Research on Data Monitoring System for Intelligent Ship

Li, Guiqin; Deng, Xuechao; Zhou, Maoheng; Zhu, Qiuyu; Lan, Jian; Xia, Hong; Mitrouchev, Peter

doi:10.1007/978-981-15-2341-0_29

Cited by 7 publications

(4 citation statements)

References 11 publications

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“…The length, width, and thickness of the plate are denoted as L, W, and T P , respectively, and the height and thickness of the stiffener as H and T S , respectively. The stiffener spacing is 0.5 W. Young's modulus of the material is E = 2:1310 5 MPa, Poisson's ratio is y = 0:3, and the density is r = 7:85310 À9 t=mm 3 . It is necessary to consider the damping in the dynamic analysis of hull-stiffened plates.…”

Section: Fe Models Of Hull Stiffened Platementioning

confidence: 99%

“…1–3 Moreover, because ship structures are subjected to variable cyclic loading during navigation, 4 cracks will be caused, and they can grow to a critical size, which jeopardizes their integrity. With the development of intelligent ships, 5 it is imperative to realize real-time online damage detection. Deep learning is used extensively for damage detection because it can work with large volumes of data and learn multi-scale, multi-level, and hierarchical representation.…”

Section: Introductionmentioning

confidence: 99%

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A deep learning-based method for hull stiffened plate crack detection

Wang

2020

Proceedings of the Institution of Mechanical Engineers, Part M:

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Deep learning has attracted the attention of many researchers for structural health monitoring. However, it is difficult to use most of the deep learning-based techniques to detect damage throughout the life cycle of a large or inaccessible structure, especially a ship. Few studies have focused on hull stiffened plate crack damage detection. We propose such a method based on deep learning using a convolutional neural network (CNN). The model is trained on acceleration data, which are calculated by the Abaqus scripting interface. Five crack locations and four crack lengths are considered, as well as the intact condition. The effects of damping ratio, loading area, and load level on the proposed method are considered. The robustness of the proposed approach to noise and stiffener slenderness ratio are also discussed. The proposed method is compared to the multilayer perceptron method by wavelet packet transformation using the same data, so as to quantify its performance. The results show that the proposed method performs better at single- and double-crack detection, and is less sensitive to noise, damping ratio, loading area, and load level.

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Section: Fe Models Of Hull Stiffened Platementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A deep learning-based method for hull stiffened plate crack detection

Wang

2020

Proceedings of the Institution of Mechanical Engineers, Part M:

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“…With the advancement of the economy, the usage of waterborne transportation is steadily increasing, making the monitoring of vessels on waterways of paramount importance [1]. However, conventional vision-based detection and tracking algorithms face limitations, particularly in low-light conditions such as adverse weather, fog, and nighttime scenarios.…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Vessel Detection and Tracking System Based on LiDAR

Qi,

Huang,

Zhang

et al. 2023

Sensors

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Vessel detection and tracking is of utmost importance to river traffic. Efficient detection and tracking technology offer an effective solution to address challenges related to river traffic safety and congestion. Traditional image-based object detection and tracking algorithms encounter issues such as target ID switching, difficulties in feature extraction, reduced robustness due to occlusion, target overlap, and changes in brightness and contrast. To detect and track vessels more accurately, a vessel detection and tracking algorithm based on the LiDAR point cloud was proposed. For vessel detection, statistical filtering algorithms were integrated into the Euclidean clustering algorithm to mitigate the effect of ripples on vessel detection. Our detection accuracy of vessels improved by 3.3% to 8.3% compared to three conventional algorithms. For vessel tracking, L-shape fitting of detected vessels can improve the efficiency of tracking, and a simple and efficient tracking algorithm is presented. By comparing three traditional tracking algorithms, an improvement in multiple object tracking accuracy (MOTA) and a reduction in ID switch times and number of missed detections were achieved. The results demonstrate that LiDAR point cloud-based vessel detection can significantly enhance the accuracy of vessel detection and tracking.

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“…However, this method relies more on regression analysis of prior experience in evaluating the internal networks, and it lacks applicability for the analysis of the relationship between discontinuous complex systems [11]- [14]. In [15], [16]. In a recent report, it was proposed to apply exploratory methods to analyze the effectiveness of complex system by analyzing the relationship between system composition and system operation efficiency [17], [18].…”

Section: Introductionmentioning

confidence: 99%

A Methodology to Evaluate the Effectiveness of Intelligent Ship Navigational Information Monitoring System

Jun

Liu

et al. 2020

IEEE Access

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The perception and acquisition of navigation-related information are essential for the safety of intelligent ship. To address the effectiveness of navigational information monitoring system designed for the intelligent ship, in the present study, a comprehensive evaluation methodology is proposed. The various functions designed for obtaining corresponding navigational information are reorganized to develop the structure of intelligent ship navigational information monitoring system, which is subjected to evaluate the effectiveness by analyzing the information behavior processes in this proposed system and the available information monitoring technologies applied on intelligent ships. The orthogonal exploratory analysis technique was applied to establish the functional relationship between system effectiveness and contributing subsystems, as a result, the contribution degree distribution of contributing subsystems identified for system effectiveness is obtained. Finally, an application of the proposed methodology verifies its feasibility in calculating system effectiveness and its potential to extend to intelligent ships at different levels. The present study provides a new perspective to understand the safety of intelligent ships, and the comprehensive evaluation methodology provides a new path for the effectiveness analysis of intelligent ship navigational information monitoring system.

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Research on Data Monitoring System for Intelligent Ship

Cited by 7 publications

References 11 publications

A deep learning-based method for hull stiffened plate crack detection

A deep learning-based method for hull stiffened plate crack detection

A Real-Time Vessel Detection and Tracking System Based on LiDAR

A Methodology to Evaluate the Effectiveness of Intelligent Ship Navigational Information Monitoring System

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