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

Consultinga
Incidenteel verzorgen van advies en cursussen
Nederlandse Defensie Academie
Professor Life Cycle Management

Mostafa, N. , Di Maio, D. , Loendersloot, R. , & Tinga, T. (2022). Railway bridge damage detection based on extraction of instantaneous frequency by Wavelet Synchrosqueezed Transform. Advances in Bridge Engineering, 3, [12]. https://doi.org/10.1186/s43251-022-00063-0

Habtour, E. , Di Maio, D. , Masmeijer, T. , Gonzalez, L. C. , & Tinga, T. (2022). Highly Sensitive Nonlinear Identification to Track Early Fatigue Signs in Flexible Structures. Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems, 5(2), [021005]. https://doi.org/10.1115/1.4052420

Tiddens, W. W. , Braaksma, J. , & Tinga, T. (2022). Framework for predictive maintenance method selection. (pp. 1-17). Universiteit Twente.

Keizers, L. S. , Loendersloot, R. , & Tinga, T. (2022). Atmospheric Corrosion Prognostics Using a Particle Filter. In Proceedings of the 32nd European Safety and Reliability Conference (ESREL 2022) (pp. 1259-1266) https://rpsonline.com.sg/rps2prod/esrel22-epro/html/R22-08-170.xml

Jimenez-Roa, L. A., Heskes, T. , Tinga, T. , Molegraaf, H. J. A. , & Stoelinga, M. I. A. (2022). Deterioration modeling of sewer pipes via discrete-time Markov chains: A large-scale case study in the Netherlands. In Proceedings of the 32nd European Safety and Reliability Conference (ESREL 2022) (pp. 1299-1306) https://doi.org/10.3850/978-981-18-5183-4_R22-13-482-cd

Gergely, A. (2022). Development of Sensor Technology & Maintenance Concepts for Corrosion-Related Maintenance. University of Twente.

Tiddens, W. W. , Braaksma, J. , & Tinga, T. (2022). Exploring predictive maintenance applications in industry. Journal of quality in maintenance engineering, 28(1), 68-85. https://doi.org/10.1108/JQME-05-2020-0029

Keizers, L. S. , Loendersloot, R. , & Tinga, T. (2021). Unscented Kalman Filtering for Prognostics Under Varying Operational and Environmental Conditions. International Journal of Prognostics and Health Management, 12(2), 1-20. https://doi.org/10.36001/ijphm.2021.v12i2.2943

Rijsdijk, C., van der Sluis, W. , & Tinga, T. (2021). Autonomous fault detection and diagnostics, an enabler to control risks of military operations. In Proceedings of the 58th ESReDA Seminar: Using Knowledge to Manage Risks and Threats: Practices and Challenges European Safety and Reliability Association.

Tinga, T. (2021). Predicting critical failures using physics of failures: opportunities and challenges. In AVT-356 Research Symposium on Physics of Failure for Military Platform Critical Subsystems (Vol. AVT-356, pp. 1-13). [13] NATO Science & Technology Organization.

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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.