiliary devices for propulsion, such as air compressors, anchor winches, and pumps, and are also used in propulsion systems; ensuring the safe and reliable operation of motors is crucial for ships. Existing deep learning methods typically target motors under a specific operating state and are susceptible to noise during feature extraction. To address these issues, this paper proposes a Resformer model based on bimodal input. First, vibration signals are transformed into time–frequency diagrams using continuous wavelet transform (CWT), and three-phase current signals are converted into Park vector modulus (PVM) signals through Park transformation. The time–frequency diagrams and PVM signals are then aligned in the time sequence to be used as bimodal input samples. The analysis of time–frequency images and PVM signals indicates that the same fault condition under different loads but at the same speed exhibits certain similarities. Therefore, data from the same fault condition under different loads but at the same speed are combined for cross-domain motor fault diagnosis. The proposed Resformer model combines the powerful spatial feature extraction capabilities of the Swin-t model with the excellent fine feature extraction and efficient training performance of the ResNet model. Experimental results show that the Resformer model can effectively diagnose cross-domain motor faults and maintains performance even under different noise conditions. Compared with single-modal models (VGG-11, ResNet, ResNeXt, and Swin-t), dual-modal models (MLP-Transformer and LSTM-Transformer), and other large models (Swin-s, Swin-b, and VGG-19), the Resformer model exhibits superior overall performance. This validates the method’s effectiveness and accuracy in the intelligent recognition of common cross-domain motor faults. 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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26 pages, 7620 KiB  
Article
A Wind Power Combination Forecasting Method Based on GASF Image Representation and UniFormer
by Wei Guo, Li Xu, Danyang Zhao, Dianqiang Zhou, Tian Wang and Xujing Tang
J. Mar. Sci. Eng. 2024, 12(7), 1173; https://doi.org/10.3390/jmse12071173 - 13 Jul 2024
Viewed by 370
Abstract
In the field of wind power prediction, traditional methods typically rely on one-dimensional time-series data for feature extraction and prediction. In this study, we propose an innovative short-term wind power forecasting approach using a “visual” 2D image prediction method that effectively utilizes spatial [...] Read more.
In the field of wind power prediction, traditional methods typically rely on one-dimensional time-series data for feature extraction and prediction. In this study, we propose an innovative short-term wind power forecasting approach using a “visual” 2D image prediction method that effectively utilizes spatial pattern information in time-series data by combining wind power series and related environmental features into a 2D GASF image. Firstly, the wind power data are decomposed using the ICEEMDAN algorithm optimized by the BWO (Beluga Whale Optimization) algorithm, extracting the submodal IMF (Intrinsic Mode Function) components with different frequencies. Then, modal reconstruction is performed on the basis of the permutation entropy value of the IMF components, selecting meteorological features highly correlated with reconstructed components through Spearman correlation analysis for data splicing and superposition before converting them into GASF images. Finally, the GASF images are input into the UniFormer model for wind power sequence prediction. By leveraging wind power data predictions from a coastal wind farm in East China and Sotavento in Spain, this study demonstrates the significant benefits and potential applications of this methodology for precise wind power forecasting. This research combines the advantages of image feature extraction and time-series prediction to offer novel perspectives and tools for predicting renewable energy sources such as wind power. 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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15 pages, 1626 KiB  
Article
Research on Time-Cooperative Guidance with Evasive Maneuver for Multiple Underwater Intelligent Vehicles
by Zuoe Fan, Hao Ding, Linping Feng, Bochen Li and Lei Song
J. Mar. Sci. Eng. 2024, 12(6), 1018; https://doi.org/10.3390/jmse12061018 - 19 Jun 2024
Viewed by 404
Abstract
In order to achieve the precise attack of multiple underwater intelligent vehicles (UIVs) on the same target ship at a fixed impact time, and to improve the penetration capability of the UIVs themselves, this study investigated the guidance law for the time-cooperative guidance [...] Read more.
In order to achieve the precise attack of multiple underwater intelligent vehicles (UIVs) on the same target ship at a fixed impact time, and to improve the penetration capability of the UIVs themselves, this study investigated the guidance law for the time-cooperative guidance of UIVs with maneuvering evasion. The evasive maneuver of the UIV increases the line-of-sight angle between the UIV and the target, which decreases the guidance precision of the UIV. A segmented control strategy is proposed to solve the problem of decreasing guidance precision caused by evading maneuvers, which is also the main contribution of this paper. This control strategy is dividing the guidance trajectory into two segments. The first segment allows for intelligent underwater vehicles to make evasion maneuvers and penetrate the defense, while the second segment controls the terminal time and achieves precision strike. Different desired target-vehicle distances are designed for each segment, unifying the impact time control issue and evasion maneuver problem into the pursuit of desired target-vehicle distances. Finally, based on feedback linearization control theory, a time-cooperative guidance law with evasion maneuver capability is proposed. Simulation results validate the effectiveness of the proposed method in attacking-moving targets. 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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16 pages, 7463 KiB  
Article
The Design and Study of a Four-Coil Oil Multi-Pollutant Detection Sensor
by Shuyao Zhang, Zuo Zhang, Baojun Wang, Shukui Hu, Chenzhao Bai, Hongpeng Zhang, Zilei Yu, Huancheng Wang, Liang Qu and Debao Yang
J. Mar. Sci. Eng. 2024, 12(5), 846; https://doi.org/10.3390/jmse12050846 - 20 May 2024
Viewed by 691
Abstract
The operating environment of large mechanical equipment on ships is extremely harsh. Under such harsh conditions, it is necessary to effectively monitor and assess the health status of machinery and equipment and to take appropriate maintenance measures to ensure the normal operation of [...] Read more.
The operating environment of large mechanical equipment on ships is extremely harsh. Under such harsh conditions, it is necessary to effectively monitor and assess the health status of machinery and equipment and to take appropriate maintenance measures to ensure the normal operation of the ship and the safety of the lives and property of the crew. However, currently used methods are less effective in detecting non-ferromagnetic abrasive particles and non-metallic contaminants and may not be able to respond to certain emergencies promptly. Therefore, in this paper, a quad-solenoid coil multi-contaminant oil detection sensor is proposed to detect metallic abrasive particles and non-metallic contaminants using the voltage–capacitance dual mode. We provide an analytical expression for the magnetic field strength of the present sensor and develop a corresponding mathematical model. In order to verify its accuracy, we compared the model results with finite element analysis and verified them experimentally. Analysis of the experimental results shows that by switching the detection mode of the sensor, ferromagnetic metal particles, non-ferromagnetic metal particles, and non-metallic contaminants in the oil can be identified according to the different experimental signal curves. The sensor recognizes ferromagnetic particles over 70 μm in diameter, non-ferromagnetic particles over 220 μm in diameter, water droplets over 100–110 μm in diameter, and air bubbles over 180–190 μm in diameter. By comparing the sensor with existing sensors, the sensor can provide accurate information about various pollutants, help maintenance personnel to develop a reasonable maintenance program, and reduce the maintenance cost of ship machinery. 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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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 784
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 - 7 Apr 2024
Viewed by 905
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
Cited by 1 | Viewed by 779
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.

Title1: A computer simulation model for estimating the probability of ship collisions in a bend of routes situation


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