A machine learning-based method for prediction of ship performance in ice: Part I. ice resistance

Sun, Qianyang; Zhang, Meng; Zhou, Li; Garme, Karl; Burman, Magnus

doi:10.1016/j.marstruc.2022.103181

Cited by 40 publications

(20 citation statements)

References 37 publications

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“…The propulsion power is linearly related to the displacement. For ice-strengthened vessels under ice classes Ice2 and Ice3, the displacement required by the minimum propulsion power should not be more than 8000 m 3 .…”

Section: Propulsion Power Requirement Of Classification Societiesmentioning

confidence: 99%

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A Machine-Learning-Based Method for Ship Propulsion Power Prediction in Ice

Zhou

Sun

Ding

et al. 2023

JMSE

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In recent years, safety issues respecting polar ship navigation in the presence of ice have become a research hotspot. The accurate prediction of propulsion power plays an important role in ensuring safe ship navigation and evaluating ship navigation ability, and deep learning has been widely applied in the field of shipping, of which the artificial neural network (ANN) is a common method. This study combines the scientific problems of ice resistance and propulsion power for polar ship design, focusing on the design of an ANN model for predicting the propulsion power of polar ships. Reference is made to the traditional propulsion power requirements of various classification societies, as well as ship model test and full-scale test data, to select appropriate input features and a training dataset. Three prediction methods are considered: building a radial basis function–particle swarm optimization algorithm (RBF-PSO) model to directly predict the propulsion power; based on the full-scale test and model test data, calculating the propulsion power using the Finnish–Swedish Ice Class Rules (FSICR) formula; using an ice resistance artificial neural network model (ANN-IR) to predict the ice resistance and calculate the propulsion power using the FSICR formula. Prediction errors are determined, and a sensitivity analysis is carried out with respect to the relevant parameters of propulsion power based on the above methods. This study shows that the RBF-PSO model based on nine feature inputs has a reasonable generalization effect. Compared with the data of the ship model test and full-scale test, the average error is about 14%, which shows that the method has high accuracy and can be used as a propulsion power prediction tool.

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Section: Propulsion Power Requirement Of Classification Societiesmentioning

confidence: 99%

“…However, they rely on understanding the mechanisms of ice-ship interactions and include several simplifications and assumptions [2]. Sun et al [3] designed a reliable ice resistance prediction ANN model within the set parameter range by selecting appropriate ship and ice parameters.…”

Section: Introductionmentioning

confidence: 99%

A Machine-Learning-Based Method for Ship Propulsion Power Prediction in Ice

Zhou

Sun

Ding

et al. 2023

JMSE

Self Cite

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“…models on global ice loas in level ice (Tan et al, 2014;Xuan et al, 2021;Li et al, 2020;Li et al, 2020b;Ni et al, 2020) as well as escort in level ice (Liu and Ji, 2021), ice resistance in pack and thin ice (Zong and Zhou, 2019), ship resistance in open-water ice channel (Huang et al, 2021), narrow ice channel (Sazonov and Dobrodeev, 2021;Li et al, 2021) as well as in restricted brash ice channel (Zhang et al, 2022;Luo et al, 2020;Xie et al, 2022), ship resistance in unconsolidated ridges (Gong et al, 2019), resistance in ice floes (Huang et al, 2020(Huang et al, , 2021b, broken ice pieces . In addition, Kim et al (2020) and Sun et al (2022) applied machine learning method to estimate ice resistance based on model test data, while Milaković et al (2020) established machine learning model based on simulation data. Generally, a wide range of models are developed on ice resistance topic.…”

Section: Extensive Review Outcomes Outside the Project To Supplement ...mentioning

confidence: 99%

On risk management of shipping system in ice-covered waters: Review, analysis and toolbox based on an eight-year polar project

Kujala

Kuikka

2022

Ocean Engineering

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“…Currently, machine learning and deep learning are common methods used for data-driven fault diagnosis and target identification [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. Deep learning theory has gradually attracted widespread attention in academia and industry.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis for spent fuel shearing machines based on Bayesian optimization and CBAM-ResNet

Wang,

Chen,

Wang

et al. 2023

Meas. Sci. Technol.

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Spent fuel shearing machines in nuclear power plants are important equipment for the head end of spent fuel reprocessing in power reactors. Condition monitoring and fault diagnosis play important roles in ensuring the safe operation of spent fuel shearing machines, avoiding serious accidents, and reducing their maintenance time and cost. Existing research on fault diagnosis of spent fuel shearing machiness has some shortcomings: (a) the current research on fault diagnosis of shearing machines is small and diagnostic accuray is not high. The research methodology of shearing machines needs to be updated; (b) the high difficulty in obtaining fault data and the often limited and highly informative fault data for shearing machines lead to low diagnostic performance. To solve these problems, this study constructs a residual network model based on Bayesian optimization and CBAM. First, dual-channel difference method is introduced into the preprocessing of noise signals, and two data enhancements were applied to the Mel spectrograms used as inputs to the model. Second, the attention mechanism CBAM is introduced to improve the residual network to enhance the deep feature extraction ability of the network, and the Bayesian optimization algorithm is used to train the hyperparameters, such as the optimizer, and retrain the network model after obtaining the optimal hyperparameters. Finally, the feasibility and effectiveness of the proposed model are verified through experiments on the noise signals of spent fuel shearing machines. The experimental results show that the diagnostic accuracy of the constructed model is 93.67%, which is a significant improvement over the other methods.

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A machine learning-based method for prediction of ship performance in ice: Part I. ice resistance

Cited by 40 publications

References 37 publications

A Machine-Learning-Based Method for Ship Propulsion Power Prediction in Ice

A Machine-Learning-Based Method for Ship Propulsion Power Prediction in Ice

On risk management of shipping system in ice-covered waters: Review, analysis and toolbox based on an eight-year polar project

Fault diagnosis for spent fuel shearing machines based on Bayesian optimization and CBAM-ResNet

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