Advanced optimal sensor placement for Kalman-based multiple-input estimation

Abstract

The direct measurement of the external loads acting on a mechanical component represents often a challenge in many engineering applications. In this context, the attention of many researchers is focused on the field of inverse load identification. Several techniques are proposed in literature to address this problem by means of experimental methodologies, often coupled with simulation solutions. In this paper, a Kalman-based methodology is considered, which solves the problem of inverse load identification in a predictive manner. The common issue of most of the techniques is the selection of an optimal set of sensors which gives the best load estimation. In the Kalman-filtering framework, an Optimal Sensor Placement (OSP) strategy has been proposed by the authors and it aims to find the best set of sensors in terms of system observability, which is the minimum requirement of a stable estimation. However, this does not guarantee the most accurate load estimation. In this contribution, two alternative metrics are proposed, based on: i) steady-state error covariance of the estimation, ii) estimator bandwidth, with respect to the available set of measurements. Both criteria will be included in the existing OSP to improve the sensors selection. A comparison of the three strategies for multiple input/state estimation is discussed on an industrial-scale Finite Element Model, in order to show the improvement in the accuracy of the estimated quantities.