发表机构
Carnegie Mellon University（卡内基梅隆大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出在数字孪生拓扑中锚定时间序列基础模型，通过拓扑约束的融合方法提升预测性维护中的剩余使用寿命预测性能，并证明多变量架构及拓扑信息与预训练权重等因素互补。
AI 中文摘要
数字孪生日益支持依赖于时间序列数据的下游分析任务，这激发了人们对时间序列基础模型（TSFMs）作为可扩展骨干网络的兴趣。然而，TSFMs主要针对时间延续进行预训练，在回归等未见任务上往往表现不佳，并且在数字孪生背景下与最先进的专用模型的系统性实证比较仍然有限。本文做出了三项贡献。首先，我们使用C-MAPSS数据集，对五个著名的具有冻结骨干网络的TSFMs在剩余使用寿命（RUL）预测上进行了基准测试，发现多变量架构显著优于单变量架构，尤其是在变化的操作条件下。这引发了一个更深层次的问题：当跨通道依赖性可以通过预训练权重、目标任务适应和数字孪生衍生的表示来建模时，每个因素贡献了多少，它们是否互补？其次，我们提出了一种拓扑信息融合方法，其中源自数字孪生在其信息模型之间存储的资产结构的拓扑约束，明确地塑造了交叉注意力，使得融合后的表示尊重物理系统的局部连接性，而不是依赖于不受约束的全对全交互。第三，我们跨C-MAPSS子集（具有不同操作复杂度）进行了一项消融研究，该研究隔离了这三个来源及其相互作用。这些来源被证明是互补而非冗余的，并且拓扑约束的注意力优于无约束的融合，尽管幅度较小，使得由数字孪生表示告知的冻结TSFM能够保持竞争力，在某些情况下甚至在该回归任务上超越最先进的性能。
英文摘要
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 temporal continuation and often underperform on unseen tasks such as regression, and systematic empirical comparisons against state-of-the-art dedicated models in digital twin contexts remain limited. This paper makes three contributions. First, we benchmark five well-known TSFMs with frozen backbones on remaining useful life (RUL) prediction using the C-MAPSS dataset, finding that multivariate architectures substantially outperform univariate ones, particularly under varying operating conditions. This raises a deeper question: when cross-channel dependencies can be modeled through pretrained weights, target-task adaptation, and digital twin-derived representations, how much does each contribute, and are they complementary? Second, we propose a topology-informed fusion approach in which topological constraints, derived from the asset structure the digital twin stores among its information models, explicitly shape cross-attention, so that fused representations respect the physical system's local connectivity rather than relying on unconstrained all-to-all interactions. Third, we conduct an ablation study across C-MAPSS subsets of varying operational complexity that isolates the three sources and their interactions. The sources prove complementary rather than redundant, and topology-constrained attention outperforms unconstrained fusion, though by a small margin, enabling a frozen TSFM informed by digital twin representations to remain competitive or in some cases exceed state-of-the-art performance on this regression task.
CommentsSubmitted to Reliability Engineering \& System Safety (RESS)
