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  • A combined methodology of adaptive neuro-fuzzy inference system and genetic algorithm for short-term energy forecasting

     Kampouropoulos, Konstantinos; Andrade Rengifo, Fabio; Garcia Espinosa, Antonio; Romeral Martinez, Jose Luis
    Advances in Electrical and Computer Engineering
    Date of publication: 2014-02
    Journal article

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    This document presents an energy forecast methodology using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Genetic Algorithms (GA). The GA has been used for the selection of the training inputs of the ANFIS in order to minimize the training result error. The presented algorithm has been installed and it is being operating in an automotive manufacturing plant. It periodically communicates with the plant to obtain new information and update the database in order to improve its training results. Finally the obtained results of the algorithm are used in order to provide a shortterm load forecasting for the different modeled consumption processes.

  • Bearing fault detection by a novel condition-monitoring scheme based on statistical-time features and neural networks

     Cirrincione, Giansalvo; Garcia Espinosa, Antonio; Delgado Prieto, Miguel; Henao, Humberto; Ortega Redondo, Juan Antonio
    IEEE transactions on industrial electronics
    Date of publication: 2013-08
    Journal article

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    Bearing degradation is the most common source of faults in electrical machines. In this context, this work presents a novel monitoring scheme applied to diagnose bearing faults. Apart from detecting local defects, i.e., single-point ball and raceway faults, it takes also into account the detection of distributed defects, such as roughness. The development of diagnosis methodologies considering both kinds of bearing faults is, nowadays, subject of concern in fault diagnosis of electrical machines. First, the method analyzes the most significant statistical-time features calculated from vibration signal. Then, it uses a variant of the curvilinear component analysis, a nonlinear manifold learning technique, for compression and visualization of the feature behavior. It allows interpreting the underlying physical phenomenon. This technique has demonstrated to be a very powerful and promising tool in the diagnosis area. Finally, a hierarchical neural network structure is used to perform the classification stage. The effectiveness of this condition-monitoring scheme has been verified by experimental results obtained from different operating conditions.

    Bearing degradation is the most common source of faults in electrical machines. In this context, this work presents a novel monitoring scheme applied to diagnose bearing faults. Apart from detecting local defects, i.e., single-point ball and raceway faults, it takes also into account the detection of distributed defects, such as roughness. The development of diagnosis methodologies considering both kinds of bearing faults is, nowadays, subject of concern in fault diagnosis of electrical machines. First, the method analyzes the most significant statistical-time features calculated from vibration signal. Then, it uses a variant of the curvilinear component analysis, a nonlinear manifold learning technique, for compression and visualization of the feature behavior. It allows interpreting the underlying physical phenomenon. This technique has demonstrated to be a very powerful and promising tool in the diagnosis area. Finally, a hierarchical neural network structure is used to perform the classification stage. The effectiveness of this condition-monitoring scheme has been verified by experimental results obtained from different operating conditions.

  • Study of stability and non-linear control applied to microgrid

     Andrade Rengifo, Fabio
    Defense's date: 2013-11-07
    Department of Electronic Engineering, Universitat Politècnica de Catalunya
    Theses

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  • LOAD FORECASTING ON THE USER SIDE BY MEANS OF COMPUTATIONAL INTELLIGENCE ALGORITHMS  Open access

     Cardenas Araujo, Juan Jose
    Defense's date: 2013-07-11
    Department of Electronic Engineering, Universitat Politècnica de Catalunya
    Theses

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    Nowadays, it would be very difficult to deny the need to prioritize sustainable development through energy efficiency at all consumption levels. In this context, an energy management system (EMS) is a suitable option for continuously improving energy efficiency, particularly on the user side. An EMS is a set of technological tools that manages energy consumption information and allows its analysis. EMS, in combination with information technologies, has given rise to intelligent EMS (iEMS), which, aside from lending support to monitoring and reporting functions as an EMS does, it has the ability to model, forecast, control and diagnose energy consumption in a predictive way. The main objective of an iEMS is to continuously improve energy efficiency (on-line) as automatically as possible. The core of an iEMS is its load modeling forecasting system (LMFS). It takes advantage of historical information on energy consumption and energy-related variables in order to model and forecast load profiles and, if available, generator profiles. These models and forecasts are the main information used for iEMS applications for control and diagnosis. That is why in this thesis we have focused on the study, analysis and development of LMFS on the user side. The fact that the LMFS is applied on the user side to support an iEMS means that specific characteristics are required that in other areas of load forecasting they are not. First of all, the user-side load profiles (LPs) have a higher random behavior than others, as for example, in power system distribution or generation. This makes the modeling and forecasting process more difficult. Second, on the user side --for example an industrial user-- there is a high number and variety of places that can be monitored, modeled and forecasted, as well as their precedence or nature. Thus, on the one hand, an LMFS requires a high degree of autonomy to automatically or autonomously generate the demanded models. And on the other hand, it needs a high level of adaptability in order to be able to model and forecast different types of loads and different types of energies. Therefore, the addressed LMFS are those that do not look only for accuracy, but also adaptability and autonomy. Seeking to achieve these objectives, in this thesis work we have proposed three novel LMFS schemes based on hybrid algorithms from computational intelligence, signal processing and statistical theory. The first of them looked to improve adaptability, keeping in mind the importance of accuracy and autonomy. It was called an evolutionary training algorithm (ETA) and is based on adaptivenetwork-based-fuzzy-inference system (ANFIS) that is trained by a multi-objective genetic algorithm instead of its traditional training algorithm. As a result of this hybrid, the generalization capacity was improved (avoiding overfitting) and an easily adaptable training algorithm for new adaptive networks based on traditional ANFIS was obtained. The second scheme deals with LMF autonomy in order to build models from multiple loads automatically. Similar to the previous proposal, an ANFIS and a MOGA were used. In this case, the MOGA was used to find a near-optimal configuration for the ANFIS instead of training it. The LMFS relies on this configuration to work properly, as well as to maintain accuracy and generalization capabilities. Real data from an industrial scenario were used to test the proposed scheme and the multi-site modeling and self-configuration results were satisfactory. Furthermore, other algorithms were satisfactorily designed and tested for processing raw data in outlier detection and gap padding. The last of the proposed approaches sought to improve accuracy while keeping autonomy and adaptability. It took advantage of dominant patterns (DPs) that have lower time resolution than the target LP, so they are easier to model and forecast. The Hilbert-Huang transform and Hilbert-spectral analysis were used for detecting and selecting the DPs. Those selected were used in a proposed scheme of partial models (PM) based on parallel ANFIS or artificial neural networks (ANN) to extract the information and give it to the main PM. Therefore, LMFS accuracy improved and the user-side LP noising problem was reduced. Additionally, in order to compensate for the added complexity, versions of self-configured sub-LMFS for each PM were used. This point was fundamental since, the better the configuration, the better the accuracy of the model; and subsequently the information provided to the main partial model was that much better. Finally, and to close this thesis, an outlook of trends regarding iEMS and an outline of several hybrid algorithms that are pending study and testing are presented.

    En el contexto energético actual y particularmente en el lado del usuario, el concepto de sistema de gestión energética (EMS) se presenta como una alternativa apropiada para mejorar continuamente la eficiencia energética. Los EMSs en combinación con las tecnologías informáticas dan origen al concepto de iEMS, que además de soportar las funciones de los EMS, tienen la capacidad de modelar, pronosticar, controlar y supervisar los consumos energéticos. Su principal objetivo es el de realizar una mejora continua, lo más autónoma posible y predictiva de la eficiencia energética. Este tipo de sistemas tienen como núcleo fundamental el sistema de modelado y pronóstico de consumos (Load Modeling and Forecasting System, LMFS). El LMFS está habilitado para pronosticar el comportamiento futuro de cargas y, si es necesario, de generadores. Es sobre estos pronósticos sobre los cuales el iEMS puede realizar sus tareas automáticas y predictivas de optimización y supervisión. Los LMFS en el lado del usuario son el foco de esta tesis. Un LMFS en el lado del usuario, diseñado para soportar un iEMS requiere o demanda ciertas características que en otros contextos no serían tan necesarias. En primera estancia, los perfiles de los usuarios tienen un alto grado de aleatoriedad que los hace más difíciles de pronosticar. Segundo, en el lado del usuario, por ejemplo en la industria, el gran número de puntos a modelar requiere que el LMFS tenga por un lado, un nivel elevado de autonomía para generar de la manera más desatendida posible los modelos. Por otro lado, necesita un nivel elevado de adaptabilidad para que, usando la misma estructura o metodología, pueda modelar diferentes tipos de cargas cuya procedencia pude variar significativamente. Por lo tanto, los sistemas de modelado abordados en esta tesis son aquellos que no solo buscan mejorar la precisión, sino también la adaptabilidad y autonomía. En busca de estos objetivos y soportados principalmente por algoritmos de inteligencia computacional, procesamiento de señales y estadística, hemos propuesto tres algoritmos novedosos para el desarrollo de un LMFS en el lado del usuario. El primero de ellos busca mejorar la adaptabilidad del LMFS manteniendo una buena precisión y capacidad de autonomía. Denominado ETA, consiste del uso de una estructura ANFIS que es entrenada por un algoritmo genético multi objetivo (MOGA). Como resultado de este híbrido, obtenemos un algoritmo con excelentes capacidades de generalización y fácil de adaptar para el entrenamiento y evaluación de nuevas estructuras adaptativas basadas en ANFIS. El segundo de los algoritmos desarrollados aborda la autonomía del LMFS para así poder generar modelos de múltiples cargas. Al igual que en la anterior propuesta usamos un ANFIS y un MOGA, pero esta vez el MOGA en vez de entrenar el ANFIS, se utiliza para encontrar la configuración cuasi-óptima del ANFIS. Encontrar la configuración apropiada de un ANFIS es muy importante para obtener un buen funcionamiento del LMFS en lo que a precisión y generalización respecta. El LMFS propuesto, además de configurar automáticamente el ANFIS, incluyó diversos algoritmos para procesar los datos puros que casi siempre estuvieron contaminados de datos espurios y gaps de información, operando satisfactoriamente en las condiciones de prueba en un escenario real. El tercero y último de los algoritmos buscó mejorar la precisión manteniendo la autonomía y adaptabilidad, aprovechando para ello la existencia de patrones dominantes de más baja resolución temporal que el consumo objetivo, y que son más fáciles de modelar y pronosticar. La metodología desarrollada se basa en la transformada de Hilbert-Huang para detectar y seleccionar tales patrones dominantes. Además, esta metodología define el uso de modelos parciales de los patrones dominantes seleccionados, para mejorar la precisión del LMFS y mitigar el problema de aleatoriedad que afecta a los consumos en el lado del usuario. Adicionalmente, se incorporó el algoritmo de auto configuración que se presentó en la propuesta anterior para hallar la configuración cuasi-óptima de los modelos parciales. Este punto fue crucial puesto que a mejor configuración de los modelos parciales mayor es la mejora en precisión del pronóstico final. Finalmente y para cerrar este trabajo de tesis, se realizó una prospección de las tendencias en cuanto al uso de iEMS y se esbozaron varias propuestas de algoritmos híbridos, cuyo estudio y comprobación se plantea en futuros estudios.

  • Dedicated hierarchy of neural networks applied to bearings degradation assessment

     Delgado Prieto, Miguel; Cirrincione, Giansalvo; Garcia Espinosa, Antonio; Ortega Redondo, Juan Antonio; Henao, Humberto
    IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives
    Presentation's date: 2013-08-30
    Presentation of work at congresses

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    Condition monitoring schemes, able to deal with different sources of fault are, nowadays, required by the industrial sector to improve their manufacturing control systems. Pattern recognition approaches, allow the identification of multiple system's scenarios by means the relations between numerical features. The numerical features are calculated from acquired physical magnitudes, in order to characterize its behavior. However, only a reduced set of numerical features are used in order to avoid computational performance limitations of the artificial intelligence techniques. In this sense, feature reduction techniques are applied. Classical approaches analyze the features significance from a global data discrimination point of view. This paper, however, proposes a novel and reliable methodology to exploit the information contained in the original features set, by means a dedicated hierarchy of neural networks. © 2013 IEEE.

  • STLF in the user-side for an iEMS based on evolutionary training of adaptive networks

     Cardenas Araujo, Juan Jose; Giacometto Torres, Francisco; Garcia Espinosa, Antonio; Romeral Martinez, Jose Luis
    IEEE International Conference on Emerging Technologies and Factory Automation
    Presentation's date: 2012-09
    Presentation of work at congresses

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    It is a fact that the short-term load forecasting (STLF) in the user side is growing interest. Consequently, intelligent energy management systems (iEMSs) are including this capability in order to take autonomous decisions. In this context, this paper presents a new STLF scheme based on Adaptative Networks Fuzzy Inference Systems (ANFIS). This ANFIS has an exponential output membership functions (e-ANFIS) and has been trained by means of a novel evolutionary training algorithm (ETA). Due to the computational burden required by ETA, parallel computing was used to eliminate this problem especially for embedded applications. This new scheme has been tested with real data from an automotive factory and it shows better results in comparison with typical adaptative network structures (neural network and ANFIS).

    It is a fact that the short-term load forecasting (STLF)in the user side is growing interest. Consequently, intelligent energy management systems (iEMSs) are including this capability in order to take autonomous decisions. In this context, this paper presents a new STLF scheme based on Adaptative Networks Fuzzy Inference Systems (ANFIS). This ANFIS has an exponential output membership functions (e-ANFIS) and has been trained by means of a novel evolutionary training algorithm (ETA). Due to the computational burden required by ETA, parallel computing was used to eliminate this problem especially for embedded applications. This new scheme has been tested with real data from an automotive factory and it shows better results in comparison with typical adaptative network structures (neural network and ANFIS).

  • Accurate Bearing Faults Classification based on Statistical-Time Features, Curvilinear Component Analysis and Neural Networks

     Delgado Prieto, Miguel; Cirrincione, Giansalvo; Garcia Espinosa, Antonio; Ortega Redondo, Juan Antonio; Henao, Humberto
    Annual Conference of the IEEE Industrial Electronics Society
    Presentation's date: 2012-10-25
    Presentation of work at congresses

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    Bearing faults are the commonest form of malfunction associated with electrical machines. So far, the research has been carried out mainly in the detection of localized faults, but the diagnosis of distributed faults is still under development. In this context, this work presents a new scheme for detecting and classifying both kinds of faults. This work deals with a new diagnosis monitoring scheme, which is based on statistical-time features calculated from vibration signal, curvilinear component analysis for compression and visualization of the features behavior and a hierarchical neural network structure for classification. The obtained results from different operation conditions validate the effectiveness and feasibility of the proposed methodology.

  • A novel condition monitoring scheme for bearing faults based on Curvilinear Component Analysis and hierarchical neural networks

     Delgado Prieto, Miguel; Cirrincione, Giansalvo; Garcia Espinosa, Antonio; Ortega Redondo, Juan Antonio; Henao, Humberto
    International Conference on Electrical Machines
    Presentation's date: 2012-09-05
    Presentation of work at congresses

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    Mostly the faults in electrical machines are related with the bearings. Thus, a reliable bearing condition monitoring scheme able to detect either local or distributed defects are mandatory to avoid a breakdown in the machine. So far, the research has been carried out mainly in the detection of local faults, such as balls and raceways faults, but surface roughness is not so reported. This paper deals with a novel and reliable scheme capable to detect any fault that may occur in a bearing, based on EXIN Curvilinear Component Analysis, CCA, and Neural Network. The EXIN CCA, which is an improvement of the Curvilinear Component Analysis, has been conceived for data visualization, interpretation and classification for real time industrial applications. The effectiveness of this condition monitoring scheme has been verified by experimental results obtained from different operation conditions.

  • Load forecasting framework of electricity consumptions for an Intelligent Energy Management System in the user-side

     Cardenas Araujo, Juan Jose; Romeral Martinez, Jose Luis; Garcia Espinosa, Antonio; Andrade Rengifo, Fabio
    Expert systems with applications
    Date of publication: 2012-04
    Journal article

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  • CONTRIBUTIONS TO ELECTROMECHANICAL SYSTEMS DIAGNOSIS BY MEANS DATA FUSION TECHNIQUES  Open access

     Delgado Prieto, Miguel
    Defense's date: 2012-10-26
    Department of Electronic Engineering, Universitat Politècnica de Catalunya
    Theses

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    Electromechanical drives have traditionally found their field of application in the industrial sector. However, the use of such systems is spreading to other sectors within the field of transport, such as the automotive sector, or to the aircraft sector with the development of the concept of More Electric Aircraft (MEA). One of the major improvements of the MEA concept is related to the actuators of the primary flight controls, where so far only have been considered electrohydraulic actuators, although the current trend is to replace them with electromechanical actuators (EMA). Widespread use, in the future, of EMA in transport systems, is only possible with research and vances in algorithms for detection and diagnosis of faults that may occur both, in the electrical or mechanical parts, in order to ensure the reliability of the drive and the safety of users. During the last years, the study of electro-techanical systems and the fault diagnosis under varying conditions of torque and speed has been mandatory. Although these requirements have been studied deeply by different authors, most of the works are focused on single fault detection. Therefore, there is a lack of diagnosis methods able to detect different kinds of faults in an electro-mechanical actuator. There are very few studies related with diagnosis schemes capable of identifying various faults under different operating conditions, and even less analyzing deeply all the diagnosis chain to face the challenge from all possible perspectives. In this research work, it is proposed the nvestigation towards integral health monitoring schemes for electro-mechanical systems based on pattern recognition. In order to identify various faults under different operating conditions, the health monitoring scheme is developed from a data fusion point of view. The processing of great deals of information enhances the pattern recognition capabilities but, in turn, requires the mplementation of advanced techniques and methodologies. Therefore, first, it is proposed in this research work a review of the whole diagnosis chain, including the different stages (feature calculation, features reduction and classification), the methodologies and techniques. The review finishes by presenting the proposed strategies to take a step further in each diagnosis stage, proposing methodologies to be investigated which would allow a significant advance towards the integral diagnosis systems. In this sense, investigation towards a novel feature calculation methodology able to deal with non-stationary conditions is presented. Next, the feature reduction stage is covered by the proposal of collaborative methodologies by different techniques to improve the significance of the reduced feature set. Also, a more concrete approach is developed by non-lineal techniques, which are not commonly used. Finally, different classification structures are analyzed and novel classification architecture is proposed to be applied in multi-fault diagnosis problems. Experimental analyses are presented resulting from the application of the proposed strategies to different electro-mechanical arrangements. The obtained results achieve high performance levels, and the proposed methodologies can be adapted to the necessary diagnostic requirements. It should be noticed that the proposed contributions increase the information obtained from the system to a better understanding of its behavior and this, has a direct effect over the reliability of the system operation.

    Els accionaments electromecànics han tingut tradicionalment el seu camp d'aplicació en el sector industrial. No obstant això l'ús d'aquest tipus de sistemes s'està estenent cap a altres sectors dins l'àmbit dels transports, com el sector de l'automòbil, o el sector de l'aeronàutica, amb el desenvolupament del concepte de l'Avió Més Elèctric (MEA). Una de les millores més importants del concepte MEA està relacionada amb els actuadors dels controls primaris de vol, on fins ara només s'han considerat actuadors electrohidràulics, encara que la tendència actual és reemplaçar-los per actuadors electromecànics (EMA). L'ús generalitzat, en el futur, d'accionaments EMA en sistemes de transport, passa per la investigació i els avenços en els algorismes de detecció i diagnòstic de fallides que es puguin produir, tant en la part elèctrica com en la mecànica, per tal de garantir la fiabilitat de l'accionament i la seguretat dels usuaris. Durant els últims anys, l'estudi de sistemes electromecànics i el diagnòstic de fallides en diverses condicions de parell i de règim de funcionament, han estat estudiats profundament per diferents autors, encara que la majoria dels treballs es centren en la detecció d'una única fallida. Per tant, hi ha una manca de mètodes de diagnòstic capaços de detectar diferents tipus de defectes en un actuador electromecànic. Hi ha molt pocs estudis relacionats amb els sistemes de diagnòstic, capaços d'identificar diverses fallides sota diferents condicions d'operació, i molt menys analitzar profundament tota la cadena de diagnòstic per afrontar el problema des de totes les perspectives possibles. En aquesta tesi, es proposa la investigació sobre tècniques per a la monitorització de condició de sistemes electromecànics, basada en el reconeixement de patrons. Per tal d'identificar diferents fallides sota diferents condicions d'operació, les tècniques propostes s'elaboren sota el prisma de la fusió de dades. El tractament de grans quantitats d'informació, millora els resultats dels algoritmes de reconeixement de patrons, però al seu torn, requereixen de l'aplicació de tècniques i metodologies avançades. Per tant, inicialment es realitza una revisió de la cadena de diagnòstic complerta, incloent les metodologies i tècniques per a les diferents etapes (càlcul d'indicadors, reducció de dimensionalitat i classificació). La revisió finalitza amb la presentació de les estratègies proposades com aportació en cada etapa de diagnòstic. Els resultats obtinguts permeten avenços significatius cap als sistemes de diagnòstic integrals. En aquest sentit, es presenta la investigació sobre metodologies de càlcul d'indicadors en condicions no estacionàries. A continuació, en l'etapa de reducció de dimensionalitat, es proposen metodologies col•laboratives aplicant diferents tècniques que permeten millorar la discriminació de classes, concretament es proposa un enfocament basant-se en tècniques no lineals, que no s'usen habitualment. Finalment, s'analitzen les diferents estructures de classificació i es proposa una arquitectura nova de classificació per ser aplicada en problemes de diagnòstic de múltiples fallides. Es presenten resultats experimentals de les diferents metodologies propostes, per a diferents configuracions electromecàniques. Els resultats obtinguts mostren un alt nivell de rendiment, i les metodologies proposades es poden adaptar als requisits de diagnòstic necessàries en diferents aplicacions. Es conclou que la informació resultant permet una millor comprensió del comportament del sistema sota test, i això té un efecte directe sobre la seva fiabilitat d'operació.

    Los accionamientos electromecánicos han tenido tradicionalmente su campo de aplicación en el sector industrial. Sin embargo el uso de este tipo de sistemas se está extendiendo hacia otros sectores dentro del ámbito de los transportes, como el sector del automóvil, o el sector de la aeronáutica con el desarrollo del concepto del Avión Más Eléctrico (MEA). Una de las mejoras más importantes del concepto MEA está relacionada con los actuadores de los controles primarios de vuelo, donde hasta el momento sólo se han considerado actuadores electrohidráulicos, aunque la tendencia actual es remplazarlos por actuadores electromecánicos (EMA). El uso generalizado, en el futuro, de accionamientos EMA en sistemas de transporte, pasa por la investigación y los avances en los algoritmos de detección y diagnóstico de fallos que se puedan producir, tanto en la parte eléctrica como en la mecánica, con el fin de garantizar la fiabilidad del accionamiento y la seguridad de los usuarios. Durante los últimos años, el estudio de sistemas electromecánicos y el diagnóstico de fallos en diversas condiciones de par y de régimen de funcionamiento, han sido estudiados profundamente por diferentes autores, aunque la mayoría de los trabajos se centran en la detección de un único fallo. Por lo tanto, existe una falta de métodos de diagnóstico capaces de detectar diferentes tipos de defectos en un actuador electro-mecánico. Hay muy pocos estudios relacionados con los sistemas de diagnóstico, capaces de identificar diversos fallos bajo diferentes condiciones de operación, y mucho menos analizar profundamente toda la cadena de diagnóstico para afrontar el problema desde todas las perspectivas posibles. En esta tesis, se propone la investigación sobre técnicas para la monitorización de condición de sistemas electromecánicos, basados en el reconocimiento de patrones. Con el fin de identificar diferentes fallos bajo diferentes condiciones de operación, las técnicas propuestas se elaboran bajo el prisma de la fusión de datos. El tratamiento de grandes cantidades de información, mejora los resultados de los algoritmos de reconocimiento de patrones, pero a su vez, requieren de la aplicación de técnicas y metodologías avanzadas. Por lo tanto, inicialmente se realiza una revisión de la cadena de diagnóstico completa, incluyendo las metodologías y técnicas para las diferentes etapas (cálculo de indicadores, reducción de dimensionalidad y clasificación). La revisión finaliza con la presentación de las estrategias propuestas como aportación en cada etapa de diagnóstico. Los resultados obtenidos permiten avances significativos hacia los sistemas de diagnóstico integrales. En este sentido, se presenta la investigación sobre metodologías de cálculo de indicadores en condiciones no estacionarias. A continuación, en la etapa de reducción de dimensionalidad, se proponen metodologías colaborativas aplicando diferentes técnicas que permiten mejorar la discriminación de clases; concretamente se propone un enfoque basándose en técnicas no lineales, que no se usan habitualmente. Finalmente, se analizan las diferentes estructuras de clasificación y se propone una arquitectura novedosa de clasificación para ser aplicada en problemas de diagnóstico de múltiples fallos. Se presentan resultados experimentales de las diferentes metodologías propuestas, para diferentes configuraciones electro-mecánicas. Los resultados obtenidos muestran un alto nivel de rendimiento, y las metodologías propuestas se pueden adaptar a los requisitos de diagnóstico necesarias en diferentes aplicaciones. Se concluye que la información resultante permite una mejor comprensión del comportamiento del sistema bajo test, y esto tiene un efecto directo sobre su fiabilidad de operación.

  • Access to the full text
    Evaluation of machine learning techniques for electro-mechanical system diagnosis  Open access

     Delgado Prieto, Miquel; Garcia Espinosa, Antonio; Urresty Betancourt, Julio César; Riba Ruiz, Jordi Roger; Ortega Redondo, Juan Antonio
    European Conference on Power Electronics and Applications
    Presentation's date: 2011-09-01
    Presentation of work at congresses

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    The application of intelligent algorithms, in electro-mechanical diagnosis systems, is increasing in order to reach high Reliability and performance ratios in critical and complex scenarios. In this context, different multidimensional intelligent diagnosis systems, based on different machine learning techniques, are presented and evaluated in an electro-mechanical actuator diagnosis scheme. The used diagnosis methodology includes the acquisition of different physical magnitudes from the system, such as machine vibrations and stator currents, to enhance the monitoring capabilities. The features calculation process is based on statistical time and frequency domains features, as well as timefrequency fault indicators. A features reduction stage is, additionally, included to compress the descriptive fault information in a reduced feature set. After, different classification algorithms such as Support Vector Machines, Neural Network, k-Nearest Neighbors and Classification Trees are implemented. Classification ratios over inputs corresponding to previously learnt classes, and generalization capabilities with inputs corresponding to learnt classes slightly modified are evaluated in an experimental test bench to analyze the suitability of each algorithm for this kind of application.

    Postprint (author’s final draft)

  • Evaluation of feature calculation methods for electromechanical system diagnosis

     Delgado Prieto, Miquel; Garcia Espinosa, Antonio; Ortega Redondo, Juan Antonio
    IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives
    Presentation's date: 2011-09-05
    Presentation of work at congresses

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    The use of intelligent machine health monitoring schemes is increasing in critical applications as traction tasks in the transport sector. The high diagnosis capability and reliability required in these systems are being supported by intelligent classification algorithms. These classifiers use calculated features from the system to perform the diagnosis. In this context, different features calculation methods can be applied to characterize the system condition obtaining different classification results. The aim of this work is based on diagnosis capabilities evaluation of the main features calculation methods: statistical features from time, statistical features from frequency, time-frequency distributions and signal decomposition techniques. The features capabilities are quantitatively evaluated by two parameters: the classification accuracy and the discriminant coefficient. Experimental results are obtained from an electromechanical actuator under different diagnosis requirements: from single fault to combined faults detection under stationary and non-stationary speed and torque conditions.

  • Motor fault classification system including a novel hybrid feature reduction methodology

     Delgado Prieto, Miquel; Urresty Betancourt, Julio César; Albiol, L.; Ortega Redondo, Juan Antonio; Garcia Espinosa, Antonio; Romeral Martinez, Jose Luis; Vidal, E.
    Annual Conference of the IEEE Industrial Electronics Society
    Presentation's date: 2011-11-10
    Presentation of work at congresses

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    The fault diagnosis field is in a continuous movement towards the generation of more reliable and powerful machine health monitoring schemes. Improved data processing methodologies are required to reach high diagnosis demands. For that reason, a contribution in motor fault classification methodology is presented. Different physical magnitudes such as phase currents, voltages and vibrations, are acquired from an electromechanical system based on Brushless DC motor. Statistical features, from time and frequency domains, are calculated to supply a classification algorithm based on Neural Network and enhanced by Genetic Algorithm. The significance of feature space dimensionality, related with the number of used features, for classification success is analyzed. The combination of a feature selection technique (by Sequential Floating Forward Selection), with a feature extraction technique (by Principal Component Analysis), is proposed as a novel hybrid feature reduction methodology to improve the classification performance in electrical machine fault diagnosis. The proposed methodology is validated experimentally and compared with classical feature reduction strategies.

  • Evolutive ANFIS training for energy load profile forecast for an IEMS in an automated factory

     Cardenas Araujo, Juan Jose; Garcia Espinosa, Antonio; Romeral Martinez, Jose Luis; Kampouropoulos, Konstantinos
    IEEE International Conference on Emerging Technologies and Factory Automation
    Presentation's date: 2011
    Presentation of work at congresses

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    In this paper an evolutive algorithm is used to train an adaptative-network-based fuzzy inference system (ANFIS), particularly a genetic algorithm (GA). The GA is able to train the antecedent and consequent parameters of an ANFIS, which is used for energy load profile forecasting in an automated factory. This load forecasting is useful to support an intelligent energy management system (IEMS), which enables the user to optimize the energy consumptions by means of getting the optimal work points, scheduling the production according to these points, etc. The proposed training algorithm showed excellent results with complex plants like industrial energy consumers in the user-side, where the randomness of the loads is higher than in utility loads. Real data from an automated car factory were used to test the presented algorithms. Appropriated results were obtained.

  • Multidimensional intelligent diagnosis system based on support vector machines classifier

     Delgado Prieto, Miquel; Ortega Redondo, Juan Antonio; Garcia Espinosa, Antonio; Cardenas Araujo, Juan Jose; Romeral Martinez, Jose Luis
    IEEE International Symposium on Industrial Electronics
    Presentation's date: 2011-06-30
    Presentation of work at congresses

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    Heding the diagnostic requirements of electromechanical systems applied in automotive and aeronautical sectors, a multidimensional diagnostic system based on Support Vector Machine classifier is presented in this paper. In this context, different stationary and non-stationary speed and torque conditions are taken into account over an experimental actuator, in the same way, different single and combined failures scenarios are analyzed. In order to achieve a proper reliability in the diagnosis process, a multidimensional strategy is proposed: currents and vibrations from an electro-mechanical actuator are acquired. A great deal of features is calculated using statistical parameters from the acquired signals in time and frequency domain. Additionally, advanced time-frequency domain analysis techniques, such as Wavelet Packet Transform and Empirical Mode Decomposition, are used to achieve features which provide information in non-stationary conditions. The feature space dimensionality is analyzed by a feature reduction stage based on Partial Least Squares, which optimizes and reduces the feature set to be used for diagnosis proposes. The classification core is based on Support Vector Machine. Moreover, this work provides a performance comparison between the proposed classification algorithm and others such as Neural Network, k-Nearest Neighbor and Classification Trees. Experimental results are presented to demonstrate the feasibility and diagnostic capability of the proposed system

  • Feature extraction of demagnetization faults in permanent-magnet synchronous motors based on box-counting fractal dimension

     Delgado Prieto, Miquel; Garcia Espinosa, Antonio; Riba Ruiz, Jordi Roger; Urresty Betancourt, Julio César; Ortega Redondo, Juan Antonio
    IEEE transactions on industrial electronics
    Date of publication: 2011-05
    Journal article

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    This paper presents a methodology for feature extraction of a new fault indicator focused on detecting demagnetization faults in a surface-mounted permanent-magnet synchronous motors operating under nonstationary conditions. Preprocessing of transient-current signals is performed by applying Choi–Williams distribution to highlight the salient features of this demagnetization fault. In this paper, fractal dimension calculation based on the computation of the box-counting method is performed to extract the optimal features for diagnosis purposes. It must be noted that the applied feature-extraction process is autotuned, so it does not depend on the severity of the fault and is applicable to a wide range of operating conditions of the motor. The performance of the proposed system is validated experimentally. According to the obtained results, the proposed methodology is reliable and feasible for diagnosing demagnetization faults in industrial applications.

  • Modeling of surface-mounted permanent magnet synchronous motors with stator winding inter-turn faults

     Romeral Martinez, Jose Luis; Urresty Betancourt, Julio César; Riba Ruiz, Jordi Roger; Garcia Espinosa, Antonio
    IEEE transactions on industrial electronics
    Date of publication: 2011-05-29
    Journal article

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    This paper develops and analyzes a parametric model for simulating healthy and faulty surface-mounted permanent magnet synchronous motors. It allows studying the effects of stator winding interturn short-circuit faults. A relevant feature of the developed model is that it deals with spatial harmonics due to a nonsinusoidal rotor permanent magnet configuration. Additionally, the proposed model is valid for studying the behavior of these machines running under nonstationary conditions, including load or speed variations. Stator current spectra obtained from simulations performed by applying the proposed model show a close similitude with experimental results, highlighting the potential of such a model to understand the effects of stator winding failures on the current spectrum and allowing it to carry out an automatic diagnosis of such faults.

  • INCREASE OF AUTOMATIVE CAR INDUSTRY COMPETITIVENESS THROUGH AN INTEGRAL AND ARTIFICIAL INTELLIGENCE DRIVEN ENERGY MANAGEMENT SYSTEM

     Kampouropoulos, Konstantinos; Cardenas Araujo, Juan Jose; Ortega Redondo, Juan Antonio; Giacometto Torres, Francisco; Sala Caselles, Vicente Miguel; Garcia Espinosa, Antonio; Romeral Martinez, Jose Luis
    Participation in a competitive project

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  • INTELLIGENT MONITORING SYSTEM BASED ON ACCOUSTIC EMISSIONS SENSING FOR PLANT CONDITION MONITORING AND PREVENTATIVE MAINTENANCE

     Ortega Redondo, Juan Antonio; Moreno Eguilaz, Juan Manuel; Garcia Espinosa, Antonio; Cusido Roura, Jordi; Riba Ruiz, Jordi Roger; Sala Caselles, Vicente Miguel; Delgado Prieto, Miguel; Romeral Martinez, Jose Luis
    Participation in a competitive project

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  • Q-00008

     Garcia Espinosa, Antonio; Kampouropoulos, Konstantinos; Romeral Martinez, Jose Luis
    Participation in a competitive project

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  • Investigación sobre accionamientos con máquinas de flujo axial de imanes permanentes para instalación en rueda de vehículos eléctricos

     Riba Ruiz, Jordi Roger; Delgado Prieto, Miguel; Urresty Betancourt, Julio; Sala Caselles, Vicente Miguel; Ortega Redondo, Juan Antonio; Garcia Espinosa, Antonio; Moreno Eguilaz, Juan Manuel; Cusido Roura, Jordi; Romeral Martinez, Jose Luis
    Participation in a competitive project

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  • Dynamic model for AC and DC contactors ¿ Simulation and experimental validation

     Riba Ruiz, Jordi Roger; Garcia Espinosa, Antonio; Cusido Roura, Jordi; Delgado Prieto, Miquel
    Simulation modelling practice and theory
    Date of publication: 2011-10-01
    Journal article

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  • Signal injection as a fault detection technique

     Cusido Roura, Jordi; Romeral Martinez, Jose Luis; Ortega Redondo, Juan Antonio; Garcia Espinosa, Antonio; Riba Ruiz, Jordi Roger
    Sensors
    Date of publication: 2011-03
    Journal article

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  • Sistema de diagnóstico y procedimiento para los fallos en actuadores electromecánicos

     Cusido Roura, Jordi; Cardenas Araujo, Juan Jose; Romeral Martinez, Jose Luis; Garcia Espinosa, Antonio
    Date of request: 2011-07-25
    Invention patent

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  • Desenvolupament de recursos per a la creació i gestio de material docent orientat a  Open access

     Bogarra Rodriguez, Santiago; Font Piera, Antonio; Alabern Morera, Francesc Xavier; Rodriguez Cortes, Pedro; Grau Gotés, Mª Ángela; Pallares Viña, Miguel Juan; Rocabert Delgado, Joan; Rolan Blanco, Alejandro; Garcia Espinosa, Antonio
    Jornada Dia d'Atenea. 2010
    Presentation's date: 2010-06-04
    Presentation of work at congresses

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    L'autoaprenentatge i l’autoavaluació d’assignatures tècniques requereixen eines de càlcul que no estan disponibles dintre del campus virtual ATENEA, per aquest motiu s’ha incorporat al campus virtual de la UPC el software WIRIS Quizzes, que integra el motor de càlcul matemàtic WIRIS amb el sistema de preguntes de Moodle, ampliant les opcions per a la realització de qüestionaris i facilitant l’adaptació d’assignatures reglades de caràcter tècnic a l'EEES. Els qüestionaris desenvolupats permeten a l’estudiant escriure tot el desenvolupament de l’exercici amb una sintaxis específica i guardar aquesta resposta, amb el que es millora la informació rebuda pel professor permetent adaptar l’ensenyament a les necessitats dels alumnes.

  • Desenvolupament de recursos per a la creació i gestió de material docent orientat a l¿aprenentatge actiu que faciliti l¿adaptació d¿assignatures tècniques a l'EEES i la seva aplicació a l¿ensenyament de circuits elèctrics  Open access

     Bogarra Rodriguez, Santiago; Font Piera, Antonio; Alabern Morera, Francesc Xavier; Rodriguez Cortes, Pedro; Grau Gotés, Mª Ángela; Pallares Viña, Miguel Juan; Rocabert Delgado, Joan; Rolan Blanco, Alejandro; Garcia Espinosa, Antonio; Marsó Riera, Arnau; Jaime Salillas, Francisco; Tarrés Soler, Mireia
    Jornada d'Innovació Docent UPC
    Presentation's date: 2010-02-11
    Presentation of work at congresses

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    L'autoaprenentatge i l’autoavaluació d’assignatures tècniques requereixen eines de càlcul que no estan disponibles dintre del campus virtual ATENEA, per aquest motiu s’ha incorporat al campus virtual de la UPC el software WIRIS Quizzes, que integra el motor de càlcul matemàtic WIRIS amb el sistema de preguntes de Moodle, ampliant les opcions per a la realització de qüestionaris i facilitant l’adaptació d’assignatures reglades de caràcter tècnic a l'EEES. Els qüestionaris desenvolupats, tant d’autoavaluació com d’avaluació, es particularitzen per cada estudiant aprofitant que WIRIS Quizzes permet incorporar a les preguntes de Moodle elements matemàtics generats de forma aleatòria. Els qüestionaris desenvolupats permeten a l’estudiant escriure tot el desenvolupament de l’exercici amb una sintaxis específica i guardar aquesta resposta, amb el que es millora la informació rebuda pel professor permetent adaptar l’ensenyament a les necessitats dels alumnes. En la línia d’adaptar les assignatures de Circuits Elèctrics a l'EEES s’ha creat l’assignatura virtual “l’ensenyament de circuits elèctrics” al campus virtual de la UPC en la que a part del qüestionaris s’ha afegit el material docent desenvolupat mitjançant l’editor de documents multimèdia EMDOC, aquest material que resta al DMD consta d’apunts amb problemes desenvolupats. També s’han desenvolupat exercicis pràctics amb l’objectiu d’incentivar l’adquisició de coneixements.

  • Demagnetization diagnosis in permanent magnet synchronous motors under non-stationary speed conditions

     Riba Ruiz, Jordi Roger; Garcia Espinosa, Antonio; Romeral Martinez, Jose Luis; Cusido Roura, Jordi
    Electric power systems research
    Date of publication: 2010-10
    Journal article

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    Permanent magnet synchronous motors (PMSMs) are applied in high performance positioning and variable speed applications because of their enhanced features with respect to other AC motor types. Fault detection and diagnosis of electrical motors for critical applications is an active field of research. However, much research remains to be done in the field of PMSM demagnetization faults, especially when running under non-stationary conditions. This paper presents a time–frequency method specifically focused to detect and diagnose demagnetization faults in PMSMs running under non-stationary speed conditions, based on the Hilbert Huang transform. The effectiveness of the proposed method is proven by means of experimental results.

  • Closed-loop controller for eliminating the contact bounce in DC core contactors

     Garcia Espinosa, Antonio; Riba Ruiz, Jordi Roger; Cusido Roura, Jordi; Ortega Redondo, Juan Antonio; Romeral Martinez, Jose Luis
    IEEE transactions on components and packaging technologies
    Date of publication: 2010-09
    Journal article

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    A computer model for teaching the dynamic behavior of AC contactors  Open access

     Riba Ruiz, Jordi Roger; Garcia Espinosa, Antonio; Romeral Martinez, Jose Luis
    IEEE transactions on education
    Date of publication: 2010-05
    Journal article

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    AC-powered contactors are extensively used in industry in applications such as automatic electrical devices, motor starters, and heaters. In this work, a practical session that allows students to model and simulate the dynamic behavior of ac-powered electromechanical contactors is presented.

  • An introduction to fault diagnosis of permanent magnet synchronous machines in master's degree courses

     Riba Ruiz, Jordi Roger; Garcia Espinosa, Antonio; Romeral Martinez, Jose Luis; Ortega Redondo, Juan Antonio
    Computer applications in engineering education
    Date of publication: 2010-08-16
    Journal article

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    Early fault detection and diagnosis of high-performance electric motors has been an active area of research for the past two decades. This work presents a practical session that facilitates instructing students in this field. To meet this objective, fault diagnostic methods based on the Fourier transform and the wavelet transform are successfully applied by means of processing and examining the frequency content of the stator currents acquired from healthy and faulty permanent magnet synchronous machines (PMSMs). The goal of this practical lab is to introduce Master's degree students to the topic of fault detection by covering both stationary and nonstationary operating conditions of the motor under study. The Technical University of Catalonia (UPC) has successfully incorporated the learning methodology proposed in this paper in a practical session of an electronic engineering course. The effectiveness of the proposed practical lab has been assessed using the results of a satisfaction questionnaire answered by students involved in the course.

    Early fault detection and diagnosis of high-performance electric motors has been an active area of research for the past two decades. This work presents a practical session that facilitates instructing students in this field. To meet this objective, fault diagnostic methods based on the Fourier transform and the wavelet transform are successfully applied by means of processing and examining the frequency content of the stator currents acquired from healthy and faulty permanent magnet synchronous machines (PMSMs). The goal of this practical lab is to introduce Master’s degree students to the topic of fault detection by covering both stationary and nonstationary operating conditions of the motor under study. The Technical University of Catalonia (UPC) has successfully incorporated the learning methodology proposed in this paper in a practical session of an electronic engineering course. The effectiveness of the proposed practical lab has been assessed using the results of a satisfaction questionnaire answered by students involved in the course.

  • Electrical Monitoring for Fault Detection in an EMA

     Romeral Martinez, Jose Luis; Rosero Garcia, Javier Alveiro; Garcia Espinosa, Antonio; Cusido Roura, Jordi; Ortega Redondo, Juan Antonio
    IEEE aerospace and electronic systems magazine
    Date of publication: 2010-03
    Journal article

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  • Intelligent Connection Agent for Three-Phase Grid-Connected Microgrids

     Rocabert Delgado, Joan
    Defense's date: 2010-09-16
    Department of Electrical Engineering, Universitat Politècnica de Catalunya
    Theses

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  • A Simple 2-D Finite-Element Geometry for Analyzing Surface-Mounted Synchronous Machines With Skewed Rotor Magnets

     Urresty Betancourt, Julio César; Riba Ruiz, Jordi Roger; Romeral Martinez, Jose Luis; Garcia Espinosa, Antonio
    IEEE transactions on magnetics
    Date of publication: 2010-09-03
    Journal article

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    On-line fault detection method for induction machines based on signal convolution  Open access

     Cusido Roura, Jordi; Romeral Martinez, Jose Luis; Garcia Espinosa, Antonio; Ortega Redondo, Juan Antonio; Riba Ruiz, Jordi Roger
    European transactions on electrical power
    Date of publication: 2010-06-28
    Journal article

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    A new technique for induction motor fault detection and diagnosis is presented. This technique, which has been experimentally verified in stationary and non-stationary motor conditions, is based on the convolution of wavelet-based functions with motor stator currents. These functions are tuned to specific fault frequencies taking into account motor speed and load torque, thus considering variable operation conditions of the motor. Based on this technique an automatic system for fault diagnosis is also presented, which is suited for easy software implementation.

  • A multi-objective GA to demand-side management in an automated warehouse

     Cardenas Araujo, Juan Jose; Garcia Espinosa, Antonio; Romeral Martinez, Jose Luis; Urresty Betancourt, Julio César
    International Journal of Electrical Energy Systems
    Date of publication: 2010-07-01
    Journal article

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  • Dynamic evaluation of fringing flux in linear electromechanical devices

     Garcia Espinosa, Antonio; Riba Ruiz, Jordi Roger; Cusido Roura, Jordi; Romeral Martinez, Jose Luis; Ortega Redondo, Juan Antonio
    Journal of electrical engineering
    Date of publication: 2010-03-12
    Journal article

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    Accurate dynamic models of electromechanical devices are essential in order to develop effective motion control strategies of such devices. The effects of fringing flux can not be ignored when dealing with electromagnetic devices that present air gaps. So far parametric models applied to compute the motion of electromechanical devices do not include accurate formulations to take into account this effect. This paper develops an experimental method to obtain a simple analytic formulation of such an effect that can be used to calculate the linear motion of the aforesaid devices in a proper and accurate way. These effects are introduced in a robust and low time-consuming parametric model and the results are shown. Measured data has been compared with data obtained from simulations thus validating the simplicity and effectiveness of the proposed methodology.

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    Fault detection by means of Hilbert-Huang transform of the stator current in a PMSM with demagnetization  Open access

     Garcia Espinosa, Antonio; Rosero Garcia, Javier Alveiro; Cusido Roura, Jordi; Romeral Martinez, Jose Luis; Ortega Redondo, Juan Antonio
    IEEE transactions on energy conversion
    Date of publication: 2010-06
    Journal article

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    This paper presents a novel method to diagnose demagnetization in permanent-magnet synchronousmotor (PMSM). Simulations have been performed by 2-D finite-element analysis in order to determine the current spectrum and the magnetic flux distribution due to this failure. The diagnostic just based on motor current signature analysis can be confused by eccentricity failure because the harmonic content is the same. Moreover, it can only be applied under stationary conditions. In order to overcome these drawbacks, a novel method is used based upon the Hilbert–Huang transform. It represents time-dependent series in a 2-D time–frequency domain by extracting instantaneous frequency components through an empirical-mode decomposition process. This tool is applied by running the motor under nonstationary conditions of velocity. The experimental results show the reliability and feasibility of the methodology in order to diagnose the demagnetization of a PMSM.

  • Wavelet and PDD as fault detection techniques

     Cusido Roura, Jordi; Romeral Martinez, Jose Luis; Ortega Redondo, Juan Antonio; Garcia Espinosa, Antonio; Riba Ruiz, Jordi Roger
    Electric power systems research
    Date of publication: 2010-08
    Journal article

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  • Detection of demagnetization faults in PMSM under Non-Stationary Conditions

     Riba Ruiz, Jordi Roger; Rosero Garcia, Javier Alveiro; Garcia Espinosa, Antonio; Romeral Martinez, Jose Luis
    IEEE transactions on magnetics
    Date of publication: 2009-07
    Journal article

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  • Validation of the parametric model of a DC contactor using Matlab-Simulink

     Riba Ruiz, Jordi Roger; Garcia Espinosa, Antonio; Ortega Redondo, Juan Antonio
    Computer applications in engineering education
    Date of publication: 2009-03
    Journal article

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  • On-Line Measurement Device to Detect Bearing Faults on Electric Motors

     Cusido Roura, Jordi; Garcia Espinosa, Antonio; Navarro Rodriguez, Luis Miguel; Delgado Prieto, Miquel; Romeral Martinez, Jose Luis; Ortega Redondo, Juan Antonio
    IEEE International Instrumentation and Measurement Technology Conference
    Presentation's date: 2009-05
    Presentation of work at congresses

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    A multi-objective GA to demand-side management in an automated warehouse  Open access

     Cardenas Araujo, Juan Jose; Garcia Espinosa, Antonio; Romeral Martinez, Jose Luis; Urresty Betancourt, Julio César
    IEEE International Conference on Emerging Technologies and Factory Automation
    Presentation's date: 2009
    Presentation of work at congresses

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    The simultaneous operation of the automated storage and retrieval machines (ASRs) in an automated warehouse can increase the likelihood that high power demand peaks turn unstable the electric system. Furthermore, high power peaks mean the need for more electrical power contracted, which in turns leads to more fixed operation cost and inefficient use of the electrical installations. In this context, we present a multi-objective genetic algorithm approach (MOGA) to implement demand-side management (DSM) in an automated warehouse. It works minimizing the total energy demand, but without increasing substantially the time for the operation. Simulations show the performances of the new approach.

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    A genetic algorithm approach to optimization of power peaks in an automated warehouse  Open access

     Cardenas Araujo, Juan Jose; Garcia Espinosa, Antonio; Romeral Martinez, Jose Luis; Andrade Rengifo, Fabio
    Annual Conference of the IEEE Industrial Electronics Society
    Presentation's date: 2009-11
    Presentation of work at congresses

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    The simultaneous operation of the automated storage and retrieval machines (ASRs) in an automated warehouse can increase the likelihood that high power demand peaks turn unstable the electric system. Furthermore, high power peaks mean the need for more electrical power contracted, which in turns leads to more fixed operation cost and inefficient use of the electrical installations. In this context, we present a genetic algorithm approach to implement demandside management (DSM) in an automated warehouse. It has been based on real data from ASRs and models of prognosis of load profile of ASRs. We took into account two main goals: minimize instantaneous power demand and keeping the performance of the system store and retrieval times.

  • A new auto-provisioned squat-based traffic management strategy for multiclass networks

     Hesselbach Serra, Xavier; Garcia Espinosa, Antonio
    EuroNF Workshop on Traffic Management and Traffic Engineering for the Future Internet
    Presentation's date: 2009-12-08
    Presentation of work at congresses

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  • Advanced Wide Band Gap Semiconductor Devices for Rational Use of Energy (RUE)

     Sala Caselles, Vicente Miguel; Llaquet Saiz, Jorge Mariano; Riba Ruiz, Jordi Roger; Moreno Eguilaz, Juan Manuel; Delgado Prieto, Miguel; Garcia Espinosa, Antonio; Cusido Roura, Jordi; Urresty Betancourt, Julio César; Romeral Martinez, Jose Luis
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  • Modelos electromágneticos FEM de motores PMSM RF/AF y controles sensoriess de velocidad

     Romeral Martinez, Jose Luis; Garcia Espinosa, Antonio; Sala Caselles, Vicente Miguel; Delgado Prieto, Miguel; Saavedra Ordoñez, Harold; Michalski, Tomasz Dobromir; Salehi Arashloo Arashloo, Ramin
    Participation in a competitive project

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  • MCIA: Accionaments Elèctrics i Aplicacions Industrials

     Ruiz Illana, German; Llaquet Saiz, Jorge Mariano; Moreno Eguilaz, Juan Manuel; Andrade Rengifo, Fabio; Riba Ruiz, Jordi Roger; Sala Caselles, Vicente Miguel; Garcia Espinosa, Antonio; Cusido Roura, Jordi; Delgado Prieto, Miguel; Ortega Redondo, Juan Antonio; Cardenas Araujo, Juan Jose; Urresty Betancourt, Julio César; Romeral Martinez, Jose Luis
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  • Fault Anticipation Software System Architecture for Aircraft EMA

     Delgado Prieto, Miquel; Navarro Rodriguez, Luis Miguel; Garcia Espinosa, Antonio; Ortega Redondo, Juan Antonio
    European Conference on Power Electronics and Applications
    Presentation's date: 2009
    Presentation of work at congresses

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  • Bearing Diagnosis Methodologies by means of Common Mode Current

     Delgado Prieto, Miquel; Garcia Espinosa, Antonio; Ortega Redondo, Juan Antonio; Urresty Betancourt, Julio César; Riba Ruiz, Jordi Roger
    European Conference on Power Electronics and Applications
    Presentation's date: 2009
    Presentation of work at congresses

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  • Electric field effects of bundle and stranded conductors in overhead power lines

     Riba Ruiz, Jordi Roger; Garcia Espinosa, Antonio; Alabern Morera, Francesc Xavier
    Computer applications in engineering education
    Date of publication: 2009-02
    Journal article

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