Desarrollo de un sistema de monitorización de la integridad estructural para aplicaciones en ingeniería mediante técnicas de reducción de la dimensionalidad

• Autores: David Agis Cherta
• Directores de la Tesis: Francesc Pozo Montero (dir. tes.)
• Lectura: En la Universitat Politècnica de Catalunya (UPC) ( España ) en 2020
• Idioma: español
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• Resumen

  • This thesis describes a structural health monitoring (SHM) strategy to detect and classify changes in structures that can be equipped with sensors. SHM is an area of great interest, because its main objective is to verify the health of the structure to ensure its correct operation and, in turn, save maintenance costs. This objective is achieved by using algorithms and equipping the structure with a network of sensors that continuously monitor it.

    Researchers from around the world focus their efforts on the development of new forms of continuous monitoring to know the current state of the structure and to avoid possible failures or catastrophes. In this sense, in this work, a network of piezoelectric sensors (PZTs) is used for the development of the strategy of detection and classification of structural changes. This network of PZTs, attached to the surface of the structure to be diagnosed, applies vibrational excitation signals and, at the same time, collects the responses propagated through the structure. With this collected information, certain mathematical algorithms are developed.

    To carry out the main task of the proposed methodology, detection and classification of structural changes, the technique called t-distributed stochastic neighbor embedding (t-SNE) is essentially used. This technique is capable of representing the local structure of the high-dimensional data collected by the sensor network in two-dimensional or three-dimensional space. Furthermore, for the classification of structural changes, the detection methodology is expanded by adding the use of three strategies: (a) the smallest point-centroid distance; (b) the majority vote; and (c) the sum of the inverses of the distances.

    The methodology proposed in this study is tested and validated using an aluminum plate equipped with four PZT sensors and for certain predefined structural changes. The promising results obtained show the great classification capacity and the strong performance of this methodology, successfully classifying about 100% of the cases in various experimental scenarios.

    The main contribution of this project is the combination of the t-SNE technique with a carefully selected pre-processing of the data and with the three proposed classification strategies. This combination significantly improves the quality of the groups or clusters obtained with the damage detection and classification method, which represent the different structural states. Likewise, said combination diagnoses a structure with a low computational cost and high reliability.

    Regarding the applicability of the suggested strategy, there is no prescribed field of application: if a network of sensors can be installed in the structure to be diagnosed and several phases of action can be considered, the approach presented here can be, a priori, implemented.