arXiv Machine Learning August 11, 2026 By Mattia Scarpa, Evgeny Kusmenko, Francesco Toso, Mattia Bruschetta, Ruggero Carli, Simon Achatz

Failure-Mechanism Transferability of Cumulative-Damage Features for Health State Estimation of SiC Power Modules

Read the original on arXiv Machine Learning →

arXiv:2608. 08365v1 Announce Type: cross Abstract: Data-driven health-state estimators for SiC (Silica-Carbide) power modules typically report their performance on a single accelerated-aging campaign, and how that performance transfers to a different failure mechanism is rarely tested.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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