English(EN) Latent variable models for simultaneous EOV identification and removal in population-based SHM

新的贝叶斯框架通过去除EOV改进结构健康监测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-13 04:00

研究人员开发了一种新颖的贝叶斯框架用于结构健康监测（SHM），该框架可同时识别和去除环境与运行变异性（EOV）。该方法将潜在EOV建模为高斯过程，通过卡尔曼滤波器和拉普拉斯近似实现高效推理。该方法在实验室结构和模拟海上风电场上进行了验证，与现有技术相比，证明了改进的损伤检测和EOV恢复能力。 AI

影响这项研究引入了先进的统计建模技术，可以提高结构健康监测系统的可靠性和准确性，可能影响基础设施的维护和安全。

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arXiv cs.LG TIER_1 English(EN) · M. D. Champneys, M. R. Jones, A. J. Hughes, T. J. Rogers, E. J. Cross, K. Worden · 2026-08-13 04:00

用于群体结构健康监测中EOV识别和去除的潜在变量模型

arXiv:2608.11995v1 Announce Type: cross Abstract: The robust treatment of environmental and operational variability (EOV) is an open challenge in population-based structural health monitoring (PBSHM). The difficulty is compounded in the case that the EOV signals are unmeasured. A…