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  • 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.

  • Influence of the final drive ratio, electric motor size and battery capacity on fuel consumption of a parallel plug-in hybrid electric vehicle

     Torres Carbonell, Oriol; Bader, Benjamin; Romeral Martinez, Jose Luis; Lux, G.; Ortega Redondo, Juan Antonio
    International Conference on Urban Transport and the Environment
    Presentation's date: 2013-05
    Presentation of work at congresses

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    The purpose of this paper is to study the influence of the electric motor (EM) size, final drive ratio (FD) and the battery capacity (BAT) of a parallel plug-in hybrid electric vehicle (PHEV) regarding fuel consumption. The energy efficiency of a certain vehicle drivetrain depends on the size of the components. For this reason and for cost reasons it is necessary to study the optimal dimensions of the components that minimize the fuel consumption for a given driving cycle. In this publication the influence of the size of the electric motor, final drive ratio and also the battery capacity are analysed using the Response Surface Methodology (RSM) of the Design of Experiments (DoE) technique. A parallel PHEV has been parameterized and simulated to obtain the fuel consumption over NEDC driving cycle using Modelica/Dymola. This paper contains an introduction, a brief explanation of the modelled parallel HEV, a description of the all electric range operating strategy based on rules, an explanation of the RSM method, the simulation results, and finally the conclusions of this study.

  • Intelligent sensor based on acoustic emission analysis applied to gear fault diagnosis

     Zurita Millan, Daniel; Delgado Prieto, Miguel; Ortega Redondo, Juan Antonio; Romeral Martinez, Jose Luis
    IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives
    Presentation's date: 2013-08-28
    Presentation of work at congresses

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    The development of intelligent and autonomous monitoring systems applied to rotating machinery, represents the evolution towards the automatic industrial plants supervision. In this regard, an acoustic emission based intelligent sensor is presented in this work. The proposed sensor records regularly the acoustic emission signal generated by gearboxes. A time domain statistical analysis is applied in order to characterize the acquired data. Afterwards, a neural network based algorithm is applied to detect gear fault patterns. Finally, the diagnosis result is sent through a wireless transceiver to the central control unit. Moreover, in order to reach a real autonomous operation, the sensor power is approached by different energy harvesting solutions.

  • Reduction of the prediction horizon of predictive energy management for a plug-in HEV in hilly terrain

     Bader, Benjamin; Torres Carbonell, Oriol; Ortega Redondo, Juan Antonio; Lux, G.; Romeral Martinez, Jose Luis
    International Conference on Urban Transport and the Environment
    Presentation's date: 2013-05
    Presentation of work at congresses

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    Due to the great weight and high costs of electric energy storage systems (ESS), the number of pure electric vehicles (EV) is increasing only slowly. As a compromise between the autonomous hybrid electric vehicle (HEV) and EV, the plug-in HEV (PHEV) allows, like the EV, the recharging of the battery by the grid but brings also a combustion engine so as not to depend on the limited electric range of the vehicle. Next to the sizing of the vehicle components, the energy management strategy has an important influence on the fuel consumption of the vehicle. To minimize fuel consumption, predictive energy management is necessary, as all stored electric energy should be consumed by the end of the trip. In this way it is possible to minimize fuel consumption by substituting as much fuel as possible by the use of electric energy. In order to reach the global optimal result, a prediction horizon of the optimization for the duration of the entire trip is necessary. However, due to model uncertainties and the limited calculation capacities of the control units in a vehicle the global optimum cannot be achieved. Therefore, measures have to be taken to reduce the computation cost on the one hand and achieve results close the global optimum on the other. One of these measures, next to an adequate optimization algorithm, is the reduction of the prediction horizon. In this study, for a real life cycle including urban and highway parts a variation of the prediction horizon is carried out and the influence on the fuel consumption is simulated. The respective results are calculated using Dynamic Programming to exclude any influence of the chosenCenergy management strategy. The results are compared to the global optimal fuel consumption of the used driving cycle.

  • Pla d'Actuació MCIA

     Ortega Redondo, Juan Antonio
    Participation in a competitive project

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  • 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.

  • Access to the full text
    Bearing fault diagnosis by EXIN CCA  Open access

     Cirrincione, Giansalvo; Henao, Humberto; Delgado Prieto, Miquel; Ortega Redondo, Juan Antonio
    International Conference on Neural Networks
    Presentation's date: 2012-06-10
    Presentation of work at congresses

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    EXIN CCA is an extension of the Curvilinear Component Analysis (CCA), which solves for the noninvariant CCA projection and allows representing data drawn under different operating conditions. It can be applied to data visualization, interpretation (as a kind of sensor of the underlying physical phenomenon) and classification for real time industrial applications. Here an example is given for bearing fault diagnostics in an electromechanical device.

  • Voice coil inter-turn faults modeling and simulation

     Ruiz Illana, German; Ortega Redondo, Juan Antonio; Hernandez Guiteras, Joan
    Audio Engineering Society
    Presentation's date: 2012-04-29
    Presentation of work at congresses

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  • Influence of the prediction horizon length of a PHEV energy management on fuel consumption

     Bader, Benjamin; Torres Carbonell, Oriol; Ortega Redondo, Juan Antonio; Lux, G.; Romeral Martinez, Jose Luis
    International Electric Vehicle Symposium and Exhibition
    Presentation's date: 2012-05
    Presentation of work at congresses

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    he use of information about the future vehicle trajectory is especially advantageous for the energy management strategies of Plug-in Hybrid Electric Vehicles. This is based on the fact that for minimal fuel consumption the stored electric energy should be consumed until the end of the trip, if the trip length exceeds the electric range of the vehicle. Therefore, best results are achieved by an optimization of the torque distribution between both electric motor and combustion engine knowing the whole trajectory until the next use of a recharging station. Due to the long recharging times this means usually an optimization until the end of the trip. A drawback of such long predictive horizons is the high computation cost. Another is the increasing model uncertainty due to the use of simplified powertrain models for the prediction algorithm and also the reliability of the predicted trip information. Therefore, one aim is to reduce the prediction horizon as much as possible without increasing significantly the fuel consumption. To save computation cost of the optimization and decrease the influence of model uncertainties, in this paper an energy management for Plug-in HEV calculating the global optimum for the whole trip is compared to optimization with different prediction horizon lengths. To define the desired SOC at the end of the prediction horizon a linear reference SOC function is used. Depending on the chosen prediction length the trajectory is divided into several sections, each one standing for one prediction horizon. At the entrance to every section the energy management calculates the optimal torque set point for the whole next section (prediction horizon). In order to exclude the influence of the optimization algorithm, Dynamic Programming is used to calculate the global optimum.

  • Comparison of different hybrid electric vehicles concepts in terms of consumption and efficiency

     Torres Carbonell, Oriol; Bader, Benjamin; Romeral Martinez, Jose Luis; Lux, G.; Ortega Redondo, Juan Antonio
    International Electric Vehicle Symposium and Exhibition
    Presentation's date: 2012-05
    Presentation of work at congresses

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    The objective of this paper is to compare the most common HEV power train structures. As a first step, forward and backward models of these vehicle concepts are implemented using Modelica/Dymola in order to evaluate and compare the energy consumption. Taking into account fuel/electrical consumption and the losses in the powertrain components, a comparison of two different alternatives of Hybrid Electric Vehicle models (parallel structure and Range Extender) are presented in this publication. To simulate these models using different driving cycles, a rule-based operating strategy is implemented. As a second step, a Dynamic Programming (DP) based algorithm is applied to these models. This algorithm is used to determine the optimal fuel consumption for given driving cycles. A comparison of the DP results and rule-based results is carried out to evaluate the potential improvement that is possible to achieve optimizing the energy management strategy and the size of the powertrain components.

  • 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.

  • 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.

  • Pla de transferencia tecnologica pttu de la Univeristat Politècnica de Catalunya

     Ortega Redondo, Juan Antonio
    Participation in a competitive project

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  • 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.

  • 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.

  • 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
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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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  • Valortec

     Cusido Roura, Jordi; Romeral Martinez, Jose Luis; Ortega Redondo, Juan Antonio
    Award or recognition

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  • TECCOL11-1-0007-16

     Ortega Redondo, Juan Antonio
    Participation in a competitive project

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  • Increment actitivitat i desenvolupament marca MCIA-TECNIO

     Ortega Redondo, Juan Antonio
    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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  • 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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  • Loudspeaker rub fault detection by means of a new nonstationary procedure test

     Ruiz Illana, German; Sala Caselles, Vicente Miguel; Delgado Prieto, Miquel; Ortega Redondo, Juan Antonio
    Audio Engineering Society
    Presentation's date: 2010-11-04
    Presentation of work at congresses

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    This paper addresses rub defect loudspeaker detection. The study includes a simulation with a rub model based on classical static coulomb friction added to the loudspeaker nonlinearities parametric model to demonstrate the electric current signal viability to rub failure detection. The electric current signal is analyzed by means of Zhao-Atlas- Marks distribution (ZAMD). A failure extractor based on relevant ZAMD frequency regions segmentation and Mahalanobis distance is presented. The simulation and experimental results show the goodness and reliability of rub detection method presented.

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    Análisis eléctrico del altavoz dinámico mediante modelado de anomalías mecánicas de fricción  Open access

     Ruiz Illana, German; Sala Caselles, Vicente Miguel; Ortega Redondo, Juan Antonio; Romeral Martinez, Jose Luis
    Seminario Anual de Automática, Electrónica Industrial e Instrumentación
    Presentation's date: 2010-07-08
    Presentation of work at congresses

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    El presente trabajo busca caracterizar el efecto de la fricción del conjunto móvil con el polo o el núcleo de la estructura magnética sobre la corriente absorbida por el altavoz, una de las más relevantes anomalías mecánicas que se dan en la fabricación de altavoces de bobina móvil: Se presenta el modelo de fallo y se añade al modelo de parámetros concentrados del altavoz. Finalmente, se caracterizan sus efectos mediante la extracción de indicadores de fallo basados en el valor eficaz de la corriente, el comportamiento del componente armónico (HD3) y los productos de intermodulación (IMD3) de tercer orden.

  • 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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  • BiosecurID: a multimodal biometric database

     Fierrez, J.; Galbally, J.; Ortega García, J.; Freire, M.R.; Alonso Fernandez, F.; Ramos, David; Toledano, D. T.; González Rodríguez, J.; Siguenza, J. A.; Garrido Salas, J.; Anguiano, E.; González de Rivera, G.; Ribalda, R.; Ortega Redondo, Juan Antonio; Cardeñoso Payo, V.; Viloria, A.; Vivaracho, C. E.; Moro, Q. I.; Faúndez Zanuy, Marcos; Igarza, J.J.; Sanchez, J.; Hernaez, I.; Orrite Uruñuela, C.; Martinez Contreras, F.; Gracia Roche, J.J.
    Pattern analysis and applications
    Date of publication: 2010-05
    Journal article

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  • 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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  • Consolidació CENTRE MCIA

     Ortega Redondo, Juan Antonio
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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.

  • 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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  • 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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    Stator short circuits detection in PMSM by means of higher order spectral analysis (HOSA)  Open access

     Rosero Garcia, Javier Alveiro; Ortega Redondo, Juan Antonio; Urresty Betancourt, Julio César; Cardenas Araujo, Juan Jose; Romeral Martinez, Jose Luis
    IEEE Applied Power Electronics Conference and Exposition
    Presentation's date: 2009
    Presentation of work at congresses

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    This paper presents and analyzed short circuit failures for Permanent Magnet Synchronous Motor (PMSM). The study includes stated and dynamic condition for experimental and simulation test. The stator current is analyzed by means of higher order spectral analysis (HOSA). HOSA techniques are used for stator current analysis under stable conditions is power frequency spectrum density (PSD); and Multiple Signal Classification (MUSIC), and bispectrum are used under dynamics conditions. Therefore, it is possible to improve the accuracy and efficiency of technique. Experimental results validate the analysis and demonstrate for HOSA can be applied to detect and identify short circuit failures in synchronous machines.

  • 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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  • Desarrollo y programación de software de gestion de la demanda de consumo elèctrico

     Romeral Martinez, Jose Luis; Ortega Redondo, Juan Antonio; Kampouropoulos, Konstantinos
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  • Investigación en tecnologías para la gestión de la migración

     Ortega Redondo, Juan Antonio
    Participation in a competitive project

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  • Stator Short Circuits Detection in PMSM by means of Zhao-Atlas-Marks distribution and energy calculation

     Urresty Betancourt, Julio César; Riba Ruiz, Jordi Roger; Ortega Redondo, Juan Antonio; Cardenas Araujo, Juan Jose
    European Conference on Power Electronics and Applications
    Presentation's date: 2009
    Presentation of work at congresses

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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
    Participation in a competitive project

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  • Desarrollo de sistemes de contaje bidireccionales y universales de energia elèctrica

     Ortega Redondo, Juan Antonio
    Participation in a competitive project

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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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  • Short-Circuit Detection by Means of Empirical Mode Decomposition and Wigner-Ville Distribution for PMSM Running Under Dynamic Condition

     Rosero Garcia, Javier Alveiro; Romeral Martinez, Jose Luis; Ortega Redondo, Juan Antonio; Rosero, Esteban
    IEEE transactions on industrial electronics
    Date of publication: 2009-11
    Journal article

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    Motor fault detection using a Rogowski sensor without an integrator  Open access

     Poncelas, O; Rosero Garcia, Javier Alveiro; Cusido Roura, Jordi; Ortega Redondo, Juan Antonio; Romeral Martinez, Jose Luis
    IEEE transactions on industrial electronics
    Date of publication: 2009-10
    Journal article

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    This paper presents a new approach for the current acquisition system in motor fault detection applications. This paper includes the study, design, and implementation of a Rogowski coil current sensor without the integrator circuit that is typically used. The circuit includes an autotuning block able to adjust to different motor speeds. Equalizing the amplitudes of the fundamental and fault harmonics leads to higher precision current measurements. The resulting compact sensor is used as a current probe for fault detection in induction motors through motor current signal analysis. The use of a Rogowski coil without an integrator allows a better discrimination of the fault harmonics around the third and fifth main harmonics. Finally, the adaptive conditioning circuit is tested over an induction machine drive. Results are presented, and quantitative comparisons are carried out.

  • Fault detection in induction machines using power spectral density in wavelet decomposition

     Cusido Roura, Jordi; Romeral Martinez, Jose Luis; Ortega Redondo, Juan Antonio; Rosero Garcia, Javier Alveiro; Garcia Espinosa, Antonio
    IEEE transactions on industrial electronics
    Date of publication: 2008-02
    Journal article

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