Eindhoven, North Brabant, Netherlands
Conducted research on the transition from operating hour-based preventive maintenance with fixed maintenance intervals, to an operating hour integrated predictive/condition-based maintenance approach. The research was conducted on the main diesel engines of the OPV class vessels of the navy. By developing a condition monitoring approach through the use of machine learning, I proved and quantified efficiency losses of the diesel engines over time.
Doing research in the field of component obsolescence. Component obsolescence is an increasing problem in our fast evolving technological world. Capital asset owners face challenges regarding the procurement of spare parts of their long lasting capital goods. In this research, I try to proof the effectiveness of current machine learning algorithms in forecasting procurement life in the spare part industry. The estimate for procurement life is an input for minimising the total cost of ownership through strategic obsolescence management. I conducted this research at the Boston University MET Decision Sciences Research Lab. At this research lab, MET students are given the opportunity to collaborate with professors and industrial practitioners to apply their analytic skills on current industrial challenges. Learn more: https://sites.bu.edu/met-dslab/
Mainly tasks of administrative nature and a few projects. For example interviewing faculty administrations and mapping processes in order to determine the best way of working. End goal of the project was a manifest that includes process flow charts and work descriptions for all tasks.