Tiedo Tinga

Tiedo Tinga is a Full Professor at the Netherlands Defence Academy and a leading expert in Dynamics-Based Maintenance. With over 175 research outputs and an h-index of 30, his work focuses on predictive maintenance, fault diagnostics, and prognostics in mechanical systems.

• Research Highlights: Bayesian filtering, sensor performance evaluation, composite material diagnostics, and smart maintenance systems.
• Scientific Recognition: Recipient of the 2015 Maintenance Awareness Award.
• Recent Contributions: Advanced methods for impact identification in composites, integration of FMEA/FTA in fault diagnosis, and applications of transfer learning for marine propulsion systems.

His research combines physics-based models with data-driven approaches to solve practical maintenance challenges in defense and industrial applications.

Recent Publications (2025-2023): Application of transfer learning for shaft power predictions, Bayesian filtering frameworks, bond graph diagnostics, and sensor performance comparisons in composite structures.

Scientific Awards

• Maintenance Awareness Award (2015)

Professional Activities

• Invited talks at conferences (2023-2025) on predictive maintenance, smart systems, and data-driven maintenance challenges.
• Collaborations on motor vibration monitoring datasets and defense technology projects.