Combined approach for optimal sensor placement and experimental verification in the context of tower-like structures

Ina Reichert,Peter Olney,Tom Lahmer

doi:10.1007/s13349-020-00448-7

Ina Reichert, Peter Olney + Show 1 more

Open Access

https://doi.org/10.1007/s13349-020-00448-7

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Abstract

When it comes to monitoring of huge structures, main issues are limited time, high costs and how to deal with the big amount of data. In order to reduce and manage them, respectively, methods from the field of optimal design of experiments are useful and supportive. Having optimal experimental designs at hand before conducting any measurements is leading to a highly informative measurement concept, where the sensor positions are optimized according to minimal errors in the structures’ models. For the reduction of computational time a combined approach using Fisher Information Matrix and mean-squared error in a two-step procedure is proposed under the consideration of different error types. The error descriptions contain random/aleatoric and systematic/epistemic portions. Applying this combined approach on a finite element model using artificial acceleration time measurement data with artificially added errors leads to the optimized sensor positions. These findings are compared to results from laboratory experiments on the modeled structure, which is a tower-like structure represented by a hollow pipe as the cantilever beam. Conclusively, the combined approach is leading to a sound experimental design that leads to a good estimate of the structure’s behavior and model parameters without the need of preliminary measurements for model updating.

Highlights

For assessing civil engineering structures, methods for designing optimal experiments (DoE) increasingly come to the fore
Even more important is the fact that the computing time for mean-squared error (MSE) can be reduced by preselecting the sensor setups after gaining the results of the optimality criterion used on Fisher Information Matrix (FIM)
With the combined approach both random and systematic errors can be included in the data, but the computation time is highly reduced compared to only applying the MSE approach

Summary

Introduction

For assessing civil engineering structures, methods for designing optimal experiments (DoE) increasingly come to the fore. It often takes a lot of effort, especially in the case of tall structures, to equip the structure with the measurement devices, the associated cables and controllers. Others make use of the modal information, such as natural frequency and modeshape, to gain the optimal sensor placement [18, 28] To improve this practice, it is useful to apply methods of DoE in order to place sensors at significant positions and to possibly reduce the amount of sensors, which is accompanied with reducing costs for the measurements [18, 36]

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