Fleet Software Digitizes Hull Maintenance

Jul 2017

Navis has invented a new module called Hull Monitor to track the underwater hull performance of all vessel types and aid fleets in reaching ports on-time while sticking to a specific fuel consumption.

The system designer created Hull Monitor to track ship-damaging factors such as self-polishing and fouling as part of its fleet performance management software Bluetracker. 

Speed loss calculations and visualizations allow ship managers and owners to keep up-to-date with underwater hull maintenance.

An automatic notification function also allows users to react quickly to intense changes to the hull, such as in the case of damage due to grounding, saving on extra expenditures for fuel.

Hull Monitor also tracks the effects of maintenance measures on the underwater hull – such as periodic renewal of the paint during docking intervals, hull cleaning, coatings and modification of the vessel's hull.

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These factors are analyzed by comparative visualization to show the benefits of intended measures.

Hull Monitor has been designed to comply with the new ISO 19030 standard for changes in hull and propeller performance.

ISO 19030 was developed by shipping companies, paint and propeller manufacturers, data analysts and scientists for outlining the appropriate methods for measuring changes in the hull and the propeller performance.

Guenter Schmidmeir, General Manager EMEA at Navis, said: “The IMO Ship Energy Efficiency Management SEEMP has recognized the importance of hull performance, but did not specify how to use this potential.

“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.”