Theme: Personalised Learning (Gepersonaliseerd Leren) Adaptive learning is a powerful tool in a multidisciplinary learning environment, in which students with a diverse background and foreknowledge and with different levels (MSc and post-MSc) work together. In essence, students can follow a learning route, tailored to their individual needs and interest. Without support of e-learning concepts and online tools, this method is however very time consuming and ineffective for the lecturer. A new concept is proposed here for the development of master courses with a diverse influx and a multidisciplinary content. The concept is based on a plenary core set of lectures embedded in a flexible shell of adaptive e-learning modules. The concept will be relying on a web-based application to guide students through the network of e-learning modules, follow their progress and allow feedback to the students. The concept will be tested in two courses of the MSc track Maintenance Engineering and Operations (MEO), which is characterised by a multidisciplinary content and student population. MEO is not part of a single department, like other MSc tracks, but is supported by four departments, and two faculties. The track courses are also followed by students from other tracks and PDEng and PhD students.

XCTE technologies centre on integrating composites durability characterization and dynamic response analysis to enhance system resilience. Understanding material behaviour and their response are critical paths to develop stronger, resilient and lightweight structures. Material durability is evaluated through high-frequency fatigue testing, a breakthrough method that significantly reduces testing time. This rapid assessment is achieved by leveraging a physical principle that amplifies microscopic fatigue damage at its earliest stages. By revealing the relationship between damage and dynamic behaviour, we expand the design space beyond traditional zero-damage tolerance criteria, unlocking new possibilities for optimized and resilient systems.

No more system malfunctions? The ZORRO project is working on diagnostic methods for high-tech systems, such as MRI scanners and printers. By continuously monitoring their behaviour with suitable sensors, algorithms from AI can detect anomalous patterns and relate these to their root causes. Suitable measures, such as replacements or repairs, can then prevent failures. We aim at breakthroughs in complexity with ZORRO: not diagnostics for simple components, but for entire systems; efficient monitoring systems that combine different sensor types; automation of diagnostic processes by capturing domain knowledge in diagnostic models and integrate these into the engineering process for high-tech systems.

The Dutch government plans 70GW of offshore wind energy by 2050. Achieving this target will only be possible if the pace of installation is accelerated and if technological solutions that embrace the three transitions (energy, food, nature) in the North Sea are developed. HybridLabs aims at accelerating the deployment of offshore renewables by addressing key questions related to the floating installation of offshore renewables, and developing groundbreaking innovations in the monitoring, control, and design of offshore renewables and their logistics. These innovations will be unlocked through nation-wide infrastructure of experimental facilities, simulators, and offshore field labs, learning from one another.

To reduce the down time and associated costs in a production facility, the predictability of failures is very important. This can be achieved when firstly the relevant parameters on operational conditions are monitored and secondly that data is properly processed to obtain accurate estimates of time to failure. In this project, the first challenge will be addressed by developing advanced wireless sensor networks, that enable the collection of the right data in a flexible way. The second challenge will be addressed by the development of physical failure models for the most critical components in the system. By feeding the models with the monitored variation in operational settings, the time to failure can be predicted and appropriate maintenance tasks can be scheduled. For less critical components a more data-driven approach will be followed, resulting in a decision support tool enabling optimization of the maintenance process and maximizing plant uptime.