Design and validation of a structural health monitoring system for aeronautical structures.

Structural Health Monitoring (SHM) is an area where the main objective is the verification of the state or the health of the structures in order to ensure proper performance and maintenance cost savings using a sensor network attached to the structure, continuous monitoring and algorithms. Different benefits are derived from the implementation of SHM, some of them are: knowledge about the behavior of the structure under different loads and different environmental changes, knowledge of the current state in order to verify the integrity of the structure and determine whether a structure can work properly or whether it needs to be maintained or replaced and, therefore, to reduce maintenance costs. The paradigm of damage identification (comparison between the data collected from the structure without damages and the current structure in orderto determine if there are any changes) can be tackled as a pattern recognition problem. Some statistical techniques as Principal Component Analysis (PCA) or Independent Component Analysis (ICA) are very useful for this purpose because they allow obtaining the most relevant information from a large amount of variables. This thesis uses an active piezoelectric system to develop statistical data driven approaches for the detection, localization and classification of damages in structures. This active piezoelectric system is permanently attached to the surface of the structure under test in order to apply vibrational excitations and sensing the dynamical responses propagated through the structure at different points. As pattern recognition technique, PCA is used to perform the main task of the proposed methodology: to build a base-line model of the structure without damage and subsequentlyto compare the data from the current structure (under test) with this model. Moreover, different damage indices are calculated to detect abnormalities in the structure under test. Besides, the localization of the damage can be determined by means of the contribution of each sensor to each index. This contribution is calculated by several different methods and their comparison is performed. To classify different damages, the damage detection methodology is extended using a Self-Organizing Map (SOM), which is properly trained and validated to build a pattern baseline model using projections of the data onto the PCAmodel and damage detection indices. This baseline is further used as a reference for blind diagnosis tests of structures. Additionally, PCA is replaced by ICAas pattern recognition technique. A comparison between the two methodologies is performed highlighting advantages and disadvantages. In order to study the performance of the damage classification methodology under different scenarios, the methodology is tested using data from a structure under several different temperatures. The methodologies developed in this work are tested and validated using different structures, in particular an aircraft turbine blade, an aircraft wing skeleton, an aircraft fuselage,some aluminium plates and some composite matarials plates.La monitorización de daños en estructuras (SHM por sus siglas en inglés) es un área que tiene como principal objetivo la verificación del estado o la salud de la estructura con el fin de asegurar el correcto funcionamiento de esta y ahorrar costos de mantenimiento. Para esto se hace uso de sensores que son adheridos a la estructura, monitorización continua y algoritmos. Diferentes beneficios se obtienen de la aplicación de SHM, algunos de ellos son: el conocimiento sobre el desempeño de la estructura cuando esta es sometida a diversas cargas y cambios ambientales, el conocimiento del estado actual de la estructura con el fin de determinar la integridad de la estructura y definir si esta puede trabajar adecuadamente o si por el contrario debe ser reparada o reemplazada con el correspondiente beneficio del ahorro de gastos de mantenimiento. El paradigma de la identificación de daños (comparación entre los datos obtenidos de la estructura sin daños y la estructura en un estado posterior para determinar cambios) puede ser abordado como un problema de reconocimiento de patrones. Algunas técnicas estadísticas tales como Análisis de Componentes Principales (PCA por sus siglas en inglés) o Análisis de Componentes Independientes (ICA por sus siglas en ingles) son muy útiles para este propósito puesto que permiten obtener la información más relevante de una gran cantidad de variables. Esta tesis hace uso de un sistema piezoeléctrico activo para el desarrollo de algoritmos estadísticos de manejo de datos para la detección, localización y clasificación de daños en estructuras. Este sistema piezoeléctrico activo está permanentemente adherido a la superficie de la estructura bajo prueba con el objeto de aplicar señales vibracionales de excitación y recoger las respuestas dinámicas propagadas a través de la estructura en diferentes puntos. Como técnica de reconocimiento de patrones se usa Análisis de Componentes Principales para realizar la tarea principal de la metodología propuesta: construir un modelo PCA base de la estructura sin daño y posteriormente compararlo con los datos de la estructura bajo prueba. Adicionalmente, algunos índices de daños son calculados para detectar anormalidades en la estructura bajo prueba. Para la localización de daños se usan las contribuciones de cada sensor a cada índice, las cuales son calculadas mediante varios métodos de contribución y comparadas para mostrar sus ventajas y desventajas. Para la clasificación de daños, se amplia la metodología de detección añadiendo el uso de Mapas auto-organizados, los cuales son adecuadamente entrenados y validados para construir un modelo patrón base usando proyecciones de los datos sobre el modelo PCA base e índices de detección de daños. Este patrón es usado como referencia para realizar un diagnóstico ciego de la estructura. Adicionalmente, dentro de la metodología propuesta, se utiliza ICA en lugar de PCA como técnica de reconocimiento de patrones. Se incluye también una comparación entre la aplicación de las dos técnicas para mostrar las ventajas y desventajas. Para estudiar el desempeño de la metodología de clasificación de daños bajo diferentes escenarios, esta se prueba usando datos obtenidos de una estructura sometida a diferentes temperaturas. Las metodologías desarrolladas en este trabajo fueron probadas y validadas usando diferentes estructuras, en particular un álabe de turbina, un esqueleto de ala y un fuselaje de avión, así como algunas placas de aluminio y de material compuest

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

New electronic tongue sensor array system for accurate liquor beverage classification

The use of sensors in different applications to improve the monitoring of a process and its variables is required as it enables information to be obtained directly from the process by ensuring its quality. This is now possible because of the advances in the fabrication of sensors and the development of equipment with a high processing capability. These elements enable the development of portable smart systems that can be used directly in the monitoring of the process and the testing of variables, which, in some cases, must evaluated by laboratory tests to ensure high-accuracy measurement results. One of these processes is taste recognition and, in general, the classification of liquids, where electronic tongues have presented some advantages compared with traditional monitoring because of the time reduction for the analysis, the possibility of online monitoring, and the use of strategies of artificial intelligence for the analysis of the data. However, although some methods and strategies have been developed, it is necessary to continue in the development of strategies that enable the results in the analysis of the data from electrochemical sensors to be improved. In this way, this paper explores the application of an electronic tongue system in the classification of liquor beverages, which was directly applied to an alcoholic beverage found in specific regions of Colombia. The system considers the use of eight commercial sensors and a data acquisition system with a machine-learning-based methodology developed for this aim. Results show the advantages of the system and its accuracy in the analysis and classification of this kind of alcoholic beverage.This research was funded by the Department of Science, Technology and Innovation of Colombia, grant 799, and Universidad Nacional de Colombia, grant 57399.Peer ReviewedPostprint (published version

Structural health monitoring based on principal component analysis: damage detection, localization and classification

Postprint (published version

Potential field methodologies for trajectory planning in mobile robots

Este artículo se presentan dos metodologías de trabajo con campos de potencial para el planteamiento de trayectorias de robots móviles. Estas metodologías tratan el terreno sobre el cual se mueve el móvil como un área que esta sometida a diferentes fuerzas de atracción y repulsión, aprovechando esta característica es posible encontrar trayectorias libres.This article presents two work methodologies with potential fields for the approach of trajectories of mobile robots. These methodologies treat the terrain on which the mobile moves as an area that is subjected to different forces of attraction and repulsion, taking advantage of this characteristic it is possible to find free trajectories

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

Combined and I indices based on principal component analysis for damage detection and localization

In this paper, two indices (combined or phi index and I index), different to the presented in [1][2][3] are used to detect damages; these indices are calculated from the information obtained from the projection of the experiments into the PCA models (baseline). They give us a measurement about the difference between the tested and the healthy structure. The experiments are taken from an active piezoelectric system which is excited with lamb waves in different phases and the fact that any defect in the structure changes its vibrational response is exploited. For localization, five different methods of contribution analysis are used (complete decomposition contribution, partial decomposition contribution, angle based contribution, reconstruction based contribution and diagonal contribution). With these methods, the contribution of each sensor to each index is analyzed, in this way, sensor with largest contribution suggests the path where the damage could be located (from the actuator to this sensor). The combination of all indices and all contributions (a total of 2 x 5) are analyzed and compared. To validate the approaches, they are applied to an aircraft turbine blade instrumented with seven PZT’s. Different damages are simulated.Peer ReviewedPostprint (published version

Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version

A bioinspired methodology based on an artificial immune system for damage detection in structural health monitoring

Among all the aspects that are linked to a structural health monitoring (SHM) system, algorithms, strategies, or methods for damage detection are currently playing an important role in improving the operational reliability of critical structures in several industrial sectors. This paper introduces 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. Damage detection and classification of structural changes using ultrasonic signals are traditionally performed using methods based on the time of flight. The approach followed in this paper is a data-based approach based on AIS, where sensor data fusion, feature extraction, and pattern recognition are evaluated. One of the key advantages of the proposed methodology is that the need to develop and validate a mathematical model is eliminated. The proposed methodology is applied, tested, and validated with data collected from two sections of an aircraft skin panel. The results show that the presented methodology is able to accurately detect damage.Postprint (published version

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