English(EN) Grounding Time-Series Foundation Models in Digital Twin Topology for Predictive Maintenance

新方法将时间序列模型在数字孪生中进行接地，用于预测性维护

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-01 04:00

研究人员开发了一种新方法，以提高时间序列基础模型（TSFM）在数字孪生环境中的预测性维护任务的性能。通过将TSFM在数字孪生的拓扑中进行接地，该方法增强了跨通道依赖性建模。这种受拓扑信息启发的融合方法在C-MAPSS数据集上进行了测试，结果表明，与现有的用于剩余使用寿命预测的最先进模型相比，多变量架构和跨注意力中的显式拓扑约束能够带来具有竞争力或更优越的性能。 AI

影响通过提高时间序列基础模型在数字孪生系统中的准确性和适用性，增强了预测性维护能力。

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arXiv cs.AI TIER_1 English(EN) · Sizhe Ma, Katherine A. Flanigan, Mario Berg\'es · 2026-10-01 04:00

基于数字孪生拓扑的实时基础模型接地用于预测性维护

arXiv:2609.40071v1 Announce Type: cross Abstract: Digital twins increasingly support downstream analytical tasks that depend on time-series data, motivating interest in time-series foundation models (TSFMs) as scalable backbones. However, TSFMs are primarily pretrained for tempor…

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基于数字孪生拓扑的实时基础模型接地用于预测性维护

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