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IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2026  > In Press, Accepted Manuscript
S. Xu, D. Pan, Z. Jiang, Z. Chen, H. Yu, and W. Gui, “KIG: A knowledge graph-guided iterative-updating graph neural network for multisensor time series time-delay estimation,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 2, pp. 1–19, Feb. 2026. doi: 10.1109/JAS.2025.125897
Citation: S. Xu, D. Pan, Z. Jiang, Z. Chen, H. Yu, and W. Gui, “KIG: A knowledge graph-guided iterative-updating graph neural network for multisensor time series time-delay estimation,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 2, pp. 1–19, Feb. 2026. doi: 10.1109/JAS.2025.125897
shu

KIG: A Knowledge Graph-Guided Iterative-Updating Graph Neural Network for Multisensor Time Series Time-Delay Estimation

doi: 10.1109/JAS.2025.125897
Funds:  This work was supported by the Young Scientists Fund of the National Natural Science Foundation of China (62303491), the Major Program of Xiangjiang Laboratory (22XJ01005), the Science and Technology Innovation Program of Hunan Province (2024RC1007), and the Natural Science Foundation of Hunan Province (2025JJ10007)
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    Abstract
  • Temporal alignment of multisensor time series (MTS) is a critical prerequisite for accurate modeling and optimal control in subsequent data-driven applications. Nevertheless, many approaches frequently neglect to consider the complex interdependencies between different sensors in MTS, and temporal alignment in many methods is typically treated as an isolated task disconnected from the downstream objectives, leading to unsatisfactory performances in follow-up applications. To address these challenges, this paper proposes a novel knowledge graph (KG)-guided iterative-updating graph neural network (GNN) for time-delay estimation (TDE) in MTS. Initially, a domain-specific KG is constructed from domain mechanism knowledge, providing a foundation for GNN’s initialization. Next, capitalizing on the inherent structure of the graph topology, a GNN-based TDE method is developed. Then, a customized loss function is constructed, which synthesizes both the performances of downstream tasks and graph-based constraints. Moreover, an innovative algorithm for GNN structure learning and iterative-updating is proposed to renovate the graph structure further. Finally, experimental results across various regression and classification tasks on numerical simulation, public datasets, and the real blast furnace ironmaking dataset demonstrate that the proposed method can achieve accurate temporal alignment of MTS.

     

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