A Machine Learning-Based Method to Predict Engine Power

Kim, Yejin; Han, Seungjae; Jung, Bong-Kyu; Han, Seung-Hun; Lee, Sang Bong

doi:10.7837/kosomes.2019.25.7.851

Cited by 3 publications

(5 citation statements)

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“…4 Shaft power was predicted using a support vector machine (SVM), demonstrating excellent prediction performance compared to the existing ISO 15016 maritime test analysis method. 5 Artificial neural networks (ANN) were utilized to predict fuel consumption, exhaust gas temperature, and volume efficiency. 6 Moreover, several studies have compared the predictive performance of different models.…”

Section: Introductionmentioning

confidence: 99%

“…1,4,6,7,12,16,17 Even when actual data is utilized, it is difficult to verify the reliability of data collection through analog handwritten data, such as noon reports or logbooks. 5,10,18,19 Furthermore, most recent work on predicting ship engine performance has focused on fully combustion phases with constant engine speed, 3,5,19,20 or failed to take engine speed conditions into account. 18 Notably, research on low-speed conditions and transient regions, which are particularly challenging to predict, is inadequate.…”

Section: Introductionmentioning

confidence: 99%

“…2,21 However, machine learning models are often difficult to interpret and have long computing times. 3,5,6 Moreover, studies using actual data may also overlook sudden fluctuations in the ship engine due to long data collection intervals, or may rely on average values. 2,3,18 In this study, a ship performance prediction model framework was developed, which incorporates a GBM that combines the WBM and BBMs that are built separately based on speed conditions.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An integrated grey-box model for accurate ship engine performance prediction under varying speed and environmental conditions

Park,

Noh,

Kang

et al. 2024

International Journal of Engine Research

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The development of ship engine fault diagnosis has led to an increased interest in predicting marine engine performance under various environmental and operating conditions. However, predicting engine performance using a single model is limited due to the various characteristics of ship engines depending on speed and environmental conditions. To address this issue, we propose a grey box model (GBM)-based ship engine performance prediction framework that combines the white box model and black box model (BBM) appropriately for both low- and high-speed operating conditions. The application of data preprocessing techniques, such as clustering and cleaning, under specific engine and environmental conditions, combined with dimensionality reduction techniques (partial least square and principal component) and the use of WBM/BBM models under classified speed conditions, makes the proposed framework accessible to real-world operators through data-driven approaches and domain knowledge. We demonstrate that the proposed framework improves the robustness, accuracy, and efficiency of engine performance predictions by considering the characteristics of each speed in real-world navigation data, compared to using single models.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An integrated grey-box model for accurate ship engine performance prediction under varying speed and environmental conditions

Park,

Noh,

Kang

et al. 2024

International Journal of Engine Research

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“…Other than that, shaft power or vessel propulsion power-related research has been conducted using support vector-based machine learning models. Among those are Support Vector Machine (SVM) [ 14 ] and Support Vector Regression (SVR) [ 15 ], both in 2021. Both showed satisfactory results.…”

Section: Introductionmentioning

confidence: 99%

Feature Attribution Analysis to Quantify the Impact of Oceanographic and Maneuverability Factors on Vessel Shaft Power Using Explainable Tree-Based Model

Kim¹,

Handayani

Lee³

et al. 2023

Sensors

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A vessel sails above the ocean against sea resistance, such as waves, wind, and currents on the ocean surface. Concerning the energy efficiency issue in the marine ecosystem, assigning the right magnitude of shaft power to the propeller system that is needed to move the ship during its operations can be a contributive study. To provide both desired maneuverability and economic factors related to the vessel’s functionality, this research studied the shaft power utilization of a factual vessel operational data of a general cargo ship recorded during 16 months of voyage. A machine learning-based prediction model that is developed using Random Forest Regressor achieved a 0.95 coefficient of determination considering the oceanographic factors and additional maneuver settings from the noon report data as the model’s predictors. To better understand the learning process of the prediction model, this study specifically implemented the SHapley Additive exPlanations (SHAP) method to disclose the contribution of each predictor to the prediction results. The individualized attributions of each important feature affecting the prediction results are presented.

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Big Data Analysis of the Speed Performance of a 176k DWT Bulk Carrier in Real Operating Conditions

Cho,

Lee

2024

JMSE

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Assessment of ship performance under in-service conditions is challenging due to the complex effects of many environmental disturbances. ISO 15016 and ISO 19030 standards are commonly used to evaluate ship operating performance. However, ISO 15016 requires numerous variables, a complex calculation formula, and considerable time and cost, and ISO 19030 only evaluates the reduction of ship speed caused by wind and neglects the effect of waves. To improve both standards and achieve a more accurate ship performance assessment, this study proposes a new performance prediction model, the multi-input single-output (MISO) system, which assumes that each ship has specific frequency characteristics according to type and size. Based on this new model, in-service navigation data collected from a 176k DWT bulk carrier, which amount to 5.7 million data points, are analyzed to assess the speed performance of the vessel subject to environmental disturbances. The proposed model was validated by comparing its results with ISO 19030 and specifically assessing the speed–power curves and speed reduction measured in operational data with the influence of environmental disturbances removed.

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A Machine Learning-Based Method to Predict Engine Power

Cited by 3 publications

References 0 publications

An integrated grey-box model for accurate ship engine performance prediction under varying speed and environmental conditions

An integrated grey-box model for accurate ship engine performance prediction under varying speed and environmental conditions

Feature Attribution Analysis to Quantify the Impact of Oceanographic and Maneuverability Factors on Vessel Shaft Power Using Explainable Tree-Based Model

Big Data Analysis of the Speed Performance of a 176k DWT Bulk Carrier in Real Operating Conditions

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