Ph.D XL

Supervisor: Alessandro Cimatti

Phone: 

Room: FBK, DSIP Unit

Mail: calza@fbk.eu, calza.davide@spes.uniud.it

Research Project

Condition monitoring and predictive maintenance of complex industrial systems: Model-based reasoning meets Data Science.

The research focuses on artificial intelligence (AI) in the context of digital industry applications, with a particular emphasis on predictive maintenance. This area of interest enables companies to anticipate failures and optimise maintenance schedules, thereby reducing downtime and costs. Previous studies have traditionally relied on purely data-driven models, often overlooking the potential benefits of integrating physical principles and domain knowledge.

To address this limitation, the study explores how physics-based information can enhance state-of-the-art AI models. Incorporating physical insights improves model interpretability and robustness, leading to more reliable and effective predictive maintenance solutions. This approach aims to bridge the gap between data-driven methodologies and physics-based reasoning, offering a more comprehensive framework for industrial AI applications.