The maritime industry is one of the most competitive industries today. However, there is a tendency for the profit margins of shipping companies to reduce due to an increase in operational costs, and it does not seem that this trend will change in the near future. The most important reason for the increase in operating costs relates to the increase in fuel prices. To compensate for the increase in operating costs, shipping companies can either renew their fleet or try to make use of new technologies to optimize the performance of their existing one. The software structure in the maritime industry has changed and is now leaning towards the use of Artificial Intelligence (AI) and, more specifically, Machine Learning (ML) for calculating its operational scenarios as a way to compensate the reduction of profit. While AI is a technology for creating intelligent systems that can simulate human intelligence, ML is a subfield of AI, which enables machines to learn from past data without being explicitly programmed. ML has been used in other industries for increasing both availability and profitability, and it seems that there is also great potential for the maritime industry. In this paper the authors compares the performance of multiple regression algorithms like Artificial Neural Network (ANN), Tree Regressor (TRs), Random Forest Regressor (RFR), K-Nearest Neighbor (kNN), Linear Regression, and AdaBoost, in predicting the output power of the Main Engines (M/E) of an ocean going vessel. These regression algorithms are selected because they are commonly used and are well supported by the main software developers in the area of ML. For this scope, measured values that are collected from the onboard Automated Data Logging & Monitoring (ADLM) system of the vessel for a period of six months have been used. The study shows that ML, with the proper processing of the measured parameters based on fundamental knowledge of naval architecture, can achieve remarkable prediction results. With the use of the proposed method there was a vast reduction in both the computational power needed for calculations, and the maximum absolute error value of prediction.

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In this paper, four machine learning algorithms are examined regarding their effectiveness in dealing with a complete lack of sensor drift values for a crucial parameter for ship performance evaluation, such as a ship’s speed through water (STW). A basic Linear Regression algorithm, a more sophisticated ensemble model (Random Forest) and two modern Recurrent Neural Networks i.e., Long Short-Term Memory (LSTM) and Neural Basis Expansion Analysis for Time Series (N-Beats) are evaluated. A computational algorithm written in python language with the use of the Darts library was developed for this scope. The results regarding the selected parameter (STW) are provided on a real- or near-to-real-time basis. The algorithms were able to estimate the speed through water in a progressive manner, with no initial values needed, making it possible to replace the complete missingness of the label data. A physical model developed with the simulation platform of Siemens Simcenter Amesim is used to calculate the ship STW under the real operating conditions of a banker ship type during a period of six months. These theoretically obtained values are used as reference values (“ground-truth” values) to evaluate the performance of each of the four machine learning algorithms examined.

In the extremely competitive environment of shipping, minimizing shipping cost is the key factor for the survival and growth of shipping companies. However, stricter rules and regulations that aim at the reduction of greenhouse gas emissions published by the International Maritime Organization, force shipping companies to increase the operational efficiency of their fleet. The prediction of a ship speed in actual seas with a given power by its engine is the most important performance indicator and thus makes it the “holy grail” in pursuing better efficiency. Traditionally, tank model tests and semi-empirical formulas were the preferred solution for the aforementioned prediction and are still widely applied. However, currently, with the increased computational power that is widely available, novel and more sophisticated methods taking into consideration computational fluid dynamics (CFD) and machine learning (ML) algorithms are emerging. In this paper, we briefly present the different approaches in the prediction of a ship’s speed but focus on ML methods comparing a representative number of the latest data-driven models used in papers, to provide guidelines, discover trends and identify the challenges to be faced by researchers. From this comparison, we can distinguish that artificial neural networks (ANN), being used in 73.3% of the reviewed papers, dominate as the algorithm of choice. Researchers mostly rely on physical laws governing the phenomena in the crucial part of data preprocessing tasks. Lastly, most researchers rely on data acquisition systems installed at ships in order to achieve usable results.