Design and validation of structural health monitoring system based on bio-inspired algorithms

The need of ensure the proper performance of the structures in service has made of structural health monitoring (SHM) a priority research area. Researchers all around the world have focused efforts on the development of new ways to continuous monitoring the structures and analyze the data collected from the inspection process in order to provide information about the current state and avoid possible catastrophes. To perform an effective analysis of the data, the development of methodologies is crucial in order to assess the structures with a low computational cost and with a high reliability. These desirable features can be found in biological systems, and these can be emulated by means of computational systems. The use of bio-inspired algorithms is a recent approach that has demonstrated its effectiveness in data analysis in different areas. Since these algorithms are based in the emulation of biological systems that have demonstrated its effectiveness for several generations, it is possible to mimic the evolution process and its adaptability characteristics by using computational algorithms. Specially in pattern recognition, several algorithms have shown good performance. Some widely used examples are the neural networks, the fuzzy systems and the genetic algorithms. This thesis is concerned about the development of bio-inspired methodologies for structural damage detection and classification. This document is organized in five chapters. First, an overview of the problem statement, the objectives, general results, a brief theoretical background and the description of the different experimental setups are included in Chapter 1 (Introduction). Chapters 2 to 4 include the journal papers published by the author of this thesis. The discussion of the results, some conclusions and the future work can be found on Chapter 5. Finally, Appendix A includes other contributions such as a book chapter and some conference papers.La necesidad de asegurar el correcto funcionamiento de las estructuras en servicio ha hecho de la monitorización de la integridad estructural un área de gran interés. Investigadores en todas las partes del mundo centran sus esfuerzos en el desarrollo de nuevas formas de monitorización contínua de estructuras que permitan analizar e interpretar los datos recogidos durante el proceso de inspección con el objetivo de proveer información sobre el estado actual de la estructura y evitar posibles catástrofes. Para desarrollar un análisis efectivo de los datos, es necesario el desarrollo de metodologías para inspeccionar la estructura con un bajo coste computacional y alta fiabilidad. Estas características deseadas pueden ser encontradas en los sistemas biológicos y pueden ser emuladas mediante herramientas computacionales. El uso de algoritmos bio-inspirados es una reciente técnica que ha demostrado su efectividad en el análisis de datos en diferentes áreas. Dado que estos algoritmos se basan en la emulación de sistemas biológicos que han demostrado su efectividad a lo largo de muchas generaciones, es posible imitar el proceso de evolución y sus características de adaptabilidad al medio usando algoritmos computacionales. Esto es así, especialmente, en reconocimiento de patrones, donde muchos de estos algoritmos brindan excelentes resultados. Algunos ejemplos ampliamente usados son las redes neuronales, los sistemas fuzzy y los algoritmos genéticos. Esta tesis involucra el desarrollo de unas metodologías bio-inspiradas para la detección y clasificación de daños estructurales. El documento está organizado en cinco capítulos. En primer lugar, se incluye una descripción general del problema, los objetivos del trabajo, los resultados obtenidos, un breve marco conceptual y la descripción de los diferentes escenarios experimentales en el Capítulo 1 (Introducción). Los Capítulos 2 a 4 incluyen los artículos publicados en diferentes revistas indexadas. La revisión de los resultados, conclusiones y el trabajo futuro se encuentra en el Capítulo 5. Finalmente, el Anexo A incluye otras contribuciones tales como un capítulo de libro y algunos trabajos publicados en conferencias

Artificial immune system (AIS) for damage detection under variable temperature conditions

Early damage detection remains one of the priorities of the structural health monitoring systems in the task of continuous monitoring. In this kind of systems different approaches can be used, however data-driven systems are requested because the information from the sensors is obtained directly from the structure in real operational and environmental conditions. Some of these approaches makes use of acousto-ultrasonics (AU) techniques, which offer the possibility of inspecting large areas of structures, by using a piezoelectric active sensor network. However, these kind of inspection systems are affected by the variations in the environmental conditions. In this sense, is a need to still working in more a nd better da ma ge detection techniques. This pa per descr ibes a hea lth monitor ing methodology combining the advantages of guided ultrasonic waves together with artificial immune systems as a pattern recognition technique to determine the effects of the temperature in the damage detection process, in addition, a sensor data fusion with the data from different temperatures is proposed as a hefty baseline to consider the healthy structure under different temperature conditions and discarding the resultant false positives by the changes in temperature. Experimental results are included to demonstrate the temperature effects and how the methodology improves the damage detection capabilities.Postprint (published version

Data-Driven Methodologies for Structural Damage Detection Based on Machine Learning Applications

Structural health monitoring (SHM) is an important research area, which interest is the damage identification process. Different information about the state of the structure can be obtained in the process, among them, detection, localization and classification of damages are mainly studied in order to avoid unnecessary maintenance procedures in civilian and military structures in several applications. To carry out SHM in practice, two different approaches are used, the first is based on modelling which requires to build a very detailed model of the structure, while the second is by means of data-driven approaches which use information collected from the structure under different structural states and perform an analysis by means of data analysis . For the latter, statistical analysis and pattern recognition have demonstrated its effectiveness in the damage identification process because real information is obtained from the structure through sensors installed permanently to the observed object allowing a real-time monitoring. This chapter describes a damage detection and classification methodology, which makes use of a piezoelectric active system which works in several actuation phases and that is attached to the structure under evaluation, principal component analysis, and machine learning algorithms working as a pattern recognition methodology. In the chapter, the description of the developed approach and the results when it is tested in one aluminum plate are also included

Distributed piezoelectric sensor system for damage identification in structures subjected to temperature changes

Structural health monitoring (SHM) is a very important area in a wide spectrum of fields and engineering applications. With an SHM system, it is possible to reduce the number of non-necessary inspection tasks, the associated risk and the maintenance cost in a wide range of structures during their lifetime. One of the problems in the detection and classification of damage are the constant changes in the operational and environmental conditions. Small changes of these conditions can be considered by the SHM system as damage even though the structure is healthy. Several applications for monitoring of structures have been developed and reported in the literature, and some of them include temperature compensation techniques. In real applications, however, digital processing technologies have proven their value by: (i) offering a very interesting way to acquire information from the structures under test; (ii) applying methodologies to provide a robust analysis; and (iii) performing a damage identification with a practical useful accuracy. This work shows the implementation of an SHM system based on the use of piezoelectric (PZT) sensors for inspecting a structure subjected to temperature changes. The methodology includes the use of multivariate analysis, sensor data fusion and machine learning approaches. The methodology is tested and evaluated with aluminum and composite structures that are subjected to temperature variations. Results show that damage can be detected and classified in all of the cases in spite of the temperature changesPeer ReviewedPostprint (published version

Structural damage detection and classification based on machine learning algorithms

Structural Health Monitoring is a growing area of interest given the benefits obtained from its use. This area includes different tasks in the damage identification process, among them, the most important is the damage detection at an early stage which enables to increase the security in mechanisms and systems, reducing risks and avoiding accidents. As a contribution in this topic, this work presents a data-driven methodology for the detection and classification of damages by using multivariate data driven approaches and machine learning algorithms which are validated and compared by using data from real structures in order to determine its behavior. In the methodology, PCA (Principal component analysis) and some pre-processing steps are used as the mechanisms to reduce data and build the features vector with relevant information about the different states of the structures under test. This methodology is validated by using some aluminum plates which are instrumented and inspected by means of PZT transducers attached to them and working in in several actuation phases. Results show a properly damage detection and classification of different simulated and real-damages.Postprint (published version

Simulation of glucose and insulin dynamics in patients with type I diabetes mellitus

La diabetes mellitus tipo I es una enfermedad de salud pública cada vez más común entre la gente, no necesariamente tiene sus orígenes en el paso genético entre generaciones. Como tal existe un desorden en los niveles normales en la glucosa en la sangre y su tratamiento médico actual más común es la aplicación de insulina al paciente deacuerdo a ciertas relaciones como el peso, la edad, hábitos alimenticios entre otros, este ajuste lo realiza el médico casi a prueba y error hasta encontrar el nivel adecuado para cada paciente así como su frecuencia de aplicación.Palabras clave: Diabetes Mellitus Tipo I, Dosificación, Insulina, Subcutánea, modelos de compartimientos.Type I diabetes mellitus is an increasingly common public health disease among people, it does not necessarily have its origins in the genetic passage between generations. As such there is a disorder in the normal levels of glucose in the blood and its most common current medical treatment is the application of insulin to the patient according to certain relationships such as weight, age, eating habits among others, this adjustment is made the doctor almost by trial and error until finding the right level for each patient as well as its frequency of application. Keywords: Diabetes Mellitus Type I, Dosage, Insulin, Subcutaneous, compartment models

Nonlinear feature extraction through manifold learning in an electronic tongue classification task

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. The developed signal processing methodology is composed of four stages: data unfolding, scaling, feature extraction, and classification. This study aims to compare seven manifold learning algorithms: Isomap, Laplacian Eigenmaps, Locally Linear Embedding (LLE), modified LLE, Hessian LLE, Local Tangent Space Alignment (LTSA), and t-Distributed Stochastic Neighbor Embedding (t-SNE) to find the best classification accuracy in a multifrequency large-amplitude pulse voltammetry electronic tongue. A sensitivity study of the parameters of each manifold learning algorithm is also included. A data set of seven different aqueous matrices is used to validate the proposed data processing methodology. A leave-one-out cross validation was employed in 63 samples. The best accuracy (96.83%) 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.Peer ReviewedPostprint (published version

Exoskeleton for lower limb rehabilitation with two degrees of freedom aimed at patients with cerebrovascular accidents

Introducción: Un exoesqueleto puede ser entendido como una estructura mecatrónica que puede ser acoplada a una extremidad   de manera externa y que permite el desarrollo de los movimientos en las diferentes articulaciones de esta misma. Estos movimientos son desarrollados con el apoyo de actuadores que son quienes definen los grados de libertad de este tipo de dispositivos.  Dada su versatilidad, estos pueden llegar a ser una herramienta útil en la asistencia de trabajos de rehabilitación de miembros superior e inferior humanos y en algunos casos por ejemplo ayudar a pacientes con parálisis en sus miembros en el desarrollo de actividades tan complejas como la marcha humana. Aunque hay algunos desarrollos importantes en esta área, esta sigue en fase de investigación y muchos de los desarrollos aún no están disponibles o no son asequibles para su uso masivo en países como Colombia y en general América Latina. Objetivo: Este trabajo presenta el desarrollo de un exoesqueleto activo, el cual fue diseñado para ayudar en la rehabilitación de pacientes que han tenido algún tipo de secuela como consecuencia de un accidente cerebro vascular (ACV), también conocido como Ictus. Metodología: Se incluye dentro del artículo, información sobre el modelado del sistema, el diseño, control y la construcción del dispositivo exoesquelético así como el desarrollo de unas pruebas preliminares orientadas a mostrar su uso en el desarrollo de pruebas de repetición en el plano sagital Resultados:  Como resultado se cuenta con un prototipo funcional que ya fue validado con pruebas de laboratorio y que permite monitorizar los movimientos de las articulaciones en un proceso de terapia en miembro inferior basada en ejercicios de repetición. Conclusiones: El sistema desarrollado presenta algunas facilidades que pueden ser útiles en la rehabilitación de pacientes con ictus, entre ellas se tienen las siguientes: desarrollo de un sistema automatizado de monitorización de rehabilitación, diseño mecánico y estructura ajustable a diferentes tipos de pacientes, modelado del sistema y desarrollo de sistema de control automático, así como facilidad de uso de interfaz desarrollada en Labview.Introduction:  An exoskeleton can be defined as a mechatronic structure that can be coupled to a limb externally and that allows the development of movements in the different joints of the same. These movements are developed with the support of actuators who are the ones who define the degrees of freedom of this type of devices. Although there are some important developments in this area, this is still in the research phase and many of the developments are not yet available or are not available for mass use in countries such as Colombia and Latin America in general. Objective:  This work presents the development of an active exoskeleton, which was designed to help in the rehabilitation of patients who have had some type of sequela as a result of an accident vascular brain (CVA), also known as stroke. Methodology:  Information on the modeling, design, control, and construction of the exoskeletal device as well as the development of preliminary tests are included and aimed at showing its use in the development of repetition tests in the sagittal plane. Results:  As a result, a functional prototype was done and validated in laboratory tests. From these experimental validations, it was possible to observe how the system works by moving the joints during a therapy process based on repetition exercises. Conclusions:The developed system presents some features and elements that may be useful in the rehabilitation of patients with stroke, among them are the following: development of an automated rehabilitation monitoring system, mechanical design which results in a structure adjustable to different types of patients, modeling of the system and development of automatic control system as well as ease of use of interface developed in Labview

Exoesqueleto para rehabilitación de miembro inferior con dos grados de libertad orientado a pacientes con accidentes cerebrovasculares

Introduction:  An exoskeleton can be defined as a mechatronic structure that can be coupled to a limb externally and that allows the development of movements in the different joints of the same. These movements are developed with the support of actuators who are the ones who define the degrees of freedom of this type of devices. Although there are some important developments in this area, this is still in the research phase and many of the developments are not yet available or are not available for mass use in countries such as Colombia and Latin America in general. Objective:  This work presents the development of an active exoskeleton, which was designed to help in the rehabilitation of patients who have had some type of sequela as a result of an accident vascular brain (CVA), also known as stroke. Methodology:  Information on the modeling, design, control, and construction of the exoskeletal device as well as the development of preliminary tests are included and aimed at showing its use in the development of repetition tests in the sagittal plane. Results:  As a result, a functional prototype was done and validated in laboratory tests. From these experimental validations, it was possible to observe how the system works by moving the joints during a therapy process based on repetition exercises. Conclusions:The developed system presents some features and elements that may be useful in the rehabilitation of patients with stroke, among them are the following: development of an automated rehabilitation monitoring system, mechanical design which results in a structure adjustable to different types of patients, modeling of the system and development of automatic control system as well as ease of use of interface developed in Labview.Introducción: Un exoesqueleto puede ser entendido como una estructura mecatrónica que puede ser acoplada a una extremidad   de manera externa y que permite el desarrollo de los movimientos en las diferentes articulaciones de esta misma. Estos movimientos son desarrollados con el apoyo de actuadores que son quienes definen los grados de libertad de este tipo de dispositivos.  Dada su versatilidad, estos pueden llegar a ser una herramienta útil en la asistencia de trabajos de rehabilitación de miembros superior e inferior humanos y en algunos casos por ejemplo ayudar a pacientes con parálisis en sus miembros en el desarrollo de actividades tan complejas como la marcha humana. Aunque hay algunos desarrollos importantes en esta área, esta sigue en fase de investigación y muchos de los desarrollos aún no están disponibles o no son asequibles para su uso masivo en países como Colombia y en general América Latina. Objetivo: Este trabajo presenta el desarrollo de un exoesqueleto activo, el cual fue diseñado para ayudar en la rehabilitación de pacientes que han tenido algún tipo de secuela como consecuencia de un accidente cerebro vascular (ACV), también conocido como Ictus. Metodología: Se incluye dentro del artículo, información sobre el modelado del sistema, el diseño, control y la construcción del dispositivo exoesquelético así como el desarrollo de unas pruebas preliminares orientadas a mostrar su uso en el desarrollo de pruebas de repetición en el plano sagital Resultados:  Como resultado se cuenta con un prototipo funcional que ya fue validado con pruebas de laboratorio y que permite monitorizar los movimientos de las articulaciones en un proceso de terapia en miembro inferior basada en ejercicios de repetición. Conclusiones: El sistema desarrollado presenta algunas facilidades que pueden ser útiles en la rehabilitación de pacientes con ictus, entre ellas se tienen las siguientes: desarrollo de un sistema automatizado de monitorización de rehabilitación, diseño mecánico y estructura ajustable a diferentes tipos de pacientes, modelado del sistema y desarrollo de sistema de control automático, así como facilidad de uso de interfaz desarrollada en Labview