Machine Learning-Based Extraction Method for Marine Load Cycles with Environmentally Sustainable Applications

Xiaojun Sun (), Yingbo Gao, Qiao Zhang and Shunliang Ding ()
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Xiaojun Sun: School of Automobile and Traffic Engineering, Liaoning University of Technology, Jinzhou 121001, China
Yingbo Gao: School of Automobile and Traffic Engineering, Liaoning University of Technology, Jinzhou 121001, China
Qiao Zhang: School of Automobile and Traffic Engineering, Liaoning University of Technology, Jinzhou 121001, China
Shunliang Ding: School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, China

Sustainability, 2024, vol. 16, issue 11, 1-21

Abstract: The current lack of harmonized standard test conditions for marine shipping hinders the comparison of performance and compliance assessments for different types of ships. This article puts forward a method for extracting ship loading cycles using machine learning algorithms. Time-series data are extracted from real ships in operation, and a segmented linear approximation method and a data normalization technique are adopted. A hierarchical-clustering type of soft dynamic time-warping similarity analysis method is presented to efficiently analyze the similarity of different time-series data, using soft dynamic time warping (Soft-DTW) combined with hierarchical clustering algorithms from the field of machine learning. The problem of data bias caused by spatial and temporal offset characteristics is effectively solved in marine test condition data. The validity and reliability of the proposed method are validated through the analysis of case data. The results demonstrate that the hierarchically clustered soft dynamic time-warping similarity analysis method can be considered reliable for obtaining test cases with different characteristics. Furthermore, it provides input conditions for effectively identifying the operating conditions of different types of ships with high levels of energy consumption and high emissions, thus allowing for the establishment of energy-saving and emissions-reducing sailing strategies.

Keywords: time-series data; similarity; hierarchical clustering; loading cycles (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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