发表机构
Infineon Technologies AG; Infineon Dresden AG & Co KG（英飞凌科技股份公司; 英飞凌德累斯顿股份公司及两合公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对 SiC 功率模块健康状态估计，测试五种参考方法与物理信息 NODE 在不同失效机制下的性能，发现累积热电特征可提升失效机制可迁移性。
AI 中文摘要
针对碳化硅（SiC）功率模块的数据驱动健康状态估计器，通常仅在单一加速老化试验中报告其性能，而该性能如何迁移至不同失效机制的情况却很少被测试。我们针对 prognostics 和状态监测领域的五种参考方法，与一种基于物理信息的 NODE（神经常微分方程）进行基准测试，测试在两种结构不同失效机制（焊料层疲劳和键合线脱落）驱动的 SiC 功率循环试验中，采用每模块 k 折交叉验证协议的表现。NODE 在两种输入方案下进行评估，其余流程保持一致：基线电前兆特征和一组累积热电特征。所有参考方法在键合线试验中性能下降，与焊料试验中的性能相比，平均误差增大且精度降低。输入累积特征的 NODE 在两种机制下均保持了其在焊料试验中的指标，差异在折间方差范围内，而输入基线前兆特征的相同架构则退化为参考方法的聚类表现。输入表示对健康状态估计器的失效机制可迁移性的贡献至少与架构的贡献相当。
英文摘要
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. We benchmark five reference methods from the prognostics and condition-monitoring literature against a physics-informed NODE (Neural Ordinary Differential Equation) on two SiC power-cycling campaigns driven by structurally different failure mechanisms, solder-layer fatigue and wire-bond lift-off, under a per-module $k$-fold protocol. The NODE is evaluated under two input regimes that share the rest of the pipeline: the baseline electrical precursors and a set of cumulative thermoelectric features. Every reference method degrades on the wire-bond campaign, with average errors growing and precision decreasing with respect to their performance on the soldered campaign. The NODE fed with the cumulative features keeps its soldered-campaign metrics on both mechanisms, with differences inside the fold-to-fold variance, while the same architecture fed with the baseline precursors falls back to the reference-method cluster. The input representation contributes at least as much as the architecture to failure-mechanism transferability of a health-state estimator.
CommentsAccepted at the 52nd Annual Conference of the IEEE Industrial Electronics Society (IECON 2026). 9 pages, 3 figures, 4 tables
