Moving towards preventive maintenance in wind turbine structural control and health monitoring

This work has been partially funded by the Spanish Agencia Estatal de Investigación (AEI)—Ministerio de Economía, Industria y Competitividad (MINECO), and the Fondo Europeo de Desarrollo Regional (FEDER) through the research projects PID2021-122132OB-C21 and TED2021- 129512B-I00; and by the Generalitat de Catalunya through the research projects 2021-SGR-01044.Peer ReviewedPostprint (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

Online structural damage classification methodology for offshore wind turbine foundations using data stream analysis

Structural health monitoring (SHM) of wind turbines is crucial to improve maintenance and extend their lifespan. This study develops an online data analysis methodology using data stream analysis to classify damage in the links of an offshore wind turbine foundation. The methodology is validated using a laboratory-scaled jacket-type wind turbine foundation structure. 2460 measurements of the healthy structure were acquired, and a 5mm crack was applied to four different links to determine the four unhealthy classes. 820 measurements were taken for each of the unhealthy structures, resulting in a dataset with 5740 instances. As this is an imbalanced multiclass classification problem, a random sampler approach was used to treat the data. The only data obtained was from eight triaxial accelerometers distributed throughout the structure. Three different tree-based stream data classifiers were compared: Hoeffding Tree classifier, Extremely Fast Decision Tree classifier, and Hoeffding Adaptive Tree classifier. Each classification model underwent a tuning parameter procedure, and high values of the receiving operating characteristic area under the curve (ROC AUC) metric were achieved as a result. It is important to note that stream learning differs from batch learning.Peer ReviewedPostprint (published version

Diseño de un prototipo de impresora 3D que aplica la técnica de prototipado rápido modelado por deposición fundida

1 recurso en línea (205 páginas) : ilustraciones color, figuras, tablas.La tecnología de impresión en tercera dimensión es capaz de fabricar piezas en diferentes tipos de materiales, por técnicas como solidificación a partir del uso de rayos láser, Luz ultravioleta, inyección de partículas, adhesivos, deposición fundida entre otros. En la técnica de prototipado rápido modelado por deposición fundida un filamento plástico se desenvuelve de un rollo y alimenta una boquilla de extrusión. La boquilla se calienta para fundir el plástico y tiene un mecanismo que permite controlar el flujo del plástico fundido. La boquilla se monta a un cabezal móvil que puede moverse en direcciones X, Y y Z y deposita un pequeño cordón o gota de plástico para formar cada capa. El plástico endurece al contacto con la capa anterior y la exposición al medio.El proyecto plantea dos fases para su desarrollo, la primera contempla el diseño de una impresora 3d que aplica la técnica de prototipado rápido modelado por deposición fundida y la segunda la implementación de un prototipo, utilizando herramientas de automatización y control, posicionamiento mecánico, informática, electrónica, software entre otras.El estudio parte de la identificación de las diversas ramas del conocimiento y de las diferentes técnicas de prototipado rápido que existen, comparando una a una y seleccionado la alternativa más apropiada de acuerdo con los recursos técnicos y financieros disponibles, posteriormente a partir de los resultados de la fase de diseño se hará la implementación del sistema.Bibliografía : páginas 154-157PregradoIngeniero Electromecánic

Damage localization using pattern recognition techniques and statistical dissimilarity analysis

The increasing expansion of standards and regulations aimed to guarantee the safe operation of different types of structures and materials ---which are extensively used in buildings, airplanes, transformers, machinery, etc.--- has driven the development and implementation of systems intended to supervise the state of the structure so critical failures may be predicted and avoided. Nevertheless many of those systems are expensive and require the structure to be inactive in order to evaluate its state, so it is frequently necessary to use external equipment and specialized technician teams, producing therefore a high interest in many sectors of industry, military and academic fields on supporting structural health monitoring (SHM) projects to develop new alternative systems. Among the proposals, the ones that includes integrated sensor networks systems are becoming increasingly popular due to their potential to perform the structure health evaluation even during these equipment are still in use, which results in a significant reduction in the economical and productive impact that other systems cause and the possibility to introduce new evaluation techniques that exploit the sensor and programming flexibility. This paper introduces a SHM system aimed to locate damages on metallic and composite material structures. For this purpose, a methodology is applied in this work that combines statistical analysis of dissimilarity with pattern recognition on the data recollected from the piezoelectric sensors network distributed on the structure surface. This methodology is tested in an aluminum plate instrumented with eight piezoelectric sensors and some masses added to the structure in order to emulate changes in the structure due to a damage. Results shows that it is possible to locate all damages.Postprint (published version

Data classification methodology for electronic noses using uniform manifold approximation and projection and extreme learning machine

The classification and use of robust methodologies in sensor array applications of electronic noses (ENs) remain an open problem. Among the several steps used in the developed methodologies, data preprocessing improves the classification accuracy of this type of sensor. Data preprocessing methods, such as data transformation and data reduction, enable the treatment of data with anomalies, such as outliers and features, that do not provide quality information; in addition, they reduce the dimensionality of the data, thereby facilitating the tasks of a machine learning classifier. To help solve this problem, in this study, a machine learning methodology is introduced to improve signal processing and develop methodologies for classification when an EN is used. The proposed methodology involves a normalization stage to scale the data from the sensors, using both the well-known min-max approach and the more recent mean-centered unitary group scaling (MCUGS). Next, a manifold learning algorithm for data reduction is applied using uniform manifold approximation and projection (UMAP). The dimensionality of the data at the input of the classification machine is reduced, and an extreme learning machine (ELM) is used as a machine learning classifier algorithm. To validate the EN classification methodology, three datasets of ENs were used. The first dataset was composed of 3600 measurements of 6 volatile organic compounds performed by employing 16 metal-oxide gas sensors. The second dataset was composed of 235 measurements of 3 different qualities of wine, namely, high, average, and low, as evaluated by using an EN sensor array composed of 6 different sensors. The third dataset was composed of 309 measurements of 3 different gases obtained by using an EN sensor array of 2 sensors. A 5-fold cross-validation approach was used to evaluate the proposed methodology. A test set consisting of 25% of the data was used to validate the methodology with unseen data. The results showed a fully correct average classification accuracy of 1 when the MCUGS, UMAP, and ELM methods were used. Finally, the effect of changing the number of target dimensions on the reduction of the number of data was determined based on the highest average classification accuracy.This work was funded in part with resources from the Fondo de Ciencia, Tecnología e Innovación (FCTeI) del Sistema General de Regalías (SGR) from Colombia. The authors express their gratitude to the Administrative Department of Science, Technology, and Innovation–Colciencias with the grant 779–“Convocatoria para la Formación de Capital Humano de Alto Nivel para el Departamento de Boyacá 2017” for sponsoring the research presented herein. This study has been partially funded by the Spanish Agencia Estatal de Investigación (AEI)-Ministerio de Economía, Industria y Competitividad (MINECO), and the Fondo Europeo de Desarrollo Regional (FEDER) through research projects DPI2017-82930-C2-1-R and PGC2018-097257-B-C33; and by the Generalitat de Catalunya through research projects 2017-SGR-388 and 2017-SGR-1278.Peer ReviewedPostprint (published version

Metodología diseño de secadores híbridos solar- biomasa para frutas

This project aims to create a solution to the waste of fruit that exists worldwide, in addition, it aims to reduce the CO2 emissions emitted by the fossil fuels that are currently burning. To carry out the analysis of the hybrid solar biomass dryer, we propose a simulation with simulation tools using computational fluid dynamics - CFD. As results of the simulations, we seek to compare the velocity and temperature profiles within the dryer with and without product, against experimental measurements.El artículo presenta los resultados del proyecto que tuvo como objetivo crear una alternativa de solución al desperdicio de fruta, asunto que se vive a nivel mundial, y al de emisiones de CO2 por causa de combustibles fósiles que actualmente se queman. Por lo anterior, se realiza una metodología para el diseño y construcción de un secador hibrido solar­biomasa planteándose una simulación con herramientas computacionales utilizando métodos numéricos, y dinámica computacional de fluidos ­CFD (computational fluid dynamics).  Como perspectiva de investigación se proponen comparaciones de los perfiles de velocidad y temperatura con y sin producto de secado simulados, contra mediciones experimentales

Attention-based deep recurrent neural network to forecast the temperature behavior of an electric arc furnace side-wall

Structural health monitoring (SHM) in an electric arc furnace is performed in several ways. It depends on the kind of element or variable to monitor. For instance, the lining of these furnaces is made of refractory materials that can be worn out over time. Therefore, monitoring the temperatures on the walls and the cooling elements of the furnace is essential for correct structural monitoring. In this work, a multivariate time series temperature prediction was performed through a deep learning approach. To take advantage of data from the last 5 years while not neglecting the initial parts of the sequence in the oldest years, an attention mechanism was used to model time series forecasting using deep learning. The attention mechanism was built on the foundation of the encoder–decoder approach in neural networks. Thus, with the use of an attention mechanism, the long-term dependency of the temperature predictions in a furnace was improved. A warm-up period in the training process of the neural network was implemented. The results of the attention-based mechanism were compared with the use of recurrent neural network architectures to deal with time series data, such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The results of the Average Root Mean Square Error (ARMSE) obtained with the attention-based mechanism were the lowest. Finally, a variable importance study was performed to identify the best variables to train the model.This work has been funded by the Colombian Ministry of Science through grant number 786, “Convocatoria para el registro de proyectos que aspiran a obtener beneficios tributarios por inversión en CTeI”. This work has been partially funded by the Spanish Agencia Estatal de Investigación (AEI)—Ministerio de Economía, Industria y Competitividad (MINECO), and the Fondo Europeo de Desarrollo Regional (FEDER) through the research project DPI2017-82930-C2-1-R, and by the Generalitat de Catalunya through the research project 2017-SGR-388.Peer ReviewedPostprint (published version

EVALUACIÓN DE LA RESISTENCIA A LA OXIDACIÓN DE PELICULAS DE Ti-Zr-Si-N PRODUCIDAS POR COSPUTTERING

Películas delgadas de Ti-Zr-Si-N se depositaron sobre sustratos de acero inoxidable 316 L usando la técnica de co-sputtering reactivo. El análisis de la estructura se realizó mediante difracción de rayos X (DRX), el análisis morfológico se realizó por microscopía electrónica de barrido (MEB) y microscopía óptica 3D. Los estudios de oxidación cíclica se realizaron en un horno en ambiente seco con un total de 300 ciclos, cada uno con una tasa de calentamiento de 46 °C/min hasta lograr una temperatura de 600 °C, la cual fue sostenida durante 30 min y finalmente enfriado a 20 °C/min. Los recubrimientos mejoraron la resistencia a la corrosión del acero inoxidable a altas temperaturas en un 30% debido a la formación de óxidos protectores. En este trabajo se discute el mecanismo de corrosión por oxidación cíclica para los recubrimientos realizados

Topology optimization method of continuous structures based on bacterial chemotaxis

ilustraciones, fotografíasEn este proyecto, la quimiotaxis de bacterias es utilizada para resolver problemas de optimización topológica estructural particularmente en estructuras continuas bidimensionales sometidas a cargas puntuales. Se desarrolló un algoritmo de optimización topológica denominado “Algoritmo de Optimización Topológica Basado en Quimiotaxis de Bacterias BCBTOA-2" que describe la estrategia quimiotáxica como mecanismo simulado para retirar material en una estructura con el ánimo de minimizar energía de deformación y maximizar rigidez. Se solucionaron algunos problemas evidenciados en optimización topológica como los llamados tableros de ajedrez y la dependencia de malla a través de un esquema de regularización basado en quimiotaxis de bacterias. A continuación, el algoritmo es aplicado a distintas configuraciones de vigas bidimensionales para mostrar su rendimiento y versatilidad; se aplicaron métricas de desempeño relacionadas con el valor de la energía de deformación total de una estructura y el número de iteraciones necesarias para que el algoritmo converja, esto con el _n de comparar el método propuesto frente a otros métodos de optimización topológica como son el método OC-SIMP y el método Soft BESO. (Texto tomado de la fuente)In this project, bacterial chemotaxis is used to solve structural topology optimization problems, especially in two-dimensional continuous structures subjected to point loads. A topology optimization algorithm called Bacterial-Chemotaxis-Based Topology Optimization Algorithm 2 BCBTOA-2”was developed to describes the chemotactic strategy as a simulation of material removal in a structure. The algorithm minimizes compliance and maximizes stiffness, so, the algorithm is applied to various two-dimensional configurations of beams to show its efficiency, performance and versatility. Common problems in topology optimization such as checkerboards and mesh dependence were solved through a regularization scheme based on bacterial chemotaxis. Then, the BCBTOA-2 algorithm is evaluated determining its effectiveness and computational performance. We apply performance metrics related to the value of the total compliance of a structure and the number of iterations required for the algorithm to converge, this in order to compare the proposed method, which is competitive with other methods of topology optimization as the OC-SIMP method and the Soft BESO method. (Text taken from source)MaestríaMagíster en ingeniería - ingeniería Mecánic