Maribel Anaya

TL;DR: This work presents the detailed description of a structural health monitoring (SHM) system based on the use of a piezoelectric (PZT) active system and uses the nearest neighbor algorithm as a machine learning approach to classify different kinds of damage.

TL;DR: The implementation of an SHM system based on the use of piezoelectric (PZT) sensors for inspecting a structure subjected to temperature changes is shown, which shows that damage can be detected and classified in all of the cases in spite of the temperature changes.

TL;DR: A bioinspired strategy for the detection of structural changes using an artificial immune system (AIS) and a statistical data-driven modeling approach by means of a distributed piezoelectric active sensor network at different actuation phases is introduced.

TL;DR: In this paper, a methodology for the detection and classification of structural changes under different temperature scenarios using a statistical data-driven modelling approach by means of a distributed piezoelectric active sensor network at different actuation phases is presented.

TL;DR: A nonlinear feature extraction-based approach using manifold learning algorithms is developed in order to improve the classification accuracy in an electronic tongue sensor array, and the best accuracy was obtained when the methodology uses Mean-Centered Group Scaling (MCGS) for data normalization, the t-SNE algorithm for feature extraction, and k-nearest neighbors (kNN) as classifier.