| KEYWORD |
20- ISED: Industrial Systems Engineering and Design
Development of predictive maintenance models for rolling bearings using digital twins and Artificial Intelligence (AI)
keywords ARTIFICIAL INTELLIGENCE, BEARINGS
Reference persons CRISTIANA DELPRETE
External reference persons Ing. Luigi DI MAGGIO, Assegnista di Ricerca DIMEAS
Research Groups 20- ISED: Industrial Systems Engineering and Design
Description The thesis work aims to develop a Simscape/Simulink simulation model to analyze the mechanical interactions in a spherical roller bearing. The model will be calibrated using experimental data to build an accurate Digital Twin of the bearing system. Subsequently, the model will be used to conduct scenario analyzes under component damage conditions caused by overload stresses or wear. The simulations will produce a dataset of accelerations aimed at training a machine learning/deep learning model for the diagnosis of potential system damage and the implementation of predictive maintenance strategies. Finally, the trained model will be subjected to testing and diagnostic validation using a set of experimental data obtained from actually damaged components.
Required skills Basic knowledge of: Matlab/Simulink environment, CAD, Python, signal analysis
Notes Durata: 6 mesi
Deadline 04/05/2025
PROPONI LA TUA CANDIDATURA