Optimal Sensor Placement Algorithms for modal vibration testing
This toolkit offers a set of Optimal Sensor Placement Algorithms (Modal Kinetic Energy Method (MKE), Effective Independence method (EFI) and Information Entropy Index (IEI) Method). The objective of these algorithms is to maximise the information gained from the structural testing, while keeping the number of necessary sensors to a minimum. An enhanced IE variant is additionally included, which resolves the problem of close positioning of sensors, which forms a common issue when sensor positions are selected from a dense grid. In addition to the three methods, a set of algorithms is provided that compares the results in terms of three metrics, namely, the information entropy index, the Modal Assurance Criterion and a newly introduced metric, the relative dispersion index. For the quantification of trade-offs among the selected metrics a Pareto Front algorithm is added. Conducted studies indicate that the evaluation of different OSP configurations is strongly dependent on the employed metric. It is therefore of vital importance to select the appropriate metric when determining optimal sensor positions (Leyder et al. 2015; Leyder, C. et al. 2016; Leyder et al. 2018; Dertimanis 2013).
We share the software we develop openly and freely but ask that you cite the related literature sources: Dertimanis, V. K. 2013. ‘On the Use of Dispersion Analysis for Model Assessment in Structural Identification’. Journal of Vibration and Control 19 (15): 2270–2284. https://doi.org/10.1177/1077546313501536.
Leyder, C., Chatzi, E., Frangi, A., and Lombaert, G. 2016. ‘Comparison of Optimal Sensor Placement Algorithms via Implementation on an Innovative Timber Structure’. In International Symposium on Life-Cycle Civil Engineering (IALCCE 2016), 260–67. Delft, the Netherlands.
Leyder, C., V. Dertimanis, A. Frangi, E. Chatzi, and G. Lombaert. 2018. ‘Optimal Sensor Placement Methods and Metrics – Comparison and Implementation on a Timber Frame Structure’. Structure and Infrastructure Engineering 14 (7): 997–1010. https://doi.org/10.1080/15732479.2018.1438483.
Leyder, C., V. K. Ntertimanis, E. Chatzi, and A. Frangi. 2015. ‘Optimal Sensor Placement for the Modal Identification of an Innovative Timber Structure’. In 1st ECCOMAS Thematic Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2015, 467–76. Crete Island, Greece.