Year: 2025
Type: conference-paper
Abstract: Simulation-generated synthetic data and hardware-based experimental data are both crucial for predictive maintenance in power electronics. However, their integration in condition monitoring is challenged by simulation-experimental gaps due to modeling assumptions and unmodeled dynamics. In this work, we propose a physics-guided alignment strategy to bridge this gap by leveraging a contrastive learning strategy. Based on large volume of synthetic data, we study the impact of introducing limited degraded experimental samples balancing alignment effectiveness and classification accuracy. Our results demonstrate that controlled inclusion of degraded experimental data improves domain alignment and enhances predictive performance with minimal reliance on real experimental data.
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Funder: European Commission
OpenAlex ID: W4416964544