Digital twins: monitoring ships’ state in real-time

Advanced data science to assist the design of cleaner, safer and smarter ships

Advanced data science to assist the design of cleaner, safer and smarter ships

In 2018 the Netherlands eScience Center (NLeSC) successfully conducted a consultancy service for MARIN. During the consulting project, we established the foundations for Optimized data handling for the CoVadem project datasets, we designed a proposal for Optimized data handling and we did some initial piloting on the data handling for the MARIN DataScience & Digital Twin R&D project.

  •  CoVadem (https://www.covadem.org/ ) is a Big Data initiative for which large amounts of sensor data are collected from more than 70 inland vessels traversing European inland waterways. As stakeholder of this initiative, MARIN has access to this anonymous measurement dataset for internal research, stored in a Cassandra cluster running at Amazon. While CoVadem mainly focus on the creation of near real time water depth charts of the inland waterways in Europe, MARIN uses this data for in-house R&D on performance profiling. Such profile is a source of information for elaborated and customized feedback on route efficiency of ships. Furthermore, it will enable route prediction via interpolation for ships with low or without monitoring service. 
  • As part of the MARIN DataScience & Digital Twin R&D project MARIN aims to collect large amounts of sensor data offshore, i.e., located at the high ocean, such as ships and floating production, storage and offloading units (FPSO’s). The data is collected and stored on-board for local analysis, i.e., signal processing, data calibration and statistics collection. The sensor data can include high resolution cameras and audio recording devices. All data combined is used to monitor the state of the ship and optimise operations. Examples are on-board simulations to predict efficient routes, forecast fuel consumption, and estimate more precise time of arrival crucial for the tied schedules of containers ships. Or the ambition to combine sensor data with simulations to enable near real-time prediction of operational conditions and expected progression, to increase uptime and safety in offshore operations, such as “walk-to-work” operations during wind turbine maintenance.
Senior eScience Research Engineer Stefan Verhoeven, BSc

Stefan is specialized in software development for life science projects.

Profile page
Senior eScience Research Engineer Dr. Vincent van Hees

Vincent is specialized in analytical methods for life science projects.

Profile page
eScience Research Engineer Dr. Meiert Grootes

Profile page
eScience Research Engineer Dr. Florian Huber

Profile page

Stay up to date, sign up for our newsletter