Analysing operational conditions of ships presents a novel approach to assessing emission levels, motivated by the inadequacy of traditional static weighting factors, such as ISO 8178-E3 cycle, to capture the dynamic and complex operating characteristics of ships at sea. This study introduces a data-driven method to construct and validate ship typical operational conditions. The method encompasses identifying ship motion states, extracting features, compressing time series data based on these features, and performing cluster analysis. It has been applied to process over 12.6 million data points, demonstrating its applicability to a large dataset. The results indicate that by using actual measurement data and the proposed methodology, three typical operational conditions for ships were successfully established. There are significant differences in the feature parameters among these conditions, highlighting the distinct characteristics of each operational state. The validity of the constructed typical operational conditions was confirmed through a validation process, which involved analysing the differences in feature parameters and comparing the probability distributions of speed and acceleration to the overall dataset. Additionally, energy consumption and emission levels calculated using the typical conditions were validated through comparison with real-world data from upstream and downstream voyages. This study providing a novel tool for assessing emissions in the maritime industry.

中文翻译：

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数据驱动的船舶典型运营条件：评估船舶排放的基准工具


分析船舶的运行条件提供了一种评估排放水平的新方法，其动机是传统的静态加权因子（如 ISO 8178-E3 循环）不足，无法捕捉船舶在海上的动态和复杂运行特性。本研究介绍了一种数据驱动的方法来构建和验证船舶的典型运行条件。该方法包括识别船舶运动状态、提取特征、根据这些特征压缩时间序列数据以及执行聚类分析。它已被应用于处理超过 1260 万个数据点，证明了它对大型数据集的适用性。结果表明，通过使用实际测量数据和所提出的方法，成功建立了船舶的 3 种典型运行条件。这些条件之间的特征参数存在显著差异，突出了每种运行状态的不同特征。通过验证过程确认了构建的典型操作条件的有效性，该过程涉及分析特征参数的差异并将速度和加速度的概率分布与整个数据集进行比较。此外，通过与上游和下游航行的真实数据进行比较，验证了使用典型条件计算的能耗和排放水平。这项研究为评估海运业的排放提供了一种新颖的工具。