Merchant marine college, Shanghai Maritime University, Shanghai, China.
Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai, China.
PLoS One. 2020 Feb 21;15(2):e0229211. doi: 10.1371/journal.pone.0229211. eCollection 2020.
Early warning on the ship deficiency is crucial for enhancing maritime safety, improving maritime traffic efficiency, reducing ship fuel consumption, etc. Previous studies focused on the ship deficiency exploration by mining the relationships between the ship physical deficiencies and the port state control (PSC) inspection results with statistical models. Less attention was paid to discovering the correlation rules among various parent ship deficiencies and subcategories. To address the issue, we proposed an improved Apriori model to explore the intrinsic mutual correlations among the ship deficiencies from the PSC inspection dataset. Four typical ship property indicators (i.e., ship type, age, deadweight and gross tonnage) were introduced to analyze the correlations for the ship parent deficiency categories and subcategories. The findings of our research can provide basic guidelines for PSC inspections to improve the ship inspection efficiency and maritime safety.
船舶缺陷预警对提高海上安全、提高海上交通效率、降低船舶燃料消耗等至关重要。以往的研究主要通过挖掘船舶物理缺陷与港口国监督（PSC）检查结果之间的关系，运用统计模型来探索船舶缺陷。但对于发现各种船舶缺陷的主要类别和子类之间的相关规则的关注较少。针对这一问题，我们提出了一种改进的 Apriori 模型，从 PSC 检查数据集中探索船舶缺陷之间的内在相互关系。引入了四个典型的船舶属性指标（即船型、船龄、载重吨和总吨），以分析船舶主要缺陷类别和子类之间的相关性。本研究的结果可为 PSC 检查提供基本指南，以提高船舶检查效率和海上安全。
