Abstract An increasing number of ships are being equipped with sensors and devices for monitoring of operational behavior, and the amount and access to operational data is gradually increasing. Due to various reasons described in this paper, the operational data may contain erroneous data points that are critical to assess prior to performing data analysis or building mathematical and statistical models. In this paper, a stepwise method for preparation of data for ship operation and performance analysis is presented. The method deals with removing jumps in the time series data, including loss of time synchronization between different measurement subsystems, outlier detection, including repeated samples, dropouts and spikes and data selection and extraction, including stationarity detection. The final result is a data set free from disturbances, distortions and undesired physical effects, that can be used to improve the quality of a ship operation and performance analysis.

中文翻译：

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为船舶操作和性能分析准备在役测量数据

摘要 越来越多的船舶配备了用于监控操作行为的传感器和设备，操作数据的数量和访问量逐渐增加。由于本文所述的各种原因，运营数据可能包含错误的数据点，这些数据点对于在执行数据分析或构建数学和统计模型之前进行评估至关重要。在本文中，提出了一种为船舶操作和性能分析准备数据的逐步方法。该方法处理消除时间序列数据中的跳跃，包括不同测量子系统之间的时间同步丢失，异常值检测，包括重复样本、丢失和尖峰以及数据选择和提取，包括平稳性检测。最终结果是一个没有干扰的数据集，