Juan Antonio Ortega Redondo | Upc (original) (raw)
Teaching Documents by Juan Antonio Ortega Redondo
El objeto de esta práctica consiste en la implementación de un circuito lógico que permita conoce... more El objeto de esta práctica consiste en la implementación de un circuito lógico que permita conocer el resultado de una comparación ponderada de dos grupos de dos elementos cada uno, equiparable al funcionamiento de una balanza. Fig. 1. Comparación ponderada. Las variables independientes A, B, C y D pueden adoptar únicamente los valores 0 o 1. Las funciones X, Y y Z expresarán el resultado de la comparación. Sólo una de las tres funciones valdrá 1 en cada instante: X si la balanza se inclina hacia la izquierda, Z si hacia la derecha e Y si permanece en equilibrio. Fig. 2. Circuito lógico a implementar. Para la implementación del circuito lógico, se empleará la herramienta software WebPack ISE y la placa de evaluación de Digilent, que incorpora la FPGA XC3S200 de la serie Spartan 3 del fabricante Xilinx, cuyo aspecto puede observarse en la figura 3. Las variables A, B, C y D serán generadas mediante los interruptores SW7, SW6, SW5 y SW4 y el valor de las funciones X, Y y Z se mostrará mediante los diodos led LD7, LD6 y LD5 respectivamente.
Papers by Juan Antonio Ortega Redondo
Applied Sciences
This paper proposes and evaluates the behavior of a new health indicator to estimate the capacity... more This paper proposes and evaluates the behavior of a new health indicator to estimate the capacity fade of lithium-ion batteries and their state of health (SOH). This health indicator is advantageous because it does not require the acquisition of data from full charge–discharge cycles, since it is calculated within a narrow SOC interval where the voltage vs. SOC relationship is very linear and that is within the usual transit range for most practical charge and discharge cycles. As a result, only a small fraction of the data points of a full charge–discharge cycle are required, reducing storage and computational resources while providing accurate results. Finally, by using the battery model defined by the Nernst equation, the behavior of future charge–discharge cycles can be accurately predicted, as shown by the results presented in this paper. The proposed approach requires the application of appropriate signal processing techniques, from discrete wavelet filtering to prediction met...
2013 World Electric Vehicle Symposium and Exhibition (EVS27), 2013
The objective of this paper is to give recommendations for the component sizing of a Parallel Plu... more The objective of this paper is to give recommendations for the component sizing of a Parallel Plug-in Hybrid Electric Vehicle (PHEV) studying the influence of the Electric Motor (EM) size, Final Drive ratio (FD), the Battery Capacity (BAT) and the Internal Combustion Engine (ICE). A multiple options for the size of the components are in the market and conflicting on the vehicle efficiency and functionality. Their selection is very important in order to achieve reduced fuel consumption and assure the vehicle performance with the minimum cost. This study explains a proposal methodology to solve this problem, firstly doing a problem model approach, then reducing his complexity doing a parameterization and finally analyzing the optimal variables for the multiple objectives. In this publication the component sizing is analysed using the Response Surface Methodology (RSM) of the Design of Experiments (DoE) technique. The parallel HEV has been parameterized and simulated to obtain the fuel...
IEEE Access, 2019
Artificial intelligence has bounced into industrial applications contributing several advantages ... more Artificial intelligence has bounced into industrial applications contributing several advantages to the field and have led to the possibility to open new ways to solve many actual problems. In this paper, a data-driven performance evaluation methodology is presented and applied to an industrial refrigeration system. The strategy takes advantage of the Multivariate Kernel Density Estimation technique and Self-Organizing Maps to develop a robust method, which is able to determine a near-optimal performance map, taking into account the system uncertainties and the multiple signals involved in the process. A normality model is used to detect and filter non-representative operating samples to subsequently develop a reliable performance map. The performance map allows to compare the plant assessment under the same operating conditions and permits to identify the potential system improvement capabilities. To ensure that the resulting evaluation is trustworthy, a robustness strategy is developed to identify either possible new operation conditions or abnormal situations in order to avoid uncertain assessments. Furthermore, the proposed approach is tested with real industrial plant data to validate the suitability of the method.
IEEE Access, 2016
This paper presents a condition-based monitoring methodology based on novelty detection applied t... more This paper presents a condition-based monitoring methodology based on novelty detection applied to industrial machinery. The proposed approach includes both, the classical classification of multiple a priori known scenarios, and the innovative detection capability of new operating modes not previously available. The development of condition-based monitoring methodologies considering the isolation capabilities of unexpected scenarios represents, nowadays, a trending topic able to answer the demanding requirements of the future industrial processes monitoring systems. First, the method is based on the temporal segmentation of the available physical magnitudes, and the estimation of a set of time-based statistical features. Then, a double feature reduction stage based on Principal Component Analysis and Linear Discriminant Analysis is applied in order to optimize the classification and novelty detection performances. The posterior combination of a Feed-forward Neural Network and One-Class Support Vector Machine allows the proper interpretation of known and unknown operating conditions. The effectiveness of this novel condition monitoring scheme has been verified by experimental results obtained from an automotive industry machine.
IEEE Access, 2016
Industrial process monitoring and modeling represent a critical step in order to achieve the para... more Industrial process monitoring and modeling represent a critical step in order to achieve the paradigm of zero defect manufacturing. The aim of this paper is to introduce the neo-fuzzy neuron method to be applied in industrial time series modeling. Its open structure and input independence provide fast learning and convergence capabilities, while assuring a proper accuracy and generalization in the modeled output. First, the auxiliary signals in the database are analyzed in order to find correlations with the target signal. Second, the neo-fuzzy neuron is configured and trained accordingly by means of the auxiliary signal, past instants, and dynamics information of the target signal. The proposed method is validated by means of real data from a Spanish copper rod industrial plant, in which a critical signal regarding copper refrigeration process is modeled. The obtained results indicate the suitability of the neo-fuzzy neuron method for industrial process modeling. INDEX TERMS Artificial intelligence, forecasting, fuzzy neural networks, industrial plants, predictive models, time series analysis.
IEEE Transactions on Instrumentation and Measurement, 2016
Advanced sensing strategies in the industrial sector are becoming a valued technological answer t... more Advanced sensing strategies in the industrial sector are becoming a valued technological answer to increase the performance and competitiveness. The development of enhanced sensing solutions considering both technology and monitoring requirements is, nowadays, subject of concern in the industrial maintenance field. In this context, this work presents a novel selfpowered wireless sensor applied to condition monitoring of gears. The proposed sensor is based on a modular architecture, offering multipoint sensing, local wireless communication, multi-source energy harvesting and embedded diagnosis algorithm for mechanical fractures detection based on acoustic emission analysis. The developments are complemented by means of a remote management interface, from which the user can configure the functionalities of the sensors, visualize the network status as well as analyze the diagnosis evolution. The sensor performance, in terms of power consumption and fault detection, has been analyzed by means of experimental results.
2011 IEEE International Symposium on Industrial Electronics, 2011
ABSTRACT Heeding the diagnostic requirements of electro- mechanical systems applied in automotive... more ABSTRACT Heeding the diagnostic requirements of electro- mechanical 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.
This paper presents and analyzed short circuit failures for permanent magnet synchronous motor (P... more This paper presents and analyzed short circuit failures for permanent magnet synchronous motor (PMSM). The study includes stated state and dynamic condition for experimental test. The stator current is analyzed by means of Hilbert-Huang transform (HHT) and energy spectrum.
The 2012 International Joint Conference on Neural Networks (IJCNN), 2012
EXIN CCA is an extension of the Curvilinear Component Analysis (CCA), which solves for the noninv... more 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.
Electrical Engineering, 2014
This work analyzes the behavior of surface-mounted permanent magnet synchronous motors (SPMSMs) o... more This work analyzes the behavior of surface-mounted permanent magnet synchronous motors (SPMSMs) operating under stator faults. The studied faults are resistive unbalance and winding inter-turn short circuits, which may lead to unbalanced conditions of the motor. Both faults may reduce motor efficiency and performance and produce premature ageing. This work develops an analytical model of the motor when operating under stator faults. By this way, the theoretical basis to understand the effects of resistive unbalance and stator winding inter-turn faults in SPMSMs is settled. This work also compares two methods for detecting and discriminating both faults. For this purpose, a method based on the analysis of the zero-sequence voltage component is presented, which is compared to the traditional method, i.e. the analysis of the stator currents harmonics. Both simulation and experimental results presented in this work show the potential of the zero-sequence voltage component method to provide helpful and reliable data to carry out a simultaneous diagnosis of resistive unbalance and stator winding inter-turn faults.
IEEE Transactions on Industrial Electronics, 2008
Motor-current-signature analysis has been successfully used in induction machines for fault diagn... more Motor-current-signature analysis has been successfully used in induction machines for fault diagnosis. The method, however, does not always achieve good results when the speed or the load torque is not constant, because this causes variations on the motor-slip and fast Fourier transform problems appear due to a nonstationary signal. This paper proposes a new method for motor fault detection, which analyzes the spectrogram based on a short-time Fourier transform and a further combination of wavelet and power-spectral-density (PSD) techniques, which consume a smaller amount of processing power. The proposed algorithms have been applied to detect broken rotor bars as well as shorted turns. Besides, a merit factor based on PSD is introduced as a novel approach for condition monitoring, and a further implementation of the algorithm is proposed. Theoretical development and experimental results are provided to support the research.
IEEE Transactions on Components and Packaging Technologies, 2010
The undesirable phenomenon of the contact bounce causes severe erosion of the contacts and, as a ... more The undesirable phenomenon of the contact bounce causes severe erosion of the contacts and, as a consequence, their electrical life and reliability are greatly reduced. On the other hand, the bounce of the armature can provoke reopening of the contacts, even when they have already been closed. This paper deals with the elimination of the bounce in both contacts and armature of a commercial dc core contactor. This is achieved by means of a current closed-loop controller, which only uses as input the current and voltage of the contactor's magnetizing coil. The logic control has been implemented in a low cost microcontroller. Moreover, the board control can be fed by either dc or ac, and either in 50 Hz or 60 Hz so as to extend its applicability. A set of data is obtained from the measurement of the position and velocity of the movable parts for different operating voltages, and the dynamic behavior of the contactor is discussed.
IEEE Aerospace and Electronic Systems Magazine, 2007
The latest advances in electric and electronic aircraft technologies from the point of view of an... more The latest advances in electric and electronic aircraft technologies from the point of view of an "all-elect~ric" aircraft are presented herein. Specifically, we describe the concept of a "More Electric Aircraft" (MEA), which involves removing the need for on-engine hydraulic power generation and bleed air off-takes, and the increasing use of power electronics in the starter/generation system of the main engine. Removal of the engine hydraulic pumps requires fully-operative electrical power actuators and mastery of the flight control architecture. The paper presents a general overview of the electrical power generation system and electric drives for the MIEA, with special regard to the flight controls. Some discussion regarding the interconnection of nodes and safety of buses and protocols in distributed systems is also presented.
International Journal of …, 2006
AbstractA remote microcontroller lab based on an 80C537 mock-up is presented. This paper present... more AbstractA remote microcontroller lab based on an 80C537 mock-up is presented. This paper presents a new way to interact with these kinds of systems via the Internet, giving the possibility for complete interaction. The Citrix Application Server is the platform which manages the ...
2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA), 2016
Industrial manufacturing plants often suffer from reliability problems during their day-today ope... more Industrial manufacturing plants often suffer from reliability problems during their day-today operations which have the potential for causing a great impact on the effectiveness and performance of the overall process and the sub-processes involved. Time-series forecasting of critical industrial signals presents itself as a way to reduce this impact by extracting knowledge regarding the internal dynamics of the process and advice any process deviations before it affects the productive process. In this paper, a novel industrial condition monitoring approach based on the combination of Self Organizing Maps for operating point codification and Recurrent Neural Networks for critical signal modeling is proposed. The combination of both methods presents a strong synergy, the information of the operating condition given by the interpretation of the maps helps the model to improve generalization, one of the drawbacks of recurrent networks, while assuring high accuracy and precision rates. Finally, the complete methodology, in terms of performance and effectiveness is validated experimentally with real data from a copper rod industrial plant.
IEEE Transactions on Industrial Electronics, 2009
This paper presents a new approach for the current acquisition system in motor fault detection ap... more 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 Rogowskicoil 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.
Shock and Vibration, 2016
Gearboxes and induction motors are important components in industrial applications and their moni... more Gearboxes and induction motors are important components in industrial applications and their monitoring condition is critical in the industrial sector so as to reduce costs and maintenance downtimes. There are several techniques associated with the fault diagnosis in rotating machinery; however, vibration and stator currents analysis are commonly used due to their proven reliability. Indeed, vibration and current analysis provide fault condition information by means of the fault-related spectral component identification. This work presents a methodology based on vibration and current analysis for the diagnosis of wear in a gearbox and the detection of bearing defect in an induction motor both linked to the same kinematic chain; besides, the location of the fault-related components for analysis is supported by the corresponding theoretical models. The theoretical models are based on calculation of characteristic gearbox and bearings fault frequencies, in order to locate the spectral ...
European Transactions on Electrical Power, 2010
A new technique for induction motor fault detection and diagnosis is presented. This technique, w... more 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 Q3 .
El objeto de esta práctica consiste en la implementación de un circuito lógico que permita conoce... more El objeto de esta práctica consiste en la implementación de un circuito lógico que permita conocer el resultado de una comparación ponderada de dos grupos de dos elementos cada uno, equiparable al funcionamiento de una balanza. Fig. 1. Comparación ponderada. Las variables independientes A, B, C y D pueden adoptar únicamente los valores 0 o 1. Las funciones X, Y y Z expresarán el resultado de la comparación. Sólo una de las tres funciones valdrá 1 en cada instante: X si la balanza se inclina hacia la izquierda, Z si hacia la derecha e Y si permanece en equilibrio. Fig. 2. Circuito lógico a implementar. Para la implementación del circuito lógico, se empleará la herramienta software WebPack ISE y la placa de evaluación de Digilent, que incorpora la FPGA XC3S200 de la serie Spartan 3 del fabricante Xilinx, cuyo aspecto puede observarse en la figura 3. Las variables A, B, C y D serán generadas mediante los interruptores SW7, SW6, SW5 y SW4 y el valor de las funciones X, Y y Z se mostrará mediante los diodos led LD7, LD6 y LD5 respectivamente.
Applied Sciences
This paper proposes and evaluates the behavior of a new health indicator to estimate the capacity... more This paper proposes and evaluates the behavior of a new health indicator to estimate the capacity fade of lithium-ion batteries and their state of health (SOH). This health indicator is advantageous because it does not require the acquisition of data from full charge–discharge cycles, since it is calculated within a narrow SOC interval where the voltage vs. SOC relationship is very linear and that is within the usual transit range for most practical charge and discharge cycles. As a result, only a small fraction of the data points of a full charge–discharge cycle are required, reducing storage and computational resources while providing accurate results. Finally, by using the battery model defined by the Nernst equation, the behavior of future charge–discharge cycles can be accurately predicted, as shown by the results presented in this paper. The proposed approach requires the application of appropriate signal processing techniques, from discrete wavelet filtering to prediction met...
2013 World Electric Vehicle Symposium and Exhibition (EVS27), 2013
The objective of this paper is to give recommendations for the component sizing of a Parallel Plu... more The objective of this paper is to give recommendations for the component sizing of a Parallel Plug-in Hybrid Electric Vehicle (PHEV) studying the influence of the Electric Motor (EM) size, Final Drive ratio (FD), the Battery Capacity (BAT) and the Internal Combustion Engine (ICE). A multiple options for the size of the components are in the market and conflicting on the vehicle efficiency and functionality. Their selection is very important in order to achieve reduced fuel consumption and assure the vehicle performance with the minimum cost. This study explains a proposal methodology to solve this problem, firstly doing a problem model approach, then reducing his complexity doing a parameterization and finally analyzing the optimal variables for the multiple objectives. In this publication the component sizing is analysed using the Response Surface Methodology (RSM) of the Design of Experiments (DoE) technique. The parallel HEV has been parameterized and simulated to obtain the fuel...
IEEE Access, 2019
Artificial intelligence has bounced into industrial applications contributing several advantages ... more Artificial intelligence has bounced into industrial applications contributing several advantages to the field and have led to the possibility to open new ways to solve many actual problems. In this paper, a data-driven performance evaluation methodology is presented and applied to an industrial refrigeration system. The strategy takes advantage of the Multivariate Kernel Density Estimation technique and Self-Organizing Maps to develop a robust method, which is able to determine a near-optimal performance map, taking into account the system uncertainties and the multiple signals involved in the process. A normality model is used to detect and filter non-representative operating samples to subsequently develop a reliable performance map. The performance map allows to compare the plant assessment under the same operating conditions and permits to identify the potential system improvement capabilities. To ensure that the resulting evaluation is trustworthy, a robustness strategy is developed to identify either possible new operation conditions or abnormal situations in order to avoid uncertain assessments. Furthermore, the proposed approach is tested with real industrial plant data to validate the suitability of the method.
IEEE Access, 2016
This paper presents a condition-based monitoring methodology based on novelty detection applied t... more This paper presents a condition-based monitoring methodology based on novelty detection applied to industrial machinery. The proposed approach includes both, the classical classification of multiple a priori known scenarios, and the innovative detection capability of new operating modes not previously available. The development of condition-based monitoring methodologies considering the isolation capabilities of unexpected scenarios represents, nowadays, a trending topic able to answer the demanding requirements of the future industrial processes monitoring systems. First, the method is based on the temporal segmentation of the available physical magnitudes, and the estimation of a set of time-based statistical features. Then, a double feature reduction stage based on Principal Component Analysis and Linear Discriminant Analysis is applied in order to optimize the classification and novelty detection performances. The posterior combination of a Feed-forward Neural Network and One-Class Support Vector Machine allows the proper interpretation of known and unknown operating conditions. The effectiveness of this novel condition monitoring scheme has been verified by experimental results obtained from an automotive industry machine.
IEEE Access, 2016
Industrial process monitoring and modeling represent a critical step in order to achieve the para... more Industrial process monitoring and modeling represent a critical step in order to achieve the paradigm of zero defect manufacturing. The aim of this paper is to introduce the neo-fuzzy neuron method to be applied in industrial time series modeling. Its open structure and input independence provide fast learning and convergence capabilities, while assuring a proper accuracy and generalization in the modeled output. First, the auxiliary signals in the database are analyzed in order to find correlations with the target signal. Second, the neo-fuzzy neuron is configured and trained accordingly by means of the auxiliary signal, past instants, and dynamics information of the target signal. The proposed method is validated by means of real data from a Spanish copper rod industrial plant, in which a critical signal regarding copper refrigeration process is modeled. The obtained results indicate the suitability of the neo-fuzzy neuron method for industrial process modeling. INDEX TERMS Artificial intelligence, forecasting, fuzzy neural networks, industrial plants, predictive models, time series analysis.
IEEE Transactions on Instrumentation and Measurement, 2016
Advanced sensing strategies in the industrial sector are becoming a valued technological answer t... more Advanced sensing strategies in the industrial sector are becoming a valued technological answer to increase the performance and competitiveness. The development of enhanced sensing solutions considering both technology and monitoring requirements is, nowadays, subject of concern in the industrial maintenance field. In this context, this work presents a novel selfpowered wireless sensor applied to condition monitoring of gears. The proposed sensor is based on a modular architecture, offering multipoint sensing, local wireless communication, multi-source energy harvesting and embedded diagnosis algorithm for mechanical fractures detection based on acoustic emission analysis. The developments are complemented by means of a remote management interface, from which the user can configure the functionalities of the sensors, visualize the network status as well as analyze the diagnosis evolution. The sensor performance, in terms of power consumption and fault detection, has been analyzed by means of experimental results.
2011 IEEE International Symposium on Industrial Electronics, 2011
ABSTRACT Heeding the diagnostic requirements of electro- mechanical systems applied in automotive... more ABSTRACT Heeding the diagnostic requirements of electro- mechanical 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.
This paper presents and analyzed short circuit failures for permanent magnet synchronous motor (P... more This paper presents and analyzed short circuit failures for permanent magnet synchronous motor (PMSM). The study includes stated state and dynamic condition for experimental test. The stator current is analyzed by means of Hilbert-Huang transform (HHT) and energy spectrum.
The 2012 International Joint Conference on Neural Networks (IJCNN), 2012
EXIN CCA is an extension of the Curvilinear Component Analysis (CCA), which solves for the noninv... more 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.
Electrical Engineering, 2014
This work analyzes the behavior of surface-mounted permanent magnet synchronous motors (SPMSMs) o... more This work analyzes the behavior of surface-mounted permanent magnet synchronous motors (SPMSMs) operating under stator faults. The studied faults are resistive unbalance and winding inter-turn short circuits, which may lead to unbalanced conditions of the motor. Both faults may reduce motor efficiency and performance and produce premature ageing. This work develops an analytical model of the motor when operating under stator faults. By this way, the theoretical basis to understand the effects of resistive unbalance and stator winding inter-turn faults in SPMSMs is settled. This work also compares two methods for detecting and discriminating both faults. For this purpose, a method based on the analysis of the zero-sequence voltage component is presented, which is compared to the traditional method, i.e. the analysis of the stator currents harmonics. Both simulation and experimental results presented in this work show the potential of the zero-sequence voltage component method to provide helpful and reliable data to carry out a simultaneous diagnosis of resistive unbalance and stator winding inter-turn faults.
IEEE Transactions on Industrial Electronics, 2008
Motor-current-signature analysis has been successfully used in induction machines for fault diagn... more Motor-current-signature analysis has been successfully used in induction machines for fault diagnosis. The method, however, does not always achieve good results when the speed or the load torque is not constant, because this causes variations on the motor-slip and fast Fourier transform problems appear due to a nonstationary signal. This paper proposes a new method for motor fault detection, which analyzes the spectrogram based on a short-time Fourier transform and a further combination of wavelet and power-spectral-density (PSD) techniques, which consume a smaller amount of processing power. The proposed algorithms have been applied to detect broken rotor bars as well as shorted turns. Besides, a merit factor based on PSD is introduced as a novel approach for condition monitoring, and a further implementation of the algorithm is proposed. Theoretical development and experimental results are provided to support the research.
IEEE Transactions on Components and Packaging Technologies, 2010
The undesirable phenomenon of the contact bounce causes severe erosion of the contacts and, as a ... more The undesirable phenomenon of the contact bounce causes severe erosion of the contacts and, as a consequence, their electrical life and reliability are greatly reduced. On the other hand, the bounce of the armature can provoke reopening of the contacts, even when they have already been closed. This paper deals with the elimination of the bounce in both contacts and armature of a commercial dc core contactor. This is achieved by means of a current closed-loop controller, which only uses as input the current and voltage of the contactor's magnetizing coil. The logic control has been implemented in a low cost microcontroller. Moreover, the board control can be fed by either dc or ac, and either in 50 Hz or 60 Hz so as to extend its applicability. A set of data is obtained from the measurement of the position and velocity of the movable parts for different operating voltages, and the dynamic behavior of the contactor is discussed.
IEEE Aerospace and Electronic Systems Magazine, 2007
The latest advances in electric and electronic aircraft technologies from the point of view of an... more The latest advances in electric and electronic aircraft technologies from the point of view of an "all-elect~ric" aircraft are presented herein. Specifically, we describe the concept of a "More Electric Aircraft" (MEA), which involves removing the need for on-engine hydraulic power generation and bleed air off-takes, and the increasing use of power electronics in the starter/generation system of the main engine. Removal of the engine hydraulic pumps requires fully-operative electrical power actuators and mastery of the flight control architecture. The paper presents a general overview of the electrical power generation system and electric drives for the MIEA, with special regard to the flight controls. Some discussion regarding the interconnection of nodes and safety of buses and protocols in distributed systems is also presented.
International Journal of …, 2006
AbstractA remote microcontroller lab based on an 80C537 mock-up is presented. This paper present... more AbstractA remote microcontroller lab based on an 80C537 mock-up is presented. This paper presents a new way to interact with these kinds of systems via the Internet, giving the possibility for complete interaction. The Citrix Application Server is the platform which manages the ...
2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA), 2016
Industrial manufacturing plants often suffer from reliability problems during their day-today ope... more Industrial manufacturing plants often suffer from reliability problems during their day-today operations which have the potential for causing a great impact on the effectiveness and performance of the overall process and the sub-processes involved. Time-series forecasting of critical industrial signals presents itself as a way to reduce this impact by extracting knowledge regarding the internal dynamics of the process and advice any process deviations before it affects the productive process. In this paper, a novel industrial condition monitoring approach based on the combination of Self Organizing Maps for operating point codification and Recurrent Neural Networks for critical signal modeling is proposed. The combination of both methods presents a strong synergy, the information of the operating condition given by the interpretation of the maps helps the model to improve generalization, one of the drawbacks of recurrent networks, while assuring high accuracy and precision rates. Finally, the complete methodology, in terms of performance and effectiveness is validated experimentally with real data from a copper rod industrial plant.
IEEE Transactions on Industrial Electronics, 2009
This paper presents a new approach for the current acquisition system in motor fault detection ap... more 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 Rogowskicoil 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.
Shock and Vibration, 2016
Gearboxes and induction motors are important components in industrial applications and their moni... more Gearboxes and induction motors are important components in industrial applications and their monitoring condition is critical in the industrial sector so as to reduce costs and maintenance downtimes. There are several techniques associated with the fault diagnosis in rotating machinery; however, vibration and stator currents analysis are commonly used due to their proven reliability. Indeed, vibration and current analysis provide fault condition information by means of the fault-related spectral component identification. This work presents a methodology based on vibration and current analysis for the diagnosis of wear in a gearbox and the detection of bearing defect in an induction motor both linked to the same kinematic chain; besides, the location of the fault-related components for analysis is supported by the corresponding theoretical models. The theoretical models are based on calculation of characteristic gearbox and bearings fault frequencies, in order to locate the spectral ...
European Transactions on Electrical Power, 2010
A new technique for induction motor fault detection and diagnosis is presented. This technique, w... more 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 Q3 .
IEEE Transactions on Energy Conversion, 2010
This paper presents a novel method to diagnose demagnetization in permanent-magnet synchronous mo... more This paper presents a novel method to diagnose demagnetization in permanent-magnet synchronous motor (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.