Application of physical failure models to enable usage and load based maintenance. - Tiedo Tinga (Reliab. Eng. Syst. Saf., 2010)

Prognostic Enhancements on Health and Usage Monitoring Systems. - Nick A. Heerink; Richard Curran; Tiedo Tinga; Adel A. Ghobbar (CE, 2012)

Towards a Usage Driven Maintenance Concept: Improving Maintenance Value. - Tom Stuivenberg; Adel A. Ghobbar; Tiedo Tinga; Richard Curran (CE, 2012)

Optimum maintenance strategy under uncertainty in the lifetime distribution. - Bram de Jonge; Warse Klingenberg; Ruud H. Teunter; Tiedo Tinga (Reliab. Eng. Syst. Saf., 2015)

Reducing costs by clustering maintenance activities for multiple critical units. - Bram de Jonge; Warse Klingenberg; Ruud H. Teunter; Tiedo Tinga (Reliab. Eng. Syst. Saf., 2016)

The influence of practical factors on the benefits of condition-based maintenance over time-based maintenance. - Bram de Jonge; Ruud H. Teunter; Tiedo Tinga (Reliab. Eng. Syst. Saf., 2017)

Improving failure analysis efficiency by combining FTA and FMEA in a recursive manner. - J. F. W. Peeters; Rob J. I. Basten; Tiedo Tinga (Reliab. Eng. Syst. Saf., 2018)

Automatic Inference of Fault Tree Models Via Multi-Objective Evolutionary Algorithms. - Lisandro Arturo Jimenez-Roa; Tom Heskes; Tiedo Tinga; Mariëlle Stoelinga (IEEE Trans. Dependable Secur. Comput., 2023)

Quantifying the suitability and feasibility of predictive maintenance approaches. - N. N. A. Silveira; A. A. Meghoe; Tiedo Tinga (Comput. Ind. Eng., 2024)

Maintenance Strategies for Sewer Pipes with Multi-State Degradation and Deep Reinforcement Learning. - Lisandro Arturo Jimenez-Roa; Thiago D. Simão; Zaharah Allah Bukhsh; Tiedo Tinga; Hajo Molegraaf; Nils Jansen 001; Mariëlle Stoelinga (CoRR, 2024)

A maturity framework for data driven maintenance. - Chris Rijsdijk; Mike van de Wijnckel; Tiedo Tinga (CoRR, 2024)

Corrosion classification through deep learning of electrochemical noise time-frequency transient information. - Axel Homborg; Arjan Mol; Tiedo Tinga (Eng. Appl. Artif. Intell., 2024)

InfoPos: A ML-Assisted Solution Design Support Framework for Industrial Cyber-Physical Systems. - Uraz Odyurt; Richard Loendersloot; Tiedo Tinga (CoRR, 2025)