Damen Dredging Equipment (DDE) in Nijkerk is a subsidiary of Damen Shipyards. Being the dredging division of Damen, DDE designs and builds a wide range of dredging equipment. The company has a large range of standard dredgers and dredging-related components. Due to the extensive experience in this market (over 70 years) we can respond to very specific customer requirements. The standard products are, therefore, continuously adjusted by making use of a wide range of options in order to provide a flexible and efficient dredging device that fully meets the needs of the customer. The company has about 100 employees and a turnover of approximately €70M (2020).
The RD&I department of Damen Dredging focuses on the development of new or improved dredging concepts and equipment. A dredging vessel is equipped with a large amount of mechanical constructions, designed to cope with the harsh maritime conditions. Technical playing field is where hydrodynamics, flow technology, kinematics, structural and rotating engineering meets civil engineering and shipbuilding. RD&I translates operational questions of DDE’s clients into both practical and innovative solutions.
The objective of the proposed internship or graduation project is to propose, design and develop tooling for efficient dredging operation and maintenance based on actual operational data of the dredging vessel.
Dredging equipment is subject to harsh conditions. Exposure to maritime, saline environments, heat and vibrations is demanding for both steel structures and electrical systems. In addition, the dynamic behavior of the dredging process itself and the way the equipment is operated and maintained pose extra challenges.
The performance of e.g. a cutter or dredge pump is highly depending on the soil (mixture) that is processed. Our clients are interested in the efficiency of the process, however they often lack sufficient knowledge to interpret this.
Data science is the use of data to gain insight into processes. This can consist of approximating parameters that cannot be measured, predicting parameters, or providing insight into a trend. Machine learning and artificial intelligence are important tools to extract the static data from the data. In general, the more data (in time and quantity), the better an analysis can be. A previous study has shown that data science can also be useful for the dredging process. One might think of:
- Provide insight into the trend in pump wear based on the pump’s performance; making maintenance predictable
- Forecasting production based on other parameters other than dedicated sensors.
- Give directions to the operator to optimize the process
It will be necessary to investigate which parameters/trends we can predict with sufficient accuracy given the current sensors. And which other parameters are desirable to improve the analysis. To validate and investigate this, it is necessary that data sets from an operating CSD is available to perform tests on. We might include additional sensors and validate algorithms on board. Alternatively, the pump test circuit at the shipyard in Nijkerk is used for tests. Triton is a useful tool for generating data. (Damen Triton is a remote monitoring and control system developed by Damen that collects operational data) However, not all parameters are available yet, and are sent to Triton, are logged. It must be investigated which parameters should be added. The datasets obtained should be studied for usability for process optimization, production estimation, maintenance and consumption predictions, including proposals for application in on-board PC/PLC systems and the user interface.
An internship or graduation project typically consist of the following activities and results, but these items are depending per test objective, and the terms defined by the educational programme:
- Literature study;
- Design and run analytical models of physical processes (by C#, Python, R, Matlab,…);
- Negotiations with the supplier of components which are to be used;
- Discussing the design with stakeholders;
- Coordination and execution of tests;
- Reporting and demonstrations to stakeholders within the project;
- Taking responsibility for your own project management.
Skills & experience
- Bachelor or Master student in Business ICT, Automation or Sofware Engineering with a track in data analysis;
- Proven skills in C#, Python, R or alike;
- Autonomous, practically and result driven. Good communication skills;
- Good command of English.
What we offer
The project will be performed at the location Nijkerk. An allowance will be given for the duration of the assignment. If applicable a temporary accommodation or a compensation for housing will be provided. Flexible working hours can be discussed. Proper mentoring at HBO or academic level.