# Assessment of Hull and Propeller Degradation Due to Biofouling Using Tree-Based Models
## 3 The Case Study and Preliminary Assessment
### Definition of Maintenance Actions and Respective Datasets  
Due to fouling accumulation, vessel hydrodynamic performance may differ drastically at different points in the operation timeline. To assess the changes in vessel performance over time, it is necessary to know the dates of the various maintenance events that took place during the reporting period. In Figure 4, the vessel's operation timeline is shown, containing the date of each important event. The dataset is split, based on maintenance events, into four distinct datasets (A, B, C, D), as described in Table 3. During Dataset A, the propeller was damaged, according to the operator, so the performance was significantly deteriorated and non-representative of the vessel under normal conditions. Dataset B represents a period during which marine growth accumulated. Due to the short duration between cleaning events, Dataset C captures a limited period where fouling build-up was not substantial. Dataset D, spanning over a year, contains the most appropriate data for studying fouling accumulation due to its extended timeframe. Datasets D1 and D2 are subsets of Dataset D: the former spans the first 2.5 months of D, the latter spans the last 3 months. Table 3 also contains the percentage of time during each period the ship remained at port obtained by the noon report data. Figure 4: Timeline of the most important events during the vessel’s operation. Figure 3: Projection of the ship’s path on the world map during the recording period, utilizing high-frequency GPS signals. As a first approach to assess the impact of hull and propeller fouling on ship performance, a typical KPI was utilized to examine its change over time. The KPI employed in this study was a customary propeller loading coefficient, defined as follows (e.g., [25]):  
\[\text{KPI}=\text{P}/\text{N}^{3} \tag{1}\]  
where P is the ME power in kW and N is the propeller rate of revolution in RPM. This KPI reflects the propeller's loading condition. When the hull and propeller become fouled, the increased resistance leads to a higher thrust requirement, resulting in a rise in the ratio as the power must increase to overcome the additional drag, while the rotational speed may not change significantly to maintain the desired ship speed. Therefore, plotting the KPI over time provides a visual indication of the changes in propeller loading due to the accumulation of fouling on the hull and propeller. In Figure 5, the KPI is plotted throughout the reporting period using separate colors for laden (green) and ballast (blue) conditions, demonstrating that as the draft increases, the KPI increases as well. The maintenance event dates are also shown in the plot, based on which the dataset is split into individual periods. Due to the high variance caused by transients and various loading and weather conditions, it is difficult to directly interpret the variation in the KPI. Therefore, a first order trendline was fitted over the data available in each period in between two consecutive maintenance events. The uptrend observed in each period trendline accounts for the hull and propeller deterioration due to fouling. In addition, the drop in the KPI noticed after each maintenance event represents the improvement in the vessel performance achieved because of the maintenance itself. Despite being intuitive and easy to implement, this approach presents a few shortcomings. First, it fails to answer the crucial question of how much power is saved because of a maintenance event, and second, it does not account for the various factors affecting  
Figure 5: Utilization of the propeller loading coefficient (Equation (1)) to monitor the status of the hull and the propeller performance, as well as to track the main maintenance events.