识别海洋船舶运营数据中异常的方法和来源调查
Jie Cai1, Marie Lützen2, Adeline Crystal John3
1Department of Technology and Innovation, University of Southern Denmark, Campusvej 55, 5230 Odense, Denmark.
Sensors (Basel, Switzerland)
|April 13, 2024
概括
这项研究引入了一种新的方法,使用统计分析来检测船舶操作中的数据误差. 该方法有效地识别了传感器 (自动) 和手动 (午间报告) 数据中的差异,改善了运营绩效评估.
科学领域:
- 海上运营和数据分析.
- 海洋工程和船舶性能监测.
- 数据质量评估中的统计方法.
背景情况:
- 准确的船舶运营数据对于绩效评估和决策至关重要.
- 手动中午报告和自动传感器 (自动传感器) 数据是主要来源,但存在差异.
- 识别和纠正数据误差对于可靠的性能指标至关重要.
研究的目的:
- 开发和验证一种用于检测船舶运营数据中的数据误差的方法.
- 为了比较手动中午报告与自动化数据的可靠性.
- 为了提高操作准确性,识别数据偏差的来源.
主要方法:
- 统计分析比较手动中午报告和自主传感器数据.
- 基于报告和预期参数值之间的误差提出新的指标建议.
- 采用移动窗口方法与变化系数 (CV) 计算来检测功率数据的偏差.
- 关于商船数据的案例研究 (自律报告和午间报告) 超过14个船舶年.
主要成果:
- 拟议的方法有效地检测到自律和午间报告中的数据异常.
- 该方法确定了每年每艘船大约一个触发期,具有确的偏差来源.
- 分析有助于在操作场景中确定最可靠的数据源.
- 在四艘商船和14个船舶年内进行的初始测试证明了实际的有效性.
结论:
- 开发的方法提供了一个实用和有效的解决方案,用于识别船舶操作中的数据误差.
- 准确的数据分析提高了运营绩效的评估,使操作人员和机组人员受益.
- 这项研究有助于在海事行业中更可靠的数据驱动决策.
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