用相关性辅助的传感器布局方法，解决复杂系统中冗余数据难题。
Data-driven Sensor Placement for Predictive Applications: A Correlation-Assisted Attribution Framework (CAAF)
- 先聚类候选传感器位置，再做特征重要性分析，减少冗余
- 在结构健康监测等场景中，显著提升预测精度和稳定性
- 适合处理非线性、混沌及多尺度交互的真实物理系统
最优传感器布置（OSP）对于复杂物理系统的高效监测、控制与推断至关重要。本文提出一种基于机器学习的特征重要性（FA）框架，用于识别目标预测所需的传感器布置方案。传统FA在高度相关的输入数据下表现不佳，而本文提出的相关性辅助归因框架（CAAF）通过在执行FA前对候选传感器位置进行聚类，降低冗余并增强泛化能力。我们通过一系列验证案例阐明了该框架的核心原理，并在真实动态系统中展示了其有效性，包括结构健康监测、机翼升力预测以及湍流通道流壁面法向速度估计。结果表明，相较于通常因非线性动力学、混沌行为和多尺度相互作用而失效的替代方法，CAAF表现更优，可有效应用于现实环境中的传感器最优布置识别。
原文摘要 · Abstract (English)
Optimal sensor placement (OSP) is critical for efficient, accurate monitoring, control, and inference in complex physical systems. We propose a machine-learning-based feature attribution (FA) framework to identify OSP for target predictions. FA quantifies input contributions to a model output; however, it struggles with highly correlated input data often encountered in practical applications for OSP. To address this, we propose a Correlation-Assisted Attribution Framework (CAAF), which introduces a clustering step on the candidate sensor locations before performing FA to reduce redundancy and enhance generalizability. We first illustrate the core principles of the proposed framework through a series of validation cases, then demonstrate its effectiveness in realistic dynamical systems such as structural health monitoring, airfoil lift prediction, and wall-normal velocity estimation for turbulent channel flow. The results show that the CAAF outperforms alternative approaches that typically struggle due to the presence of nonlinear dynamics, chaotic behavior, and multi-scale interactions, and enables the effective application of FA for identifying OSP in real-world environments.
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