A Practical Identification Method for Data Variation of Noon Reports in Vessel Operation

The data in noon reports on a daily basis from vessel operations reflect the variation of operational behaviors. In our previous research, a practical assessment method has been proposed to diagnose the quality of individual sample through validation rules. The findings have been implemented and as a consequence, the quality of data has been improved to some extent based on triggered warnings and alarms. However, the unstable periods of data, defined as the spans with large variations that are different from expected values, and their root causes are still hard to be identified by the method, which limits further improvement of vessel operations. In the present research, the so-called Sliding CV method, is proposed to provide diagnosis on the unstable periods of noon reports in vessel operations accounting for multiple samples. This method utilizes historical CV (coefficient of variation) values of the performance indicators (e.g., Specific fuel oil consumption (SFOC)) and a sliding window with a fixed width and step for the identification of current unstable periods. Sensitivity study has been conducted for the selection of widths and steps of sliding windows. The unstable periods can be identified when the CV values exceed a given threshold. The effects of short voyages and long voyages have been taken into account in this method. Case studies have been done based on the datasets provided by shipping companies. Typical root causes of the unstable periods of vessels are identified based on data analysis, which will provide guidance on the improvement of vessel performance in practice.