Smart Sensor Placement: A Correlation-Aware Attribution Framework (CAAF) for Real-world Data Modeling
This work addresses sensor placement challenges for monitoring and control in domains such as engineering and fluid dynamics, representing an incremental improvement by enhancing existing feature attribution methods.
The paper tackles the problem of optimal sensor placement in complex real-world systems by proposing a Correlation-Aware Attribution Framework (CAAF) that improves feature attribution for highly correlated input data, resulting in outperformance over alternative approaches in applications like structural health monitoring and turbulent flow estimation.
Optimal sensor placement (OSP) is critical for efficient, accurate monitoring, control, and inference in complex real-world systems. We propose a machine-learning-based feature attribution framework to identify OSP for the prediction of quantities of interest. Feature attribution quantifies input contributions to a model's output; however, it struggles with highly correlated input data often encountered in real-world applications. To address this, we propose a Correlation-Aware Attribution Framework (CAAF), which introduces a clustering step before performing feature attribution 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 real-world 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 feature attribution for identifying OSP in real-world environments.