Tiedo Tinga (1973) is a professor in Dynamics based Maintenance at the faculty of Engineering Technology, with a background in Materials Science and Mechanical Engineering. His research focuses on the detection and prediction of failures in systems, with the aim of developing smart maintenance concepts, like Predictive Maintenance. The is achieved by combining the Physics of Failure, thorough understanding of the (dynamic) system behaviour and advanced (condition) monitoring techniques, as well as data science and artificial intelligence.
Engineering & Materials Science
# Corrosion # Fatigue Damage # Health # Monitoring # Predictive Maintenance # Structural Health Monitoring
Physics & Astronomy
Incidenteel verzorgen van advies en cursussen
Nederlandse Defensie Academie
Professor Life Cycle Management
NLR (Netherlands Aerospace Center)
Adviescommissie Aerospace Vehicles NLR
Ribeiro Marinho, N. , Di Maio, D. , Loendersloot, R. , & Tinga, T. (2023). Exploiting high frequency waves for SHM using 3D SLDV system: what can we learn more? In Measuring by Light 2023
Ribeiro Marinho, N. , Loendersloot, R. , Tinga, T., Grooteman, F., & Wiegman, J. W. (2023). A Comparison of Optical Sensing Systems with Piezo-Electric Sensors for Impact Identification of Composite Plates. In S. Farhangdoust, A. Guemes, & F-K. Chang (Eds.), Proceedings of the 14th International Workshop on Structural Health Monitoring 2023: Designing SHM for Sustainability, Maintainability, and Reliability (pp. 1127-1133). DEStech Publications, Inc.
Alves da Silveira, N. N. , Meghoe, A. A. , & Tinga, T. (2023). Integration of multiple failure mechanisms in a life assessment method for centrifugal pump impellers. Advances in mechanical engineering, 15(6). https://doi.org/10.1177/16878132231175755
Tinga, T., Homborg, A. M. , & Rijsdijk, C. (2023). Data-driven maintenance of military systems: Potential and challenges. In P. B. M. J. Pijpers, M. Voskuijl, & R. Beeres (Eds.), Towards a data-driven military: A multidisciplinary perspective (pp. 73-96). Leiden University Press. https://doi.org/10.24415/9789087284084
Meghoe, A. A. , Loendersloot, R. , & Tinga, T. (2023). Validation of a physics-based prognostic model with incomplete data: a rail wear case study. International Journal of Prognostics and Health Management, 14(1), 1-16. Article 3283. https://doi.org/10.36001/ijphm.2023.v14i1.3283
Tiddens, W. W. , Braaksma, J. , & Tinga, T. (2023). Decision Framework for Predictive Maintenance Method Selection. Applied Sciences, 13(3), Article 2021. https://doi.org/10.3390/app13032021
Jimenez-Roa, L. A., Heskes, T. , Tinga, T. , & Stoelinga, M. I. A. (2023). Automatic inference of fault tree models via multi-objective evolutionary algorithms. IEEE transactions on dependable and secure computing, 20(4), 3317-3327. Advance online publication. https://doi.org/10.1109/TDSC.2022.3203805
Meghoe, A. A. , Loendersloot, R. , & Tinga, T. (2022). Uncertainty propagation in rail wear prediction using an analytical method and field observations. Paper presented at Fifth international conference on railway technology, Montpellier, France.
Ribeiro Marinho, N. R. M. , Loendersloot, R. , Tinga, T., & Grooteman, F. (2022). Impact identification method for composite structures: A structured approach for a full-scale aircraft component. Poster session presented at 25th Engineering Mechanics Symposium, EM 2022, Arnhem, Netherlands.
UT Research Information System
Courses Academic Year 2023/2024
Courses in the current academic year are added at the moment they are finalised in the Osiris system. Therefore it is possible that the list is not yet complete for the whole academic year.