Méthodes data-driven pour l’estimation de la durée de vie restante des modules de puissance
Date:
This presentation was delivered to the STAN team at the LMPS laboratory, bridging the gap between applied machine learning and mechanical engineering. The talk introduced the mechanical community to data-driven approaches for a complex multi-physics problem: estimating the lifetime of power electronic modules. Specifically, it explored how machine learning can be leveraged to model bond wire degradation through a Markov chain-based damage modeling coupled with surrogate machine learning models to infer mechanical indicators based on numerical simulation data.