Navis has added a new Hull Monitor module to its fleet performance management software Bluetracker, to be used to track degradation in hull performance resulting from self-polishing, fouling or damage.
The software uses speed loss calculations and visualisations to monitor changes, taking into account corrections required by other influencing factors such as weather, in accordance with ISO 19030.
ISO 19030 is a new industry standard developed by shipping companies, paint and propeller manufacturers, and data analysts, which describes appropriate methods for measuring changes in hull and propeller performance.
The Bluetracker Hull Monitor offer access to that data during ongoing ship operations, which can be used to determine the optimum time for maintenance on the underwater hull.
Ship managers can also be informed via an automatic notification function if the system notices sudden, exceptionally high speed losses or changes to the hull outside of defined limits, for example in case of damage due to grounding. The KPI calculations include drill-down functionality all the way to the raw data details.
Maintenance measures that are performed on the underwater hull, such as periodic renewal of the paint during docking intervals, hull cleaning, coatings or modifications, can additionally be analysed by the software, through comparative visualisation between the regressive states after the measures and the actual state.
“The IMO Ship Energy Efficiency Management SEEMP has recognised the importance of hull performance, but did not specify how to use this potential,” said Guenter Schmidmeir, general manager EMEA at Navis.
“Thanks to the new approach of Bluetracker Hull Monitor, ship owners and managers can use the collection of the hull’s lifetime data to monitor the adaptive regression and define suitable hull maintenance events exactly when maintenance is needed.”
“They also can verify the performance effect of the taken measurements, e.g. a new coating or hull cleaning. As a data specialist the software module includes plausibility checks and automatic notification if the data deviates from the defined standard to ensure a reliable data quality.”