Fujitsu Laboratories Ltd. announced the development of technology that uses analysis of ship-related big data to estimate fuel efficiency, speed and other performance in actual sea conditions, to a highly accurate margin of error of less than 5%.
This newly developed technology puts to work Fujitsu Laboratories’ propriety high-dimensional statistical analysis technology to estimate the performance of ships actually at sea. The technology utilizes a massive volume of measurement data gathered while the ship is underway, including sensor data of meteorological and hydrographic conditions such as wind, waves, and ocean currents, ship engine log data, and data about the speed and position of the ship.
By applying the results of this research to Tokyo University of Marine Science and Technology’s weather routing simulator for evaluation, Fujitsu Laboratories demonstrated it could improve fuel efficiency by about 5% from previous results, with ships that navigate the shortest shipping routes.
With this technology, it is possible to accurately estimate a ship’s performance in actual sea conditions, which previously had a large margin of error, enabling evaluation of ship performance, design feedback, and significant improvements in fuel efficiency when used in ship navigation.
This technology uses Fujitsu’s AI technology, Human Centric AI Zinrai, and Fujitsu Laboratories will continue to improve its estimation accuracy through further operational trials going forward.
Key features of the technology
1. Technology that does analysis using data as is from actual travel
Fujitsu Laboratories’ propriety high-dimensional statistical analysis technology allowed measurement data obtained from ships underway to be used as is, for the successful analysis of the influence of a variety of simultaneously integrated factors, such as meteorological and hydrographic conditions. This enables performance estimates that incorporate the complex interaction of conditions, including wind, waves, and ocean currents, based not on synthesized data from experiments in tanks of water, but on data gathered as-is from actual ships at sea.
2. Technology that automatically groups measurement data, and adjusts the degree of machine learning for each group
With physics models, because physical phenomena, such as the strength of the wind, for example, have to be expressed uniformly in a simplified model, it was impossible to raise the level of estimate accuracy (Figure 1a). With this technology, the high-dimensional data, which incorporates a variety of measurement data, is automatically grouped by similar meteorological and hydrographic conditions, and then machine learning and estimation are carried out on each group individually (Figure 1b).
Overly prioritizing actual measured data for machine learning can create a problem where the estimation accuracy goes down for conditions which have not been experienced and there is no measurement data. This problem is solved by automatically adjusting the group boundaries so that no group has data that matches measurement data too closely. This enabled a uniform reduction in prediction error.
Results
Carrying out joint research with Tokyo University of Marine Science and Technology, Fujitsu Laboratories applied this technology to measured data held by the university from actual ships at sea, including wind and wave data, and the ship’s fuel consumption, and successfully and accurately estimated the ship’s speed performance and fuel consumption performance to within a 5% margin of error. By combining this technology with the Tokyo University of Marine Science and Technology’s weather routing simulation, they verified that, for a Pacific Ocean shipping route from Tokyo to Los Angeles, by taking an optimal route based on the ship’s performance, as determined by this technology, as opposed to the most direct route, fuel consumption could be cut by about 5%, greatly reducing both fuel costs and CO2 emissions.
Feeding back data from voyages by previously developed ships into the ship design process, this technology can enable the design of safe ships with high fuel efficiency. In addition, changes in ship performance before and after maintenance and also before and after applying various fuel-efficient technologies can be quantitatively evaluated.
Source & Image Credit: Fujitsu
