Graphic summary
  • Show / hide key
  • Information


Scientific and technological production
  •  

1 to 50 of 53 results
  • A structural damage detection indicator based on principal component analysis and statistical hypothesis testing

     Mujica Delgado, Luis Eduardo; Ruiz Ordoñez, Magda Liliana; Pozo Montero, Francesc; Rodellar Benede, Jose Julian; Güemes Gordo, Alfredo
    Smart materials and structures
    Date of publication: 2014-02
    Journal article

    Read the abstract Read the abstract View View Open in new window  Share Reference managers Reference managers Open in new window

    A comprehensive statistical analysis is performed for structural health monitoring (SHM). The analysis starts by obtaining the baseline principal component analysis (PCA) model and projections using measurements from the healthy or undamaged structure. PCA is used in this framework as a way to compress and extract information from the sensor-data stored for the structure which summarizes most of the variance in a few (new) variables into the baseline model space. When the structure needs to be inspected, new experiments are performed and they are projected into the baseline PCA model. Each experiment is considered as a random process and, consequently, each projection into the PCA model is treated as a random variable. Then, using a random sample of a limited number of experiments on the healthy structure, it can be inferred using the chi(2) test that the population or baseline projection is normally distributed with mean mu(h) and standard deviation sigma(h). The objective is then to analyse whether the distribution of samples that come from the current structure (healthy or not) is related to the healthy one. More precisely, a test for the equality of population means is performed with a random sample, that is, the equality of the sample mean mu(s) and the population mean mu(h) is tested. The results of the test can determine that the hypothesis is rejected (mu(h) not equal mu(c) and the structure is damaged) or that there is no evidence to suggest that the two means are different, so the structure can be considered as healthy. The results indicate that the test is able to accurately classify random samples as healthy or not.

  • Data-driven multivariate algorithms for damage detection and identification: Evaluation and comparison

     Torres Arredondo, Miguel Angel; Tibaduiza Burgos, Diego Alexander; Mujica Delgado, Luis Eduardo; Rodellar Benede, Jose Julian; Fritzen, Claus-Peter
    Structural health monitoring: an international journal
    Date of publication: 2014-01
    Journal article

    Read the abstract Read the abstract View View Open in new window  Share Reference managers Reference managers Open in new window

    This article is concerned with the experimental validation of a structural health monitoring methodology for damage detection and identification. Three different data-driven multivariate algorithms are considered here to obtain the baseline pattern. These are based on principal component analysis, independent component analysis and hierarchical non-linear principal component analysis. The contribution of this article is to examine and compare the three proposed algorithms that have been reported as reliable methods for damage detection and identification. The approach is based on a distributed piezoelectric active sensor network for the excitation and detection of structural dynamic responses. A woven multilayered composite plate and a simplified aircraft composite skin panel are used as examples to test the approaches. Data-driven baseline patterns are built when the structure is known to be healthy from wavelet coefficients of the structural dynamic responses. Damage is then simulated by adding masses at different positions of the structures. The data from the structure in different states (damaged or not) are then projected into the different models by each actuator in order to generate the input feature vectors of a self-organizing map from the computed components together with squared prediction error measures. All three methods are shown to be successful in detecting and classifying the simulated damages. At the end, a critical comparison is given in order to investigate the advantages and disadvantages of each method for the damage detection and identification tasks.

  • Data-driven methodology to detect and classify structural changes under temperature variations

     Anaya, Maribel; Tibuadiza, Diego; Torres, Miguel Angel; Pozo Montero, Francesc; Ruiz Ordoñez, Magda Liliana; Mujica Delgado, Luis Eduardo; Rodellar Benede, Jose Julian; Fritzen, Claus-Peter
    Smart materials and structures
    Date of publication: 2014-01-01
    Journal article

    View View Open in new window  Share Reference managers Reference managers Open in new window

  • Design and validation of a methodology for wind energy structures health monitoring

     Zugasti Uriguen, Ekhi
    Defense's date: 2014-01-16
    Department of Applied Mathematics III, Universitat Politècnica de Catalunya
    Theses

    Read the abstract Read the abstract  Share Reference managers Reference managers Open in new window

    L'objectiu de la Monitorització de la salut estructural (SHM) és la verificació de l'estat o la salut de les estructures per tal de garantir el seu correcte funcionament i estalviar en el cost de manteniment. El sistema SHM combina una xarxa de sensors connectada a l'estructura amb monitoratge continu i algoritmes específics. Es deriven diferents beneficis de l'aplicació de SHM, on trobem: coneixement sobre el comportament de l'estructura sota diferents operacions i diferents càrregues ambientals , el coneixement de l'estat actual per tal de verificar la integritat del'estructura i determinar si una estructura pot funcionar correctament o si necessita manteniment o substitució i, per tant, reduint els costos de manteniment.El paradigma de la detecció de danys es pot abordar com un problema de reconeixement de patrons (comparació entre les dades recollides de l'estructura sense danys i l'estructura actual, per tal de determinar si hi ha algun canvi) . Hi ha moltes tècniques que poden gestionar el problema. En aquest treball s'utilitzen les dades dels acceleròmetres per desenvolupar aproximacions estadístiques utilitzant dades en temps per a la detecció dels danys en les estructures.La metodologia s'ha dissenyat per a una turbina eòlica off - shore i només s'utilitzen les dades de sortida per detectar els danys. L'excitació de la turbina de vent és induïda pel vent o per les ones del mar.La detecció de danys no és només la comparació de les dades. S'ha dissenyat una metodologia completa per a la detecció de danys en aquest treball. Gestiona dades estructurals, selecciona les dades adequades per detectar danys, i després de tenir en compte les condicions ambientals i operacionals (EOC) en el qual l'estructura està treballant, es detecta el dany mitjançant el reconeixement de patrons.Quan es parla del paradigma de la detecció de danys sempre s'ha de tenir en compte si els sensors estan funcionant correctament. Per això és molt important comptar amb una metodologia que comprova si els sensors estan sans. En aquest treball s'ha aplicat un mètode per detectar els sensors danyats i s'ha insertat en la metodologia de detecció de danys.Aquesta estratègia de detecció de danys s'ha validat en diferents models i estructures reals: en un model de turbina que s'executa en un programa de verificació, i en una torre de laboratori simulant un aerogenerador marí, a més d'alguns models estructurals simples. En aquesta tesi es presenten resultats prometedors per a certs estats d'error predefinits i funcions dels danys.

  • Multivariate data-driven modelling and pattern recognition for damage detection and identification for acoustic emission and acousto-ultrasonics

     Torres Arredondo, Miguel Angel; Tibaduiza Burgos, Diego Alexander; Mcgugan, Malcolm; Toftegaard, Helmuth L.; Borum, K.K.; Mujica Delgado, Luis Eduardo; Rodellar Benede, Jose Julian; Fritzen, Claus-Peter
    Smart materials and structures
    Date of publication: 2013-10
    Journal article

    Read the abstract Read the abstract View View Open in new window  Share Reference managers Reference managers Open in new window

    Different methods are commonly used for non-destructive testing in structures; among others, acoustic emission and ultrasonic inspections are widely used to assess structures. The research presented in this paper is motivated by the need to improve the inspection capabilities and reliability of structural health monitoring (SHM) systems based on ultrasonic guided waves with focus on the acoustic emission and acousto-ultrasonics techniques. The use of a guided wave based approach is driven by the fact that these waves are able to propagate over relatively long distances, and interact sensitively and uniquely with different types of defect. Special attention is paid here to the development of efficient SHM methodologies. This requires robust signal processing techniques for the correct interpretation of the complex ultrasonic waves. Therefore, a variety of existing algorithms for signal processing and pattern recognition are evaluated and integrated into the different proposed methodologies. As a contribution to solve the problem, this paper presents results in damage detection and classification using a methodology based on hierarchical nonlinear principal component analysis, square prediction measurements and self-organizing maps, which are applied to data from acoustic emission tests and acousto-ultrasonic inspections. At the end, the efficiency of these methodologies is experimentally evaluated in diverse anisotropic composite structures.

  • A study of two unsupervised data driven statistical methodologies for detecting and classifying damages in structural health monitoring

     Tibaduiza Burgos, Diego Alexander; Torres Arredondo, Miguel Angel; Mujica Delgado, Luis Eduardo; Rodellar Benede, Jose Julian; Fritzen, Claus-Peter
    Mechanical systems and signal processing
    Date of publication: 2013-12
    Journal article

    Read the abstract Read the abstract View View Open in new window  Share Reference managers Reference managers Open in new window

    This article is concerned with the practical use of Multiway Principal Component Analysis (MPCA), Discrete Wavelet Transform (DWT), Squared Prediction Error (SPE) measures and Self-Organizing Maps (SOM) to detect and classify damages in mechanical structures. The formalism is based on a distributed piezoelectric active sensor network for the excitation and detection of structural dynamic responses. Statistical models are built using PCA when the structure is known to be healthy either directly from the dynamic responses or from wavelet coefficients at different scales representing Time-frequency information. Different damages on the tested structures are simulated by adding masses at different positions. The data from the structure in different states (damaged or not) are then projected into the different principal component models by each actuator in order to obtain the input feature vectors for a SOM from the scores and the SPE measures. An aircraft fuselage from an Airbus A320 and a multi-layered carbon fiber reinforced plastic (CFRP) plate are used as examples to test the approaches. Results are presented, compared and discussed in order to determine their potential in structural health monitoring. These results showed that all the simulated damages were detectable and the selected features proved capable of separating all damage conditions from the undamaged state for both approaches.

  • Feature selection - Extraction methods based on PCA and mutual information to improve damage detection problem in offshore wind turbines

     Zugasti, Ekhi; Mujica Delgado, Luis Eduardo; Anduaga, Javier; Martinez, Fernando
    Conference on Damage Assessment of Structures
    Presentation of work at congresses

    Read the abstract Read the abstract View View Open in new window  Share Reference managers Reference managers Open in new window

    Damage detection problem in Structural Health Monitoring (SHM) is widely studied by many researchers, therefore lots of damage detection algorithms can be found in the literature. Feature Selection / Extraction methods are essential in the accuracy of these algorithms, they provide the suitable data to be used. The main goal of this work is to improve the input data to be the most representative for the damage detection problem. This is done using different Feature Selection / Extraction methods (PCA, UmRMR, and a combination of both). After taking the representative features, the results are tested using a damage detection method; the NullSpace in this case. The data has been collected from a Laboratory Offshore tower model. The different results are compared (different preprocessing vs Raw data) and these show how the correct preselection of the data can improve damage detection.

  • Damage detection by using FBGs and strain field pattern recognition techniques

     Sierra Pérez, Julian; Güemes Gordo, Alfredo; Mujica Delgado, Luis Eduardo
    Smart materials and structures
    Date of publication: 2013
    Journal article

    View View Open in new window  Share Reference managers Reference managers Open in new window

  • Principal component analysis in combination with case-based reasoning for detecting therapeutically correct and incorrect measurements in continuous glucose monitoring systems

     Ruiz Ordoñez, Magda Liliana; Leal, Yenny; Lorencio, Carol; Bondia, Jorge; Mujica Delgado, Luis Eduardo; Vehí6, Josep
    Biomedical signal processing and control
    Date of publication: 2013-11-01
    Journal article

    View View Open in new window  Share Reference managers Reference managers Open in new window

  • A study of two unsupervised data driven statistical methodologies for detecting & classifying damages in structural health monitoring

     Tibaduiza Burgos, Diego Alexander; Torres Arredondo, Miguel Angel; Mujica Delgado, Luis Eduardo; Rodellar Benede, Jose Julian; Fritzen, Claus-Peter
    Mechanical systems and signal processing
    Date of publication: 2013-12-06
    Journal article

    Read the abstract Read the abstract View View Open in new window  Share Reference managers Reference managers Open in new window

    This article is concerned with the practical use of Multiway Principal Component Analysis (MPCA), Discrete Wavelet Transform (DWT), Squared Prediction Error (SPE) measures and Self-Organizing Maps (SOM) to detect and classify damages in mechanical structures. The formalism is based on a distributed piezoelectric active sensor network for the excitation and detection of structural dynamic responses. Statistical models are built using PCA when the structure is known to be healthy either directly from the dynamic responses or from wavelet coefficients at different scales representing Time¿frequency information. Different damages on the tested structures are simulated by adding masses at different positions. The data from the structure in different states (damaged or not) are then projected into the different principal component models by each actuator in order to obtain the input feature vectors for a SOM from the scores and the SPE measures. An aircraft fuselage from an Airbus A320 and a multi-layered carbon fiber reinforced plastic (CFRP) plate are used as examples to test the approaches. Results are presented, compared and discussed in order to determine their potential in structural health monitoring. These results showed that all the simulated damages were detectable and the selected features proved capable of separating all damage conditions from the undamaged state for both approaches.

  • A robust procedure for Damage detection from strain measurements based on Principal Component Analysis

     Güemes Gordo, Alfredo; Sierra Pérez, Julián; Rodellar Benede, Jose Julian; Mujica Delgado, Luis Eduardo
    Key engineering materials
    Date of publication: 2013-04-17
    Journal article

    View View Open in new window  Share Reference managers Reference managers Open in new window

  • Partial least square projection to latent structures (PLS) regression to estimate impact localization in structures

     Ruiz Ordoñez, Magda Liliana; Mujica Delgado, Luis Eduardo; Berjaga Moliné, Xavier; Rodellar Benede, Jose Julian
    Smart materials and structures
    Date of publication: 2013-01-25
    Journal article

    View View Open in new window  Share Reference managers Reference managers Open in new window

  • Design and Validation of a Structural Health Monitoring System for Aeronautical Structures.  Open access

     Tibaduiza Burgos, Diego Alexander
    Defense's date: 2013-01-18
    Department of Applied Mathematics III, Universitat Politècnica de Catalunya
    Theses

    Read the abstract Read the abstract Access to the full text Access to the full text Open in new window  Share Reference managers Reference managers Open in new window

    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 compuesto

  • Access to the full text
    Damage detection in piping systems using pattern recognition techniques  Open access

     Buethe, Inka; Torres-Arredondo, Miguel Angel; Mujica Delgado, Luis Eduardo; Rodellar Benede, Jose Julian; Fritzen, Claus-Peter
    European Workshop on Structural Health Monitoring
    Presentation's date: 2012-07
    Presentation of work at congresses

    Read the abstract Read the abstract Access to the full text Access to the full text Open in new window  Share Reference managers Reference managers Open in new window

    The interest in the propagation of ultrasound waves in pipe-like solid waveguides arises out of several areas of the structural health monitoring (SHM) community for the detection, localization and assessment of defects as well as the prediction of remaining life in civil, mechanical, aeronautic and aerospace structures. SHM premise offers a continuous observation of the structural integrity of operational systems. This is particularly convenient, therefore, for the reduction of time and cost for maintenance without decreasing the level of safety. Some practical applications are the monitoring of pipework in gas and oil industries, suspension bridge cables, nuclear fuel cladding tubes, etc. This paper describes an approach for SHM using guided waves in pipe-like structures in terms of a pattern recognition problem. The formalism is based on a distributed piezoelectric sensor network for the detection of structural dynamic responses. Several methods for signal filtration, feature selection and extraction, and data compression of the recorded time histories are discussed and evaluated. Principal Component Analysis (PCA), Non-Linear PCA (NLPCA) and Wavelet Transform are among them. Additionally, the different clusters, corresponding to each damage level are visualized with the help of Self Organizing Maps (SOM). Tests were performed on a piping system where the properties of the proposed methods are compared and appraised with experimental pitch-catch signals between the pristine and the damaged structure.

  • Access to the full text
    Damage assessment in a stiffened composite panel using non-linear data-driven modelling and ultrasonic guided waves  Open access

     Torres-Arredondo, Miguel Angel; Tibaduiza Burgos, Diego Alexander; Mujica Delgado, Luis Eduardo; Rodellar Benede, Jose Julian; Fritzen, Claus-Peter
    International Symposium on NDT in Aerospace
    Presentation's date: 2012-11
    Presentation of work at congresses

    Read the abstract Read the abstract Access to the full text Access to the full text Open in new window  Share Reference managers Reference managers Open in new window

    Structural components made of composite materials are being used more often in aerospace and aeronautic structures due to their well-known properties such as high mass specific stiffness and strength. However, their application also increases the analysis complexity of such structures. Structural health monitoring (SHM) systems for these structures aim to determine the status of the system in real time such that a longer safe life and lower operational costs can be guaranteed. On that account, this paper is concerned with the experimental validation of a structural health monitoring methodology where a damage detection and classification scheme based on an acousto-ultrasonic (AU) approach is applied to a composite panel incorporating stiffening elements using a piezoelectric active sensor network in conjunction with time-frequency multiresolution analysis and non-linear feature extraction. Therefore, structural dynamic responses from the simplified aircraft composite skin panel are collected and signal features are then extracted with a signal processing and data fusion methodology in terms of the wavelet transform technique and hierarchical non-linear principal component analysis. A critical comparison with linear feature extraction methods indicates that the proposed method outperforms the traditional linear methods for the purpose of damage classification. Additionally, results show that all the damages were detectable and classifiable, and the selected features proved capable of separating all damage conditions from the undamaged state.

  • Access to the full text
    Principal component analysis vs. independent component analysis for damage detection  Open access

     Tibaduiza Burgos, Diego Alexander; Mujica Delgado, Luis Eduardo; Anaya, Maribel; Rodellar Benede, Jose Julian; Güemes, Alfredo
    European Workshop on Structural Health Monitoring
    Presentation's date: 2012-07
    Presentation of work at congresses

    Read the abstract Read the abstract Access to the full text Access to the full text Open in new window  Share Reference managers Reference managers Open in new window

    In previous works, the authors showed advantages and drawbacks of the use of PCA and ICA by separately. In this paper, a comparison of results in the application of these methodologies is presented. Both of them exploit the advantage of using a piezoelectric active system in different phases. An initial baseline model for the undamaged structure is built applying each technique to the data collected by several experiments. The current structure (damaged or not) is subjected to the same experiments and the collected data are projected into the models. In order to determine whether damage exists or not in the structure, the projections into the first and second components using PCA and ICA are depicted graphically. A comparison between these plots is performed analyzing differences and similarities, advantages and drawbacks. To validate the approach, the methodology is applied in two sections of an aircraft wing skeleton powered with several PZTs transducers.

  • Independent component analysis for detecting damages on aircraft wing skeleton

     Tibaduiza Burgos, Diego Alexander; Mujica Delgado, Luis Eduardo; Anaya, Maribel; Rodellar Benede, Jose Julian; Güemes, Alfredo
    European Conference on Structural Control
    Presentation's date: 2012-06
    Presentation of work at congresses

    View View Open in new window  Share Reference managers Reference managers Open in new window

  • Access to the full text
    Damage detection using robust fuzzy principal component analysis  Open access

     Gharibnezhad, Fahit; Mujica Delgado, Luis Eduardo; Rodellar Benede, Jose Julian; Fritzen, Claus-Peter
    European Workshop on Structural Health Monitoring
    Presentation's date: 2012-07
    Presentation of work at congresses

    Read the abstract Read the abstract Access to the full text Access to the full text Open in new window  Share Reference managers Reference managers Open in new window

    In this work Robust Fuzzy Principal Component Analysis (RFPCA) is used and compared with comparing with classical Principal Component Analysis (PCA) to detect and classify damages. It has been proved that the RFPCA method achieves better result mainly because it is more compressible than classical PCA and also carries more information, hence not only it can distinguish the healthy structure from the damaged structure much sharper than the traditional counterparts but also in some cases traditional PCA is incapable of discerning the pristine from damaged structure. This work involves experimental results using pipe-like structure powered by a piezoelectric actuators and sensors.

  • A robust procedure for damage detection from strain measurements based on principal component analysis

     Güemes Gordo, Alfredo; Sierra Pérez, Julián; Rodellar Benede, Jose Julian; Mujica Delgado, Luis Eduardo
    Asia-Pacific Workshop on Structural Health Monitoring
    Presentation's date: 2012-12
    Presentation of work at congresses

    Read the abstract Read the abstract View View Open in new window  Share Reference managers Reference managers Open in new window

    FBGs are excellent strain sensors, because of its low size and multiplexing capability. Tens to hundred of sensors may be embedded into a structure, as it has already been demonstrated. Nevertheless, they only afford strain measurements at local points, so unless the damage affects the strain readings in a distinguishable manner, damage will go undetected. This paper show the experimental results obtained on the wing of a UAV, instrumented with 32 FBGs, before and after small damages were introduced. The PCA algorithm was able to distinguish the damage cases, even for small cracks. Principal Component Analysis (PCA) is a technique of multivariable analysis to reduce a complex data set to a lower dimension and reveal some hidden patterns that underlie.

  • Atypical lymphoid cells detection and classification using mathematical morphology and fuzzy clustering on digital blood image analysis

     Alferez Baquero, Edwin Santiago; Merino, Ana; Ruiz Ordoñez, Magda Liliana; Mujica Delgado, Luis Eduardo; Rodellar Benede, Jose Julian
    International journal of laboratory hematology
    Date of publication: 2012-06
    Journal article

    Read the abstract Read the abstract View View Open in new window  Share Reference managers Reference managers Open in new window

    The robust segmentation methodology described allows to extract information and measurements about diferent regions of the lymphoid cells, reaching a high precision in the classification of HCL cells. The addition of descriptors and the use of new artificial intelligence techniques will improve the method in order to extend their application to the morphological feature classification of other PB lymphoid cells in the different lymphoid neoplasias.

  • Damage classification in structural health monitoring using principal component analysis and self-organizing maps

     Tibaduiza Burgos, Diego Alexander; Mujica Delgado, Luis Eduardo; Rodellar Benede, Jose Julian
    Structural control & health monitoring
    Date of publication: 2012-12-13
    Journal article

    View View Open in new window  Share Reference managers Reference managers Open in new window

  • Access to the full text
    Damage detection index based on statistical inference and PCA  Open access

     Mujica Delgado, Luis Eduardo; Ruiz Ordoñez, Magda Liliana; Pozo Montero, Francesc; Rodellar Benede, Jose Julian
    International Workshop on Structural Health Monitoring
    Presentation's date: 2011-09-12
    Presentation of work at congresses

    Read the abstract Read the abstract Access to the full text Access to the full text Open in new window  Share Reference managers Reference managers Open in new window

    This paper is focused on the development of new estimators propounding if someone statistical law could estimate or infer a system without damage knowing its reliability. This new measurement considers each experiment, and consequently, each projection to the PCA model as a random variable. An in-depth statistical analysis is performed for SHM. PCA projections are obtained from the undamaged structure (baseline projection). If these projections are considered as the set of possible results (population), then the new projections from the current structure (healthy or not) are defined as random samples. Therefore, the probability distribution of the baseline projection can be found. This new distribution can make an inference about the state of the structure and determine if there is damage in it. Consequently, the relative likelihood of each new projection is determined. If the new projection is strongly related with the population, then the structure is healthy. Otherwise, the relation indicates the damage.

  • Access to the full text
    Damage detection in the presence of outliers based on robust PCA  Open access

     Gharibnezhad, Fahit; Mujica Delgado, Luis Eduardo; Rodellar Benede, Jose Julian
    International Conference on Structural Dynamics
    Presentation's date: 2011-07-12
    Presentation of work at congresses

    Read the abstract Read the abstract Access to the full text Access to the full text Open in new window  Share Reference managers Reference managers Open in new window

    Identification of outliers in samples from univariate and multivariate populations has received considerable attention over the past few decades. Presence of outliers has undeniable effects on the results of statistical methods such as Principal Component Analysis (PCA). Outliers, anomalous observations, can affect the variance and covariance as vital parts of PCA method. In statistical sense outliers are samples from a different population than the data majority. An effective way to deal with this problem is to apply a robust, i.e. not sensitive to outliers, variant of PCA. In this work a robust PCA method is used instead of classical PCA in order to construct a model using data with outliers to detect and distinguish damages in structures. The comparisons of the results shows that, the use some indexes based on the robust model,can distinguish the damages much better than using classical one, and even in many cases allows the detection where classic PCA is not able to discern between damaged and non-damaged structure. This work involves experiments with an aircraft turbine blade using piezoelectric transducers as sensors and actuators and simulated damages.

  • Damage detection using Andrew plots  Open access

     Gharibnezhad, Fahit; Mujica Delgado, Luis Eduardo; Rodellar Benede, Jose Julian
    International Workshop on Structural Health Monitoring
    Presentation's date: 2011-09-19
    Presentation of work at congresses

    Read the abstract Read the abstract Access to the full text Access to the full text Open in new window  Share Reference managers Reference managers Open in new window

    In current work, Andrew plot is used as a new index to detect any probable damage in the structure. At the first step, using piezoelectric actuators and sensors, appropriate lamb wave is propagated and received through the structure. Then Principal Component Analysis is applied to the recorded data and prepares necessary data for Andrew curves. Andrew plots are depicted based on calculated principal components. It has been shown that comparing Andrew curves from baseline, structure without damage, with current statues of structure can identify any probable damages in the structure.

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

     Tibaduiza Burgos, Diego Alexander; Mujica Delgado, Luis Eduardo; Rodellar Benede, Jose Julian
    International Workshop on Structural Health Monitoring
    Presentation's date: 2011-09-13
    Presentation of work at congresses

    View View Open in new window  Share Reference managers Reference managers Open in new window

  • Comparison of two robust PCA methods for damage detection in the presence of outliers

     Gharibnezhad, Fahit; Mujica Delgado, Luis Eduardo; Rodellar Benede, Jose Julian
    Journal of physics: conference series
    Date of publication: 2011-07-19
    Journal article

    Read the abstract Read the abstract View View Open in new window  Share Reference managers Reference managers Open in new window

    Statistical methods such as Principal Component Analysis (PCA) are suffering from contaminated data. For instance, variance and covariance as vital parts of PCA method are sensitive to anomalous observation called outliers. Outliers, who are usually, appear due to experimental errors, are observations that lie at a considerable distance from the bulk of the observations. An effective way to deal with this problem is to apply a robust, i.e. not sensitive to outliers, variant of PCA. In this work, two robust PCA methods are used instead of classical PCA in order to construct a model using data in presence of outliers to detect and distinguish damages in structures. The comparisons of the results shows that, the use of the mentioned indexes based on the robust models, distinguish the damages much better than using classical one, and even in many cases allows the detection where classic PCA is not able to discern between damaged and non-damaged structure. In addition, two robust methods are compared with each other and their features are discussed. This work involves experiments with an aircraft turbine blade using piezoelectric transducers as sensors and actuators and simulated damages.

  • Comparison of several methods for damage localization using indices and contributions based on PCA

     Tibaduiza Burgos, Diego Alexander; Mujica Delgado, Luis Eduardo; Rodellar Benede, Jose Julian
    Journal of physics: conference series
    Date of publication: 2011-07-19
    Journal article

    Read the abstract Read the abstract View View Open in new window  Share Reference managers Reference managers Open in new window

    In previous works by the authors, it was shown the advantages of applying an active piezoelectric system in combination with Principal Component Analysis and Neural Networks as methodology for damage detection in structures. An active piezoelectric system considers the advantage of using piezoelectric transducers (PZT's) as actuator as well as sensor. In each phase of the diagnosis procedure, one PZT is used as actuator (a known electrical signal is applied) and the others are used as sensors (collecting the wave propagated through the structure at di erent points). An initial baseline model for undamaged structure is built applying Principal Component Analysis (PCA) to the data collected by several experiments. Current structure (damaged or not) is subjected to the same experiments, and the collected data are projected in the PCA model. In this paper, two indices are used to detect damages; these indices are calculated from the information obtained from the projection of the experiments in the PCA model (baseline). Besides the localization is performed using ve di erent methods. These methods are based on the contribution of each sensor to each index, in this way, according to these contributions, the damage can be localized. The combination of all indices and all contributions (a total of 2 x 5) are analyzed and compared. To validate the approach, the methods are applied to an aluminum plate which is instrumented with several PZT's.

  • Access to the full text
    Data-driven multiactuator piezoelectric system for structural damage localization  Open access

     Mujica Delgado, Luis Eduardo; Tibaduiza Burgos, Diego Alexander; Rodellar Benede, Jose Julian
    World Conference on Structural Control
    Presentation's date: 2010-07-14
    Presentation of work at congresses

    Read the abstract Read the abstract Access to the full text Access to the full text Open in new window  Share Reference managers Reference managers Open in new window

    In initial studies, it has been shown that the combination of Principal Component Analysis (PCA) and the contribution plots of Q and T2-statistics can be considered an efficient technique to detect, distinguish and localize damages in structures that are equipped with several piezoelectric transducers (PZT’s). In those previous works, the specimen (aircraft turbine blade) was excited using just one of the set of PZT’s bounded on the surface. This paper studies the advantage of using the whole set of PZT’s as actuators as well as sensors. An active piezoelectric system is developed. At each phase of the diagnosis procedure, one PZT is used as actuator (a known electrical signal is applied) and the others are used as sensors (collecting the wave propagated through the structure at different points). A data-driven model for undamaged structure is built applying PCA to the data collected by several experiments. The current structure (damaged or not) is subjected to the same experiments, and the collected data are projected into the baseline PCA model. The indices Q, T2, ϕ and I determine whether the structure is healthy or not. In addition, the contribution of each sensor to these indices supplies information about the localization of the damage.

  • Active piezoelectric system using PCA

     Tibaduiza Burgos, Diego Alexander; Mujica Delgado, Luis Eduardo; Güemes Gordo, Alfredo; Rodellar Benede, Jose Julian
    European Workshop on Structural Health Monitoring
    Presentation's date: 2010-07-01
    Presentation of work at congresses

    Read the abstract Read the abstract View View Open in new window  Share Reference managers Reference managers Open in new window

    In previous work by the authors, an aircraft turbine blade was used to show that T and Q-statistics formulation based in Principal Components Analysis (PCA) are successful indices to detect and distinguish damages. In this paper authors consider the advantage of using PZT’s as actuator as well as sensor. An active piezoelectric system is developed. In each phase of the diagnosis procedure, one PZT’s is used as actuator (a known electrical signal is applied) and the others are used as sensors (collecting the wave propagated through the structure at different points). An initial baseline model for undamaged structure is built applying Principal Components Analysis (PCA) to the data collected by several experiments. Current structure (damaged or not) is subjected to the same experiments, and the collected data are projected into de PCA model. Two of these projections and the damage indices (T-statistic and Q-statistic) by each phase are used as features for the final classification.

  • Q-statistic and T2-statistic PCA-based measures for damage assessment in structures

     Mujica Delgado, Luis Eduardo; Rodellar Benede, Jose Julian; Fernandez, Antonio; Güemes, Alfredo
    Structural health monitoring: an international journal
    Date of publication: 2010-11-23
    Journal article

    View View Open in new window  Share Reference managers Reference managers Open in new window

  • Extended PCA visualisation of system damage features under environmental and operational variations

     Mujica Delgado, Luis Eduardo; Rodellar Benede, Jose Julian; Vehí9 Casellas, Josep; Worden, K; Staszewski, W
    Proceedings of SPIE, the International Society for Optical Engineering
    Date of publication: 2009-04
    Journal article

    View View Open in new window  Share Reference managers Reference managers Open in new window

  • Contribution plots on PCA based indices for damage identification on structures

     Mujica Delgado, Luis Eduardo; Ruiz, Magda; Guemes, Alfredo; Rodellar Benede, Jose Julian
    ECCOMAS Thematic Conference Smart Structures and Materials
    Presentation's date: 2009
    Presentation of work at congresses

    View View Open in new window  Share Reference managers Reference managers Open in new window

  • Optimization of PCA models for SHM applications with multiple sensors

     Mujica Delgado, Luis Eduardo; Ruiz, Magda; Guemes, Alfredo; Rodellar Benede, Jose Julian
    International Workshop on Structural Health Monitoring
    Presentation's date: 2009
    Presentation of work at congresses

    View View Open in new window  Share Reference managers Reference managers Open in new window

  • Access to the full text
    Multiway partial least square (MPLS) to estimate impact localization in structures  Open access

     Mujica Delgado, Luis Eduardo; Ruiz Ordoñez, Magda Liliana; Berjaga Moliné, Xavier; Rodellar Benede, Jose Julian
    IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes
    Presentation's date: 2009-07-03
    Presentation of work at congresses

    Read the abstract Read the abstract Access to the full text Access to the full text Open in new window  Share Reference managers Reference managers Open in new window

    This paper presents results from the application of Multiway Partial Least Square (MPLS) as a regressor tool in order to estimate the localization of impacts in an aircraft structure. MPLS is a technique that maximizes the covariance between the predictor matrix X and the predicted matrix Y for each component of the space. The structure can be considered as a small scale version of part of a wing aircraft. 574 experiments were performed impacting the wing over its surface and receiving vibration signals from nine sensors. Experiments are divided in four groups depending on their localization and probability of occurrence. A PLS model is build using three of these groups and tested using the remaining group. Results are presented, discussed and compared with results of other methods.

  • ESTRUCTURAS AERONAUTICAS INTELIGENTES: DESARROLLO Y VALIDACION DE TECNICAS DE DETECCION DE DEFECTOS BASADAS EN RECONOCIMIENTO DE PAT

     Tibaduiza Burgos, Diego Alexander; Mujica Delgado, Luis Eduardo; Gharibnezhad, Fahit; Rodellar Benede, Jose Julian
    Participation in a competitive project

     Share

  • Control, dinàmica i aplicacions (CODALAB)

     Mantecon Baena, Juan Antonio; Galvis Restrepo, Eduard; Ikhouane, Fayçal; Rubió Massegú, Josep; Vidal Segui, Yolanda; Rossell Garriga, Josep Maria; Palacios Quiñonero, Francisco; Pujol Vazquez, Gisela; Pozo Montero, Francesc; Mañosa Fernández, Víctor; Tibaduiza Burgos, Diego Alexander; Mujica Delgado, Luis Eduardo; Acho Zuppa, Leonardo; Gharibnezhad, Fahit; Ismail Abdelkareem Moustafa, Mohammed; Rodellar Benede, Jose Julian
    Participation in a competitive project

     Share

  • A review of impact damage detection in structures using strain data

     Mujica Delgado, Luis Eduardo; Rodellar Benede, Jose Julian; Vehí Casellas, Josep Maria
    International journal of COMADEM
    Date of publication: 2008
    Journal article

     Share Reference managers Reference managers Open in new window

  • Impact damage detection in aircraft composites using knowledge-based reasoning

     Mujica Delgado, Luis Eduardo; Vehí6, Josep; Staszewski, W; Worden, K
    Structural health monitoring: an international journal
    Date of publication: 2008-09-11
    Journal article

    View View Open in new window  Share Reference managers Reference managers Open in new window

  • Multivariate statistics process control for dimensionality reduction in structural assessment

     Mujica Delgado, Luis Eduardo; Vehí5, Josep; Ruiz Ordoñez, Magda Liliana; Verleysen, M; Staszewski, W; Worden, K
    Mechanical systems and signal processing
    Date of publication: 2008-01-01
    Journal article

    View View Open in new window  Share Reference managers Reference managers Open in new window

  • Experimental applications of a case based reasoning method for structural damage assessment

     Mujica Delgado, Luis Eduardo; Rodellar Benede, Jose Julian
    World forum on Smart Material and Smart Structures Technology
    Presentation of work at congresses

     Share Reference managers Reference managers Open in new window

  • A case based reasoning approach for damage assessment in smart structures

     Mujica Delgado, Luis Eduardo; Rodellar Benede, Jose Julian
    III ECCOMAS Thematic Conference on Smart Structures and Materials
    Presentation of work at congresses

     Share Reference managers Reference managers Open in new window

  • Non-destructive testing for assessing structures by using soft-computing

     Mujica Delgado, Luis Eduardo; Vehí7 Caselles, Josep; Rodellar Benede, Jose Julian
    Date of publication: 2006-09-13
    Book chapter

    View View Open in new window  Share Reference managers Reference managers Open in new window

  • Two approaches to structural damage identification: Model updating versus soft computing

     Kolakowski, Przemyslaw; Mujica Delgado, Luis Eduardo; Vehí7 Caselles, Josep
    Journal of intelligent material systems and structures
    Date of publication: 2006-01-01
    Journal article

    View View Open in new window  Share Reference managers Reference managers Open in new window

  • A hybrid approach of knowledge-based reasoning for structural assessment

     Mujica Delgado, Luis Eduardo; Vehí8, J; Rodellar Benede, Jose Julian; Kolakowski, P
    Smart materials and structures
    Date of publication: 2005-11
    Journal article

    View View Open in new window  Share Reference managers Reference managers Open in new window

  • A hybrid system combining sel organizing maps with case based reasoning in structural assessment

     Mujica Delgado, Luis Eduardo; Vehí8, J; Rodellar Benede, Jose Julian
    Frontiers in artificial intelligence and applications
    Date of publication: 2005-10
    Journal article

     Share Reference managers Reference managers Open in new window

  • A hybrid system combining self organizing maps with case based reasoning in structural assessment

     Mujica Delgado, Luis Eduardo; Vehí7 Caselles, Josep; Rodellar Benede, Jose Julian
    Date of publication: 2005-10-01
    Book chapter

    View View Open in new window  Share Reference managers Reference managers Open in new window

  • Detección de impactos mediante razonamiento basado en conocimiento: aplicación a una sección de ala de avión

     Mujica Delgado, Luis Eduardo; Vehí1 Casellas, Josep; Rodellar Benede, Jose Julian
    I Seminario de Aplicaciones Industriales de Control Avanzado
    Presentation of work at congresses

     Share Reference managers Reference managers Open in new window

  • Comparison of two software tools for damage identification: Gradient-based vs. case-based aproach

     Kolakowski, Przemyslaw; Mujica Delgado, Luis Eduardo; Vehí7 Caselles, Josep
    Key engineering materials
    Date of publication: 2005-09-01
    Journal article

    View View Open in new window  Share Reference managers Reference managers Open in new window

  • Experiences in exploiting data: Selected problems in application domains

     Meléndez, Joaquim; Colomer, Joan; López, Beatriz; Vehí6, Josep; Pous, Carles; Ruiz Ordoñez, Magda Liliana; Mujica Delgado, Luis Eduardo
    Date of publication: 2004-02-06
    Book chapter

    View View Open in new window  Share Reference managers Reference managers Open in new window

  • Hybrid knowledge based reasoning approach for structural assessment

     Mujica Delgado, Luis Eduardo; Vehí1 Casellas, Josep; Rodellar Benede, Jose Julian; Garcia, O; Kolakowski, P
    Second European Workshop on Structural Health Monitoring
    Presentation of work at congresses

     Share Reference managers Reference managers Open in new window