Model Based Structural Monitoring of Plates using Kalman Filter

Abstract

Structural health monitoring (SHM) is a quickly advancing field of study in civil engineering and recent advances in the field are in stark contrast to where the field started. For example modern technology of wireless sensing systems allowed for easier monitoring of structures, but the challenge of limiting the number of instrumented locations has not been overcome with traditional methods. The potential of alternative methods has only been realized in recent years with the increase of model based approaches. In particular, the use of limited measurements to estimate structural response at all locations is appealing. To accomplish this goal, this work approaches SHM by using a numerical model combined with a linear recursive state estimation algorithm, known as the Kalman Filter, to update the model-based prediction with a limited number of real time measurements taken on the structure. A thorough overview of the contents is given here. The first section introduces the topic of SHM and the goal of SHM. Then the challenges and limitation that face SHM are discussed along with the recent advances that can be used to overcome them. In Section 2, the proposed framework, a Kalman filter approach, is established. First, a finite element model is formulated for plate structures using the Mindlin-Reissner plate theory and then this finite element code is verified by a comparison with a commercial FEA software. Then the state space model of the system is defined for use with the Augmented Kalman Filter (AKF); the AKF approach overcomes the intrinsic challenge of unknown excitations for civil structures. The AKF is then formulated and discussed. For Section 3, using the AKF in numerical simulations are conducted for 5 different cases. The first three cases study the advantages of multi-metric measurements, i.e. strain and acceleration measurements combined, versus single metric measurement, i.e. strain measurement only or acceleration measurement only. Following that, the next two cases explore the question of whether multi-metric measurements will always provide the best results. Based on the conclusions from the previous section, Section 4 investigates the application of a genetic algorithm, a search algorithm based of Darwinian principles, to find the optimal sensor placement to use as the input to the AKF. Here the developed search algorithm is used in two cases, the first is to find the optimal placement for the strain measurement only case. Next, the improvements in accuracy that are gained by placing taking more measurements is investigated to determine if the gain in accuracy per added measurement decreases for large numbers of measurements. Section 5 contains the final conclusions about the use of the AKF for SHM of plate structures then the potential opportunities of future work regarding plate structures are discussed.