Abstract
In this paper, we evaluate the performance of the so-called parametric t-distributed stochastic neighbor embedding (P-t-SNE), comparing it to the performance of the t-SNE, the non-parametric version. The methodology used in this study is introduced for the detection and classification of structural changes in the field of structural health monitoring. This method is based on the combination of principal component analysis (PCA) and P-t-SNE, and it is applied to an experimental case study of an aluminum plate with four piezoelectric transducers. The basic steps of the detection and classification process are: (i) the raw data are scaled using mean-centered group scaling and then PCA is applied to reduce its dimensionality; (ii) P-t-SNE is applied to represent the scaled and reduced data as 2-dimensional points, defining a cluster for each structural state; and (iii) the current structure to be diagnosed is associated with a cluster employing two strategies: (a) majority voting; and (b) the sum of the inverse distances. The results in the frequency domain manifest the strong performance of P-t-SNE, which is comparable to the performance of t-SNE but outperforms t-SNE in terms of computational cost and runtime. When the method is based on P-t-SNE, the overall accuracy fluctuates between 99.5% and 99.75%.
Highlights
In structural health monitoring (SHM), an important process for engineering structures, many methods have been applied for damage detection
We proposed an SHM strategy for the detection and classification of structural changes combining principal component analysis (PCA) and parametric t-distributed stochastic neighbor embedding (P-t-distributed stochastic neighbor embedding (t-SNE))
We have compared the parametric version of t-SNE with the non-parametric version
Summary
In structural health monitoring (SHM), an important process for engineering structures, many methods have been applied for damage detection. In [3], it is casted SHM in the context of statistical pattern recognition, and damage or structural changes are detected using two techniques based on time series analysis. In [4], three optimization-algorithm based support vector machines for damage detection in SHM are presented, which are expected to help engineers to process high-dimensional data. Dimensionality reduction is the process of reducing the number of high-dimensional variables by obtaining a low-dimensional set of variables. This reduced representation must correspond to the intrinsic information of the data.
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