prof.dr.ir. T. Tinga (Tiedo)

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

Chemistry

# Abrasive

Physics & Astronomy

# Abrasives

Nederlandse Defensie Academie
Professor Life Cycle Management
NLR (Netherlands Aerospace Center)
Adviescommissie Aerospace Vehicles NLR
Consultinga
Incidenteel verzorgen van lezingen en/of advies

Mostafa, N. (2024). Utilizing the system instantaneous frequency for the structural health monitoring of bridges. [PhD Thesis - Research UT, graduation UT, University of Twente]. University of Twente. https://doi.org/10.3990/1.9789036560382

Cordova, L. , Bor, T., Macía Rodríguez, E. , Tinga, T., & Campos, M. (2024). In-situ mechanical and microstructural characterization of miniaturized Al-Mg-Sc-Zr and AlSi10Mg specimens processed by laser powder-bed fusion (PBF-LB). Journal of Materials Research and Technology, 30, 348-359. Advance online publication. https://doi.org/10.1016/j.jmrt.2024.03.084

Homborg, A. M., Mol, J. M. C. , & Tinga, T. (2024). Corrosion classification through deep learning of electrochemical noise time-frequency transient information. Engineering applications of artificial intelligence, 133, 1-9. Article 108044. Advance online publication. https://doi.org/10.1016/j.engappai.2024.108044

Bruinsma, S., Geertsma, R. , Loendersloot, R. , & Tinga, T. (2024). Motor Current and Vibration Monitoring Dataset for various Faults in an E-motor-driven Centrifugal Pump. Data in brief, 52, Article 109987. https://doi.org/10.1016/j.dib.2023.109987

Meghoe, A. A. , Loendersloot, R. , & Tinga, T. (2023). Selection of a suitable wear model for implementation in a generic rail damage function. 1-16. Paper presented at Railway Engineering 2023, Edinburgh, United Kingdom.

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

Mostafa, N. , Di Maio, D. , Loendersloot, R. , & Tinga, T. (2023). The influence of vehicle dynamics on the time-dependent resonances of a bridge. Advances in Bridge Engineering, 4(22), Article 22. Advance online publication. https://doi.org/10.1186/s43251-023-00102-4

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

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