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.
Expertise
Physics
- Frequencies
- Detection
- Model
- Utilization
- Maintenance
- Impact
- Vibration
Engineering
- Predictive Maintenance
Organisations
Ancillary activities
- Nederlandse Defensie AcademieProfessor Life Cycle Management
- NLR (Netherlands Aerospace Center)Adviescommissie Aerospace Vehicles NLR
- ConsultingaIncidenteel verzorgen van lezingen en/of advies
Publications
2024
Corrosion classification through deep learning of electrochemical noise time-frequency transient information, Article 108044, 1-9. Homborg, A. M., Mol, J. M. C. & Tinga, T.https://doi.org/10.1016/j.engappai.2024.108044Quantifying the suitability and feasibility of predictive maintenance approaches, Article 110342, 1-13 (E-pub ahead of print/First online). Alves da Silveira, N. N., Meghoe, A. A. & Tinga, T.https://doi.org/10.1016/j.cie.2024.110342Impact Identification Method for Structural Health Monitoring of Stiffened Composite Panels using Passive Sensing Systems. Marinho, N. R., Loendersloot, R., Grooteman, F., Wiegman, J. W. & Tinga, T.https://doi.org/10.58286/29655A maturity framework for data driven maintenanceIn PHM Society European Conference, Article 4039 (pp. 1-11). PHM Society. Rijsdijk, C., van de Wijnckel, M. J. R. & Tinga, T.https://doi.org/10.36001/phme.2024.v8i1.4039Towards a Hybrid Framework For Prognostics With Limited Run-to-Failure DataIn Proceedings of the 8th European Conference of the Prognostics and Health Management Society 2024 (pp. 844-855). PHM Society. Keizers, L. S., Loendersloot, R. & Tinga, T.https://doi.org/10.36001/phme.2024.v8i1.4017Maintenance Strategies for Sewer Pipes with Multi-State Degradation and Deep Reinforcement LearningIn Proceedings of the 8th European Conference of the PHM Society 2024 (pp. 629-642). Jimenez, L., Simão, T. D., Bukhsh, Z., Tinga, T., Molegraaf, H., Jansen, N. & Stoelinga, M. I. A.https://doi.org/10.36001/phme.2024.v8i1.4091Comparing Homogeneous And Inhomogeneous Time Markov Chains For Modelling Degradation In Sewer Pipe NetworksIn European Safety and Reliability Conference (ESREL), Article 9 (pp. 86-96). Polish Safety and Reliability Association. Jimenez, L., Tinga, T., Heskes, T. & Stoelinga, M. I. A.In-situ mechanical and microstructural characterization of miniaturized Al-Mg-Sc-Zr and AlSi10Mg specimens processed by laser powder-bed fusion (PBF-LB), 348-359. Cordova, L., Bor, T., Macía Rodríguez, E., Tinga, T. & Campos, M.https://doi.org/10.1016/j.jmrt.2024.03.084Utilizing the system instantaneous frequency for the structural health monitoring of bridges. University of Twente. Mostafa, N.https://doi.org/10.3990/1.9789036560382Motor Current and Vibration Monitoring Dataset for various Faults in an E-motor-driven Centrifugal Pump, Article 109987. Bruinsma, S., Geertsma, R., Loendersloot, R. & Tinga, T.https://doi.org/10.1016/j.dib.2023.109987
Research profiles
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.
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