ID² Lab combines physics-based dynamics with data-driven AI in three research areas.
We collect vibration, load, and operational data in real time and use signal processing and deep learning (e.g., CNN) to detect faults in bearings and blades and to predict remaining useful life.
Using high-fidelity finite element models and experiments (modal testing, 3D DIC), we analyze the dynamics of wind turbine blades and towers, rotating machinery, and mechatronic systems, and design them to avoid resonance.
We build digital twins that combine physics-based dynamic models with real-time sensor and operational data to estimate the current state and loads of equipment and to predict its behavior and lifetime under varying operating conditions.
Keywords: PHM, Vibration Signal Processing, Wind Turbines, Arc Fault Detection, FEM, Digital Twin