Research

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18 pages, 12152 KiB

Open AccessArticle

An Adversarial Single-Domain Generalization Network for Fault Diagnosis of Wind Turbine Gearboxes

by Xinran Wang, Chenyong Wang, Hanlin Liu, Cunyou Zhang, Zhenqiang Fu, Lin Ding, Chenzhao Bai, Hongpeng Zhang and Yi Wei

J. Mar. Sci. Eng. 2023, 11(12), 2384; https://doi.org/10.3390/jmse11122384 - 18 Dec 2023

Cited by 2 | Viewed by 898

Abstract

In deep learning-based fault diagnosis of the wind turbine gearbox, a commonly faced challenge is the domain shift caused by differing operational conditions. Traditional domain adaptation methods aim to learn transferable features from the source domain and apply them to the target data. [...] Read more.

In deep learning-based fault diagnosis of the wind turbine gearbox, a commonly faced challenge is the domain shift caused by differing operational conditions. Traditional domain adaptation methods aim to learn transferable features from the source domain and apply them to the target data. However, such methods still require access to target domain data during the training process, which limits their applicability in real-time fault diagnosis. To address this issue, we introduce an adversarial single-domain generalization network (ASDGN). It relies solely on data from a single length of data acquisition in wind turbine fault diagnosis. This novel approach introduces a more flexible and efficient solution to the field of real-time fault diagnosis for wind turbines. Full article

(This article belongs to the Special Issue Advances in Sensor Technology in Smart Ships and Offshore Facilities)

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17 pages, 19846 KiB

Open AccessArticle

Evaluation of a Multi-Hop Wireless Internet-of-Things Network on Large Ships

by Jabeom Gu, Miryong Park, Seungsik Lee, Hoyong Kang and Bugi Kim

J. Mar. Sci. Eng. 2023, 11(12), 2243; https://doi.org/10.3390/jmse11122243 - 27 Nov 2023

Cited by 1 | Viewed by 843

Abstract

IoT networks on large ships are known to face challenges, such as severe signal attenuation due to the complex steel bulkhead structure inside the ship. However, reliable connectivity is still required to monitor critical facilities such as engine rooms. This study presents an [...] Read more.

IoT networks on large ships are known to face challenges, such as severe signal attenuation due to the complex steel bulkhead structure inside the ship. However, reliable connectivity is still required to monitor critical facilities such as engine rooms. This study presents an evaluation study of an IoT network using the IEEE 802.15.4 DSME MAC protocol for reliable data collection within a ship. We investigate the impact of ship-specific characteristics on signal propagation and analyze the feasibility of utilizing the DSME MAC protocol. We also compare the viability of 2.4 GHz and sub-1 GHz communication within a ship. In addition, we strategically select router locations and evaluate the stability and time sensitivity of the constructed network. The experimental results demonstrate the feasibility and reliability of the proposed multi-hop wireless network for seamless data transmission onboard ships. Full article

(This article belongs to the Special Issue Advances in Sensor Technology in Smart Ships and Offshore Facilities)

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17 pages, 1698 KiB

Open AccessArticle

Optimization of Maritime Communication Workflow Execution with a Task-Oriented Scheduling Framework in Cloud Computing

by Zulfiqar Ahmad, Tayfun Acarer and Wooseong Kim

J. Mar. Sci. Eng. 2023, 11(11), 2133; https://doi.org/10.3390/jmse11112133 - 08 Nov 2023

Viewed by 893

Abstract

To ensure safe, effective, and efficient marine operations, the optimization of maritime communication workflows with a task-oriented scheduling framework is of the utmost importance. Navigation, vessel traffic management, emergency response, and cargo operations are all made possible by maritime communication, which necessitates seamless [...] Read more.

To ensure safe, effective, and efficient marine operations, the optimization of maritime communication workflows with a task-oriented scheduling framework is of the utmost importance. Navigation, vessel traffic management, emergency response, and cargo operations are all made possible by maritime communication, which necessitates seamless information sharing between ships, ports, coast guards, and regulatory bodies. However, traditional communication methods face challenges in adapting to the dynamic and distributed nature of maritime activities. This study suggests a novel approach for overcoming these difficulties that combines task-oriented scheduling and resource-aware cloud environments to enhance marine communication operations. Utilizing cloud computing offers a scalable, adaptable infrastructure that can manage various computational and communication needs. Even during busy times, effective data processing, improved decision making, and improved communication are made possible by utilizing the cloud. The intelligent allocation and prioritization of communication activities using a task-oriented scheduling framework ensures that urgent messages receive prompt attention while maximizing resource utilization. The proposed approach attempts to improve marine communication workflows’ task prioritization, scalability, and resource optimization. In order to show the effectiveness of the proposed approach, simulations were performed in CloudSim. The performance evaluation parameters, i.e., throughput, latency, execution cost, and energy consumption, have been evaluated. Simulation results reflect the efficacy and practical usability of the framework in various maritime communication configurations. By making marine communication methods more durable, dependable, and adaptable to the changing needs of the maritime industry, this study advances maritime communication techniques. The findings of this research have the potential to revolutionize maritime communication, leading to safer, more efficient, and more resilient maritime operations on a large scale. Full article

(This article belongs to the Special Issue Advances in Sensor Technology in Smart Ships and Offshore Facilities)

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12 pages, 4167 KiB

Open AccessArticle

Multi-Channel Dual-Mode Oil Multi-Pollutant Detection Sensor

by Chenyong Wang, Hongpeng Zhang, Chenzhao Bai, Wei Li, Shengzhao Wang and Shuyao Zhang

J. Mar. Sci. Eng. 2023, 11(10), 1938; https://doi.org/10.3390/jmse11101938 - 08 Oct 2023

Viewed by 801

Abstract

In order to realize the lubricant fluid condition monitoring of ships and offshore engineering equipment, a multi-channel, dual-mode oil multi-pollution detection sensor is proposed and fabricated. The sensor has three detection channels connected via tee tubes, as well as two different detection modes, [...] Read more.

In order to realize the lubricant fluid condition monitoring of ships and offshore engineering equipment, a multi-channel, dual-mode oil multi-pollution detection sensor is proposed and fabricated. The sensor has three detection channels connected via tee tubes, as well as two different detection modes, inductive and capacitive, respectively. In comparison to the traditional sensor, this sensor not only has the ability to distinguish and identify a diverse range of pollutants, but it also experiences an 11-fold increase in its volume of flow, resulting in a significant enhancement in detection efficiency. The mechanism of the inductive and capacitive modes for the differentiated detection of multiple pollutants is elucidated through theoretical analysis. The performance of the sensor is investigated using the constructed experiment platform. The experimental results show that the sensor can realize the simultaneous detection of metallic and non-metallic contaminants in lubricating oil fluids. It can detect the smallest iron particle size of 54 μm, the smallest copper particle size of 90 μm, the smallest water droplet size of 116 μm, and the smallest air bubble size of 130 μm. A novel approach for achieving ship and marine engineering equipment health monitoring and fault diagnosis is presented in this study. Full article

(This article belongs to the Special Issue Advances in Sensor Technology in Smart Ships and Offshore Facilities)

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18 pages, 6299 KiB

Open AccessArticle

Research on High-Speed Catamaran Motion Reduction with Semi-Active Control of Flexible Pontoon

by Jiong Li, Zheng Li, Yongkang Wu, Xianqi Xiong, Zhi Li and Wei Xiong

J. Mar. Sci. Eng. 2023, 11(9), 1747; https://doi.org/10.3390/jmse11091747 - 05 Sep 2023

Cited by 1 | Viewed by 821

Abstract

A high-speed catamaran with a suspension system and flexible pontoons to reduce motion is proposed, and the vertical motion characteristics of the vessel are investigated. The results demonstrate that altering the stiffness of the flexible pontoon can significantly alter the motion characteristics of [...] Read more.

A high-speed catamaran with a suspension system and flexible pontoons to reduce motion is proposed, and the vertical motion characteristics of the vessel are investigated. The results demonstrate that altering the stiffness of the flexible pontoon can significantly alter the motion characteristics of a high-speed vessel when subjected to wave excitation. The maximum relative error between the theoretical and experimental values of the vertical dynamic characteristics of the flexible pontoon, considered as a gas spring, is 10.5%. The vertical force exerted by the pontoon exhibits nonlinear behavior in response to compression, yet displays approximately linear behavior within its primary operational range. The design of the Linear Quadratic Regulator controller, utilizing genetic algorithm optimization, avoids the issue of subjectively setting weight coefficients typically found in traditional control systems. This approach achieves the objective of determining the optimal feedback matrix within specified constraints. Simulation results illustrate that the LQR controller developed using genetic algorithm significantly enhance the semi-active suspension performance compared to the passive suspension system. The Root Mean Square value of the main cabin acceleration is reduced by 85.82%, simultaneously reducing the RMS value of the suspension dynamic travel by 85.03% and the RMS value of the pontoon dynamic displacement by 24.42%. These outcomes thoroughly substantiate the effective reduction in vertical motion, effectively attenuating the motion of high-speed vessels under wave excitation. Full article

(This article belongs to the Special Issue Advances in Sensor Technology in Smart Ships and Offshore Facilities)

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19 pages, 389 KiB

Open AccessArticle

A Fuzzy Dempster–Shafer Evidence Theory Method with Belief Divergence for Unmanned Surface Vehicle Multi-Sensor Data Fusion

by Shuanghu Qiao, Baojian Song, Yunsheng Fan and Guofeng Wang

J. Mar. Sci. Eng. 2023, 11(8), 1596; https://doi.org/10.3390/jmse11081596 - 15 Aug 2023

Viewed by 951

Abstract

The safe navigation of unmanned surface vehicles in the marine environment requires multi-sensor collaborative perception, and multi-sensor data fusion technology is a prerequisite for realizing the collaborative perception of different sensors. To address the problem of poor fusion accuracy for existing multi-sensor fusion [...] Read more.

The safe navigation of unmanned surface vehicles in the marine environment requires multi-sensor collaborative perception, and multi-sensor data fusion technology is a prerequisite for realizing the collaborative perception of different sensors. To address the problem of poor fusion accuracy for existing multi-sensor fusion methods without prior knowledge, a fuzzy evidence theory multi-sensor data fusion method with belief divergence is proposed in this paper. First of all, an adjustable distance for measuring discrepancies between measurements is devised to evaluate the degree of measurement closeness to the true value, which improves the adaptability of the method to different classes of sensor data. Furthermore, an adaptive multi-sensor measurement fusion strategy is designed for the case where the sensor accuracy is known in advance. Secondly, the affiliation function of the fuzzy theory is introduced into the evidence theory approach to assign initial evidence of measurements in terms of defining the degree of fuzzy support between measurements, which improves the fusion accuracy of the method. Finally, the belief Jensen–Shannon divergence and the Rényi divergence are combined for measuring the conflict between the evidence pieces to obtain the credibility degree as the reliability of the evidence, which solves the problem of high conflict between evidence pieces. Three examples of multi-sensor data fusion in different domains are employed to validate the adaptability of the proposed method to different kinds of multi-sensors. The maximum relative error of the proposed method for multiple sensor experiments is greater than or equal to 0.18%, and its error accuracy is much higher than the best result of 0.46% among other comparative methods. The experimental results verify that the proposed data fusion method is more accurate than other existing methods. Full article

(This article belongs to the Special Issue Advances in Sensor Technology in Smart Ships and Offshore Facilities)

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14 pages, 3145 KiB

Open AccessArticle

Study on the Classification Perception and Visibility Enhancement of Ship Navigation Environments in Foggy Conditions

by Chiming Wang, Boyan Fan, Yanan Li, Jingjing Xiao, Lanxi Min, Jing Zhang, Jiuhu Chen, Zhong Lin, Sunxin Su, Rongjiong Wu and Shunzhi Zhu

J. Mar. Sci. Eng. 2023, 11(7), 1298; https://doi.org/10.3390/jmse11071298 - 26 Jun 2023

Cited by 1 | Viewed by 1022

Abstract

Based on ship navigational requirements and safety in foggy conditions and with a particular emphasis on avoiding ship collisions and improving navigational abilities, we constructed a fog navigation dataset along with a new method for enhancing foggy images and perceived visibility using a [...] Read more.

Based on ship navigational requirements and safety in foggy conditions and with a particular emphasis on avoiding ship collisions and improving navigational abilities, we constructed a fog navigation dataset along with a new method for enhancing foggy images and perceived visibility using a discriminant deep learning architecture and the EfficientNet neural network by replacing the SE module and incorporating a convolution block attention module and focal loss function. The accuracy of our model exceeded 95%, which meets the needs of an intelligent ship navigation environment in foggy conditions. As part of our research, we also determined the best enhancement algorithm for each type of fog according to its classification. Full article

(This article belongs to the Special Issue Advances in Sensor Technology in Smart Ships and Offshore Facilities)

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14 pages, 2838 KiB

Open AccessArticle

Method for Identifying Materials and Sizes of Particles Based on Neural Network

by Xingming Zhang, Yewen Cao, Bingsen Xue, Geyang Hua and Hongpeng Zhang

J. Mar. Sci. Eng. 2023, 11(3), 541; https://doi.org/10.3390/jmse11030541 - 02 Mar 2023

Cited by 1 | Viewed by 1318

Abstract

Ships are equipped with power plants and operational assistance devices, both of which need oil for lubrication or energy transfer. Oil carries a large number of metal particles. By identifying the materials and sizes of metal particles in oil, the position and type [...] Read more.

Ships are equipped with power plants and operational assistance devices, both of which need oil for lubrication or energy transfer. Oil carries a large number of metal particles. By identifying the materials and sizes of metal particles in oil, the position and type of wear can be fully understood. However, existing online oil-detection methods make it difficult to identify the materials and the sizes of metal particles simultaneously and continuously. In this paper, we proposed a method for identifying the materials and the sizes of particles based on neural network. Firstly, a tree network model was designed. Then, each sub-network was trained in stages. Finally, the identification performance of several key groups of different frequencies and frequency combinations was tested. The experimental results showed that the method was effective. The accuracies of material and size identification reached 98% and 95% in the pre-training stage, and both had strong robustness. Full article

(This article belongs to the Special Issue Advances in Sensor Technology in Smart Ships and Offshore Facilities)

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15 pages, 5486 KiB

Open AccessArticle

Fabrication of Fluorine-Free Superhydrophobic Surface on Aluminum Substrate for Corrosion Protection and Drag Reduction

by Jingguo Fu, Yihe Sun, Jingye Wang, Hongpeng Zhang, Jifeng Zhang and Yulong Ji

J. Mar. Sci. Eng. 2023, 11(3), 520; https://doi.org/10.3390/jmse11030520 - 27 Feb 2023

Cited by 2 | Viewed by 1178

Abstract

A fluorine-free cerium palmitate superhydrophobic surface on an aluminum plate was fabricated via a two-step electrodeposition method. The mechanical durability, anti-corrosion performance, water repellency and drag reduction properties were tested. The results indicate that a superhydrophobic surface with a densely packed convex island [...] Read more.

A fluorine-free cerium palmitate superhydrophobic surface on an aluminum plate was fabricated via a two-step electrodeposition method. The mechanical durability, anti-corrosion performance, water repellency and drag reduction properties were tested. The results indicate that a superhydrophobic surface with a densely packed convex island shape with micro-pores and nano-scale strips was formed on the aluminum plate. Furthermore, the as-prepared surface exhibits excellent water-repelling ability with a water contact angle of 162.3° and a sliding angle of 1.5°. Owing to the protective effect of the convex island structure on the surface, the surface also shows superb mechanical durability, which is a shortcoming of ordinary electrodeposited surfaces. Moreover, compared with bare aluminum, the corrosion inhibition efficiency of the superhydrophobic surface is 99.55% and the surface drag decreases by 65.3% at a lower flow rate. Therefore, it is believed that the environmentally friendly fluorine-free superhydrophobic surface has promising potential applications in marine engineering. Full article

(This article belongs to the Special Issue Advances in Sensor Technology in Smart Ships and Offshore Facilities)

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14 pages, 4274 KiB

Open AccessArticle

Analysis of Exhaust Pollutants from Four-Stroke Marine Diesel Engines Based on Bench Tests

by Zhongmin Ma, Taili Du, Shulin Duan, Hongfei Qu, Kai Wang, Hui Xing, Yongjiu Zou and Peiting Sun

J. Mar. Sci. Eng. 2023, 11(2), 413; https://doi.org/10.3390/jmse11020413 - 14 Feb 2023

Cited by 1 | Viewed by 1563

Abstract

Implementation of new emissions regulations calls for a reassessment of the emissions levels of newly built ships sailing in Chinese regions. In this paper, marine diesel engines are subjected to emissions bench tests using high-precision testing equipment. A total of 135 marine diesel [...] Read more.

Implementation of new emissions regulations calls for a reassessment of the emissions levels of newly built ships sailing in Chinese regions. In this paper, marine diesel engines are subjected to emissions bench tests using high-precision testing equipment. A total of 135 marine diesel engines meeting the Limits and Measurement Methods for Exhaust Pollutants from Marine Engines (CHINA I/II) were first systematically analyzed. The emission factors of marine main engines (ME) and auxiliary engines (AE) were obtained under different displacements. The results show that the fuel-based emission factors for NO_X + HC and CO meeting CHINA I/II are 25.80~44.87/16.47~46.35 and 2.47~13.22/1.64~5.62 kg/t-fuel, respectively. The energy-based emission factors for NO_X + HC, CO, CO₂, and PM satisfying CHINA I/II are 5.70~9.24/3.70~9.07, 0.49~2.30/0.36~0.99, 620~683/612~718, and 0.05~0.36/0.05~0.27 g/kWh, respectively. Additionally, the specific emission of NOx rises with the increase in single-cylinder displacement, so the CO emission limit of pure diesel fuel is recommended to be lower than 5 g/kWh. The results in this paper provide valuable basic data for research on and estimation of ship emissions in waterway transportation and for understanding the emission characteristics of marine diesel engines. Full article

(This article belongs to the Special Issue Advances in Sensor Technology in Smart Ships and Offshore Facilities)

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13 pages, 5627 KiB

Open AccessArticle

Development of Electromagnetic Current Meter for Marine Environment

by Shizhe Chen, Yushang Wu, Shixuan Liu, Yingdong Yang, Xiaozheng Wan, Xianglong Yang, Keke Zhang, Bo Wang and Xingkui Yan

J. Mar. Sci. Eng. 2023, 11(1), 206; https://doi.org/10.3390/jmse11010206 - 12 Jan 2023

Cited by 1 | Viewed by 2235

Abstract

Ocean current is one of the most important parameters in ocean observation, and ocean current measurement based on electromagnetic induction is becoming more and more important because of its advantages such as simple structure and high measurement accuracy. However, it is difficult to [...] Read more.

Ocean current is one of the most important parameters in ocean observation, and ocean current measurement based on electromagnetic induction is becoming more and more important because of its advantages such as simple structure and high measurement accuracy. However, it is difficult to detect weak current signals in a complex marine environment. In this paper, an electromagnetic induction current measurement scheme based on lock-in amplification technology is proposed. Key technologies such as the evaluation of induced current intensity, overall design, circuit design, and orientation design of the current meter were studied. The prototype of the electromagnetic current meter was developed and tested in the laboratory and at sea. The repeatability of current velocity and current direction was higher than 1.5 cm/s and 1.5°, respectively. A comparison test between the electromagnetic current meter prototype and Nortek ADCP (Acoustic Doppler Current Profiler) installed on a buoy at sea was carried out, and the correlation coefficients of the current velocity and current direction datum were 0.90 and 0.96, respectively. Through continuous on-site and fault-free operations at sea, the experimental data show that the electromagnetic current meter has good adaptability at sea, which provides feasible technical and equipment support for ocean current observation. Full article

(This article belongs to the Special Issue Advances in Sensor Technology in Smart Ships and Offshore Facilities)

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16 pages, 3079 KiB

Open AccessArticle

A Multi-Model Diagnosis Method for Slowly Varying Faults of Plunger Pump

by Changli Yu, Haodong Yan, Xingming Zhang and Hua Ye

J. Mar. Sci. Eng. 2022, 10(12), 1968; https://doi.org/10.3390/jmse10121968 - 11 Dec 2022

Cited by 1 | Viewed by 979

Abstract

As the energy supply component of hydraulic transmission systems, the plunger pump is widely used in the field of ship and ocean engineering. Thus, its fault diagnosis is of great importance. The multi-model fault diagnosis method based on the Kalman filter is slow [...] Read more.

As the energy supply component of hydraulic transmission systems, the plunger pump is widely used in the field of ship and ocean engineering. Thus, its fault diagnosis is of great importance. The multi-model fault diagnosis method based on the Kalman filter is slow in detection and isolation in the process of slowly varying fault diagnosis, and it may be diagnosed as a false failure. In this article, to improve the performance of the multi-model fault diagnosis method, we combine the method and support vector machine and propose a new method by fusing the conditional probability of the multi-model with the posterior probability of the support vector machine. The experimental results on a marine plunger pump illustrate the effectiveness of the proposed method. With the appropriate weight coefficient, the detection speed and isolation speed of the joint multi-model method are improved after the combination of the support vector machine, and the new method has better robustness. Full article

(This article belongs to the Special Issue Advances in Sensor Technology in Smart Ships and Offshore Facilities)

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18 pages, 6478 KiB

Open AccessArticle

Investigation of a Cabin Suspended and Articulated Rescue Vessel in Terms of Motion Reduction

by Jiong Li, Xuesong Bai, Yang Li, Hongwang Du, Fangtao Fan, Shuaixian Li, Zhi Li and Wei Xiong

J. Mar. Sci. Eng. 2022, 10(12), 1966; https://doi.org/10.3390/jmse10121966 - 10 Dec 2022

Cited by 1 | Viewed by 1644

Abstract

To solve the problem that small rescue vessels are seriously affected by waves when working at sea, which causes damage to the crew and equipment, a catamaran with a suspended and articulated cabin is designed. The multi-body dynamic model of the whole vessel [...] Read more.

To solve the problem that small rescue vessels are seriously affected by waves when working at sea, which causes damage to the crew and equipment, a catamaran with a suspended and articulated cabin is designed. The multi-body dynamic model of the whole vessel based on SimMechanics is built, and the prototype is tested on the water. The correctness of the model is verified based on the three aspects of the roll motion, pitch motion, and heave motion of the rescue platform. The motion of the rescue platform is simulated under the conditions of bow waves and large heave waves, and the RMS value of the vertical acceleration of the rescue platform is taken as the response target to analyze the influence of the suspension system parameters of the whole vessel. The suspension system has good buffering and motion reduction effects under various working conditions, and it has an obvious reduction effect on the vertical acceleration at the rescue platform. The larger the amplitude of the wave impact, the more obvious the effect. The articulated system reduces the roll angle and the heave amplitude under the conditions of bow waves. Full article

(This article belongs to the Special Issue Advances in Sensor Technology in Smart Ships and Offshore Facilities)

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12 pages, 6026 KiB

Open AccessArticle

Selective NO₂ Detection by Black Phosphorus Gas Sensor Prepared via Aqueous Route for Ship Pollutant Monitoring

by Yang Wang, Yujia Wang, Yue Sun, Kuanguang Zhang, Chenyang Zhang, Jianqiao Liu, Ce Fu and Junsheng Wang

J. Mar. Sci. Eng. 2022, 10(12), 1892; https://doi.org/10.3390/jmse10121892 - 04 Dec 2022

Cited by 2 | Viewed by 1780

Abstract

The emission of nitrogen dioxide (NO₂) caused by marine transportation has attracted worldwide environmental concerns. Two-dimensional (2D) black phosphorus (BP) is an emerging semiconductive material with the advantages of high electron mobility, a layer-dependent direct band gap and a large specific [...] Read more.

The emission of nitrogen dioxide (NO₂) caused by marine transportation has attracted worldwide environmental concerns. Two-dimensional (2D) black phosphorus (BP) is an emerging semiconductive material with the advantages of high electron mobility, a layer-dependent direct band gap and a large specific surface area. These properties ensure excellent potential in gas-sensing applications. In this work, BP quantum dots (QDs) are synthesized from commercial red phosphorus (RP) fine powder via the aqueous route. The BP QDs show uniform size distribution with an average size of 2.2 nm. They are employed to fabricate thin film gas sensors by aerial-assisted chemical vapor deposition. The microstructure, morphology and chemical composition are determined by various characterizations. The sensor performances are evaluated with the optimized response set to 100 ppm NO₂ of 10.19 and a sensitivity of 0.48 is obtained. The gas sensor also demonstrates excellent repeatability, selectivity and stability. The fabricated thin film gas sensor assembled by BP QDs exhibits prospective applications in selective NO₂ detection for marine gaseous pollutant monitoring and control. Full article

(This article belongs to the Special Issue Advances in Sensor Technology in Smart Ships and Offshore Facilities)

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11 pages, 5194 KiB

Open AccessArticle

Magnetic Plug Sensor with Bridge Nonlinear Correction Circuit for Oil Condition Monitoring of Marine Machinery

by Yuwei Zhang, Jiaju Hong, Haotian Shi, Yucai Xie, Hongpeng Zhang, Shuyao Zhang, Wei Li and Haiquan Chen

J. Mar. Sci. Eng. 2022, 10(12), 1883; https://doi.org/10.3390/jmse10121883 - 03 Dec 2022

Cited by 3 | Viewed by 1322

Abstract

Diesel engines in marine power systems often work in extreme environments. Oil monitoring technology can guarantee the operational safety of diesel engines. In this paper, a magnetic plug sensor for oil debris monitoring is proposed to improve sensitivity and accuracy. Through finite element [...] Read more.

Diesel engines in marine power systems often work in extreme environments. Oil monitoring technology can guarantee the operational safety of diesel engines. In this paper, a magnetic plug sensor for oil debris monitoring is proposed to improve sensitivity and accuracy. Through finite element analysis, absolute deviation is reduced by optimizing the sensor structure. A bridge nonlinear correction circuit is designed to make sensitivity consistent over the entire scale range, which can facilitate calibration and data processing. In order to reduce noise and amplify the signal effectively, a signal post-processing circuit is adopted as well, which consists of a first stage filter circuit, a second stage filter, an active filter module, and an instrumentation amplifier. Therefore, this magnetic plug sensor exhibits better sensitivity and accuracy. Furthermore, a void test and a dynamic test are carried out to investigate its performance. There is a linear relationship between the voltage and the particle mass for the sensor with a bridge nonlinear correction circuit. The results illustrate a minimum of 0.033 mg iron debris with a 1.647 signal-to-noise ratio. Additionally, it can capture and detect 47 μm particles with a debris capture rate of over 90%, which allows it to excel in early fault diagnosis as well. Full article

(This article belongs to the Special Issue Advances in Sensor Technology in Smart Ships and Offshore Facilities)

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17 pages, 2259 KiB

Open AccessArticle

Multi-Branch Gated Fusion Network: A Method That Provides Higher-Quality Images for the USV Perception System in Maritime Hazy Condition

by Yunsheng Fan, Longhui Niu and Ting Liu

J. Mar. Sci. Eng. 2022, 10(12), 1839; https://doi.org/10.3390/jmse10121839 - 01 Dec 2022

Cited by 2 | Viewed by 1403

Abstract

Image data acquired by unmanned surface vehicle (USV) perception systems in hazy situations is characterized by low resolution and low contrast, which can seriously affect subsequent high-level vision tasks. To obtain high-definition images under maritime hazy conditions, an end-to-end multi-branch gated fusion network [...] Read more.

Image data acquired by unmanned surface vehicle (USV) perception systems in hazy situations is characterized by low resolution and low contrast, which can seriously affect subsequent high-level vision tasks. To obtain high-definition images under maritime hazy conditions, an end-to-end multi-branch gated fusion network (MGFNet) is proposed. Firstly, residual channel attention, residual pixel attention, and residual spatial attention modules are applied in different branch networks. These attention modules are used to focus on high-frequency image details, thick haze area information, and contrast enhancement, respectively. In addition, the gated fusion subnetworks are proposed to output the importance weight map corresponding to each branch, and the feature maps of three different branches are linearly fused with the importance weight map to help obtain the haze-free image. Then, the network structure is evaluated based on the comparison with pertinent state-of-the-art methods using artificial and actual datasets. The experimental results demonstrate that the proposed network is superior to other previous state-of-the-art methods in the PSNR and SSIM and has a better visual effect in qualitative image comparison. Finally, the network is further applied to the hazy sea–skyline detection task, and advanced results are still achieved. Full article

(This article belongs to the Special Issue Advances in Sensor Technology in Smart Ships and Offshore Facilities)

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15 pages, 12984 KiB

Open AccessArticle

Marine-Hydraulic-Oil-Particle Contaminant Identification Study Based on OpenCV

by Chenyong Wang, Chao Yang, Hongpeng Zhang, Shengzhao Wang, Zhaoxu Yang, Jingguo Fu and Yuqing Sun

J. Mar. Sci. Eng. 2022, 10(11), 1789; https://doi.org/10.3390/jmse10111789 - 21 Nov 2022

Cited by 5 | Viewed by 1645

Abstract

Particulate pollutants mixed in hydraulic oil will lead to the failure of the marine hydraulic system. Nowadays, the current identification methods of particulate pollutants in oil make it challenging to obtain the specific parameters of pollutants. For this reason, this paper proposes a [...] Read more.

Particulate pollutants mixed in hydraulic oil will lead to the failure of the marine hydraulic system. Nowadays, the current identification methods of particulate pollutants in oil make it challenging to obtain the specific parameters of pollutants. For this reason, this paper proposes a recognition method of marine-hydraulic-oil-particle pollutants based on OpenCV. The image of particles in the marine hydraulic oil was preprocessed by OpenCV software and using the Canny operator edge detection algorithm to extract the contour of particle pollutants to obtain their area and perimeter. The recognition accuracy reached 95%. Using the Douglas–Peucker algorithm for fit polygons, then image moments to obtain the angle-distance waveform of particulate pollutants, the shape of marine-hydraulic-oil particulate pollutants was successfully identified. The designed method has the advantages of fast calculation efficiency, high accuracy, and real-time detection of various parameters of particulate pollutants in marine hydraulic oil. It has great significance for the fault diagnosis of hydraulic systems and prolonging the working life of hydraulic equipment. This research provides a new idea for the condition monitoring and fault diagnosis of ships and offshore engineering equipment. Full article

(This article belongs to the Special Issue Advances in Sensor Technology in Smart Ships and Offshore Facilities)

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Review

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23 pages, 2844 KiB

Open AccessReview

Computer Vision and Image Processing Approaches for Corrosion Detection

by Ahmad Ali Imran Mohd Ali, Shahrizan Jamaludin, Md Mahadi Hasan Imran, Ahmad Faisal Mohamad Ayob, Sayyid Zainal Abidin Syed Ahmad, Mohd Faizal Ali Akhbar, Mohammed Ismail Russtam Suhrab and Mohamad Riduan Ramli

J. Mar. Sci. Eng. 2023, 11(10), 1954; https://doi.org/10.3390/jmse11101954 - 10 Oct 2023

Viewed by 2747

Abstract

Corrosion is an undesirable phenomenon resulting in material deterioration and degradation through electrochemical or chemical reactions with the surrounding environment. Additionally, corrosion presents considerable threats in both the short and long term because of its ability to create failures, leakages, and damage to [...] Read more.

Corrosion is an undesirable phenomenon resulting in material deterioration and degradation through electrochemical or chemical reactions with the surrounding environment. Additionally, corrosion presents considerable threats in both the short and long term because of its ability to create failures, leakages, and damage to materials, equipment, and environment. Despite swift technological developments, it remains difficult to determine the degrees of corrosion due to the different textures and the edgeless boundary of corrosion surfaces. Hence, there is a need to investigate the robust corrosion detection algorithms that are suitable for all degrees of corrosion. Recently, many computer vision and image processing algorithms have been developed for corrosion prediction, assessment, and detection, such as filtering, texture, color, pixelation, image enhancement, wavelet transformation, segmentation, classification, and clustering approaches. As a result, this paper reviews and discusses the state-of-the-art computer vision and image processing methods that have been developed for corrosion detection in various applications, industries, and academic research. The challenges for corrosion detection using computer vision and image processing algorithms are also explored. Finally, recommendations for future research are also detailed. Full article

(This article belongs to the Special Issue Advances in Sensor Technology in Smart Ships and Offshore Facilities)

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