Asset & Maintenance Engineering

Among the cornerstones of a society in transition are reliable, available, and cost-effective technical systems. By controlling functional performance, asset & maintenance engineers can support operational systems against uncertainty, fostering both a resilient and a circular use of precious resources.

We focus our asset & maintenance engineering work at the Faculty of Engineering Technology on capital-intensive infrastructures. The work includes predictive maintenance strategies for large assets, process industry, and infrastructure in a circular economy. We encourage a multidisciplinary approach to maintenance engineering, delving into the physics of failure and condition monitoring to data analysis, maintenance process optimisation, and logistical challenges. All of this is set in a double context of education and research.

Collaboration is fundamental: we partner with UT’s Asset Management and Maintenance Innovation Centre and the ‘Asset Management and Maintenance’ department of the Netherlands’ Royal Institute of Engineers, KIVI, alongside other faculties and national and international universities. Our research groups are aligned with UT’s drive towards a sustainable society, prioritising several of the United Nations’ Sustainable Development Goals (SDGs), including SDG 9, ‘Industry, Innovation, and Infrastructure’.

  • Research facilities

    Across the ET faculty, many groups come together to address this research theme, setting up joint research projects. Experiments are performed in the Department of Mechanics of Solids, Surfaces & Systems labs. The creation of the Lifetime Performance Laboratory supports this research, alongside that of other research groups and industrial partners.

  • Example projects

    1.       NWA PrimaVera: predictive maintenance solutions for high-tech systems  
    Predictive maintenance offers new solutions for high-tech system maintenance. Integrating just-in-time maintenance remains challenging, and the PrimaVera project lays the foundations for better asset performance, lower cost, and autonomous maintenance. It does this by exploring: 

    • Novel combinations of model- and data-driven failure prediction techniques
    • Multi-scale optimisation techniques
    • An integrated approach to health predictions and maintenance optimisation

    2.       MX3D bridge: testing, monitoring, and maintaining the first 3D-printed metal bridge
    The first 3D-printed stainless-steel bridge, the MX3D bridge in Amsterdam, required a new level of monitoring. After load tests at the UT, our Construction Management and Engineering team, together with partners like Autodesk and The Alan Turing Institute, installed a sensor network for continuous monitoring. This boosts our understanding of intelligent algorithms, design, and maintenance communications.

     

    3.       BURWEAR: a lifetime prediction model for steel-producing rolls

    In steel production, timely roll replacement is vital. Together with Tata Steel, the Burwear project is developing a lifetime prediction model for the backup rolls. In backup rolls, two degradation mechanisms interact: wear and rolling contact fatigue. In this project, degradation phenomena will be modelled, experimentally validated, and utilized to optimize roll use.

Theme leader: 

dr.ir. R. Loendersloot (Richard)
Associate Professor