Predictive Maintenance for Contested Airspaces – Royal Netherlands Air Force + Netherlands Defense Academy



Company Name / Department

Royal Netherlands Air Force + Netherlands Defense Academy

Contact Person

dr. ir. Bram Westerweel / ir. Mark van Someren

Location Breda / Woensdrecht
Optional remote work Limited (data analysis has to occur on site)
Travel expenses (own account or reimbursed by the company) Reimbursed by company
Housing arranged by company Possibility to stay on Airbase
Housing expenses (how much per month, own account or subsidized by the company) Subsidized by company
Internship compensation  €700-750 per month
Study program


ESCF community


Start date





Company Description

The Royal Netherlands Air Force (RNLAF) is a modern, high-tech armed forces service that contributes to peace and security on a global basis. For this purpose, it has highly-qualified personnel, aircraft, helicopters and other weapon systems at its disposal. They employ around 6500 active military. 

The Netherlands Defence Academy (NLDA) provides (maritime) military education, university-level education and personal development to aspiring officers. The NLDA is located in Breda, Den Helder and The Hague.


Project Description

Project description:

For several decades, RNLAF has only had to fight in conflicts of choice. In these conflicts, air superiority and freedom to operate was a given, but the RNLAF can no longer rely on this. It must prepare more diligently for prolonged combat operations in a contested environment with much greater uncertainty than the RNLAF has faced in the past. In such an event, the availability of all its high-tech assets, dependent in large part on effective service logistics, is crucial for success.

 The uncertainty of a conflict against a near-peer can manifest itself in many ways. In this project, we focus specifically on uncertainty in asset operating profiles.

 Uncertainty in asset operating profiles is a result of never-before encountered levels of operational intensity, as well as evolving tactical operating procedures (e.g., more low-altitude or night operations).

Goals of the project:

No historical data on such operating profiles exists, which is why this project focusses on obtaining component failure predictions through a combination of multiple data sources.

Specifically, you will investigate how, and to what extent the combination of sensor data and human judgement can yield failure predictions that are more robust in case of suddenly changing asset operating profiles.

Lastly, you will explore how these sudden changes in operating profiles impact and are impacted by maintenance and operational decisions.


    Ideally the analysis will lead to;

    • failure predictions of critical components
    • distinct operations profiles with said failure predictions
    • insight into the relationships between component degradation, maintenance strategy and operational decisions.

    Essential student knowledge:

    • Maintenance and reliability engineering knowledge
    • Programming skills
    • Data analysis skills




      More information:  


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