An Improved Optimal Sensor Placement Strategy for Kalman-Based Multiple-Input Estimation

The knowledge of the dynamic behavior of a mechanical system in a certain operating scenario is essential in many industrial applications. In particular, nowadays, the accurate and concurrent identification of the response fields and external loads represents a challenging target. Several experimental techniques, some exploiting a coupling with simulated solutions based on predictive methodologies, have recently been proposed and are in current use. However, in practice there is a common issue in the selection of the optimal types of sensors and their measurement location selection in order to reconstruct the desired quantities (e.g., loads, displacement or acceleration field) for a desired accuracy and dynamic range. This paper focuses on a Kalman filter approach for multiple input/state estimation, combining operational measurement and numerical model data. In the presented framework, an existing Optimal Sensor Placement (OSP) strategy for load identification is discussed and an improvement of this sensor selection is proposed. The reference OSP approach, previously proposed by the authors, is mainly focused on system observability, which is only a minimum requirement to obtain a stable estimator. For this reason it does not necessarily lead to the most accurate estimator or the highest dynamic range. In this work, we propose two alternative metrics based respectively on estimator covariance convergence and closed-loop estimator bandwidth with respect to the available set of measurements. The existing OSP is compared with the proposed metrics for multiple input/state estimation, showing improved accuracy of estimated quantities when these new metrics are accounted for in the sensor selection.