Resumen de Structural damage monitoring based on machine learning and bio-inspired computing
Jaime Vitola Oyaga
For a few decades, systems for supervising structures have become increasingly irnportant. In origin, the strategies had as a goal only the detection of damages. Furthermore, now monitoring the civil or military structures permanently and offering sufficient and relevant information helping make the right decisions. The SHM is applicable, carrying out preventive or corrective maintenance decisions, reducing the possibility of accidents, and promoting the reduction of costs that more extensive repairs imply when the damage is detected early. The current work focused on three elements of diagnosis of structural damage: detection, classification, and location, either in metaltic or cornposite material structures, given their wide use in air, land, rnaritime transport vehicles, aerospace, wind turbines, civil and military infrastructure. This work used the tools offered by machine leaming and bio-inspired computing. Given the right results to solve complex problems and recognizing pattems. It also involves changes in temperature since it is one of the parameters that influence real environments' structures. Information of a statistical nature applied to recognizing pattems and reducing the size of the information was used with tools such as PCA (principal component analysis), thanks to the experience obtained in works developed by the CoDAlab research group. The document is divided into five parts. The first includes a general description of the problem, the objecti.-es, and the results obtained, in addition to a brief theoretical introduction. Chapters 2, 3, and 4 include articles published in different joumals. Chapter 5 shows the results and conclusions. Other contributions, such as a book chapter and sorne papers presented at conferences, are included in appendix A. Finally, appendix B presents a multiplexing system used to develop the experiments carried out in this work.
