4,226 research outputs found

    Structural damage monitoring based on machine learning and bio-inspired computing

    Get PDF
    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.Desde hace algunas décadas los sistemas para supervisar estructuras han tenido cada vez más relevancia. En esta evolución se ha pasado de estrategias que tenían como meta sólo la detec­ción de fallas a otras que buscan monitorizar permanentemente las estructuras bien sean éstas civiles o militares, ofreciendo información suficiente y pertinente que incide positivamente en el momento de tomar buenas decisiones, dentro de las cuales cabe destacar por ejemplo, las ori­entadas a realizar mantenimientos preventivos o correctivos si es del caso, reduciendo la posi­bilidad de accidentes, además de propiciar la disminución de costos que implican las repara­ciones más extensas cuando el daño se logra detectar de manera temprana. El presente trabajo se enfocó en tres elementos de diagnóstico de daños en estructuras, siendo estos en particular la detección, clasificación y localización, bien sea en estructuras metálicas o de material com­puesto, dado su amplio uso en vehículos de transporte aéreo, terrestre, marítimo, aeroespacial, aerogeneradores, infraestructura civil y militar. Se utilizaron las herramientas que ofrecen el aprendizaje automático (machine leaming) y la computación bio-inspirada, dados los buenos resultados que han ofrecido en la solución de problemas complejos y el reconocimiento de pa­trones. Involucrando cambios de temperatura dado que es uno de los parámetros a los que se ven enfrentadas las estructuras en ambientes reales. Se utilizó información de naturaleza estadística aplicada al reconocimiento de patrones y reducción del tamaño de la información con herramientas como el PCA (análisis de componentes principales), gracias a la experiencia lograda en trabajos desarrollados por el grupo de investigación CoDAlab. El documento está dividido en cinco capítulos. En el primerio se incluye una descripción general del problema, los objetivos y los resultados obtenidos, además de un breve introduc­ción teórica. Los Capítulos 2,3 y 4 incluyen los artículos publicados en diferentes revistas. En el Capítulo 5 se realiza una presentación de los resultados y conclusiones. En el Anexo A se incluyen otras contribuciones tales como un capítulo de libro y algunos trabajos presentados en conferencias. Finalmente en el anexo B se presenta el diseño de un sistema de multipliexación utilizado en el desarrollo de los experimentos realizados en el presente trabajo.Postprint (published version

    Detection of cyber-attacks in systems with distributed control based on support vector regression

    Get PDF
    Concept of Industry 4.0 and implementation of Cyber Physical Systems (CPS) and Internet of Things (IoT) in industrial plants are changing the way we manufacture. Introduction of industrial IoT leads to ubiquitous communication (usually wireless) between devices in industrial control systems, thus introducing numerous security concerns and opening up wide space for potential malicious threats and attacks. As a consequence of various cyber-attacks, fatal failures can occur on system parts or the system as a whole. Therefore, security mechanisms must be developed to provide sufficient resilience to cyber-attacks and keep the system safe and protected. In this paper we present a method for detection of attacks on sensor signals, based on e insensitive support vector regression (e-SVR). The method is implemented on publicly available data obtained from Secure Water Treatment (SWaT) testbed as well as on a real-world continuous time controlled electro-pneumatic positioning system. In both cases, the method successfully detected all considered attacks (without false positives)

    A damage classification approach for structural health monitoring using machine learning

    Get PDF
    Inspection strategies with guided wave-based approaches give to structural health monitoring (SHM) applications several advantages, among them, the possibility of the use of real data from the structure which enables continuous monitoring and online damage identification. These kinds of inspection strategies are based on the fact that these waves can propagate over relatively long distances and are able to interact sensitively with and uniquely with different types of defects. The principal goal for SHM is oriented to the development of efficient methodologies to process these data and provide results associated with the different levels of the damage identification process. As a contribution, this work presents a damage detection and classification methodology which includes the use of data collected from a structure under different structural states by means of a piezoelectric sensor network taking advantage of the use of guided waves, hierarchical nonlinear principal component analysis (h-NLPCA), and machine learning. The methodology is evaluated and tested in two structures: (i) a carbon fibre reinforced polymer (CFRP) sandwich structure with some damages on the multilayered composite sandwich structure and (ii) a CFRP composite plate. Damages in the structures were intentionally produced to simulate different damage mechanisms, that is, delamination and cracking of the skin.Peer ReviewedPostprint (published version
    corecore