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One of the most critical devices in an electrical system is the transformer. It is continuously under different electrical and mechanical stresses that can produce failures in its components and other electrical network devices. The short-circuited turns (SCTs) are a common winding failure. This type of fault has been widely studied in literature employing the vibration signals produced in the transformer. Although promising results have been obtained, it is not a trivial task if different severity levels and a common high-level noise are considered. This paper presents a methodology based on statistical time features (STFs) and support vector machines (SVM) to diagnose a transformer under several SCTs conditions. As STFs, 19 indicators from the transformer vibration signals are computed; then, the most discriminant features are selected using the Fisher score analysis, and the linear discriminant analysis is used for dimension reduction. Finally, a support vector machine classifier is employed to carry out the diagnosis in an automatic way. Once the methodology has been developed, it is implemented on a field-programmable gate array (FPGA) to provide a system-on-a-chip solution. A modified transformer capable of emulating different SCTs severities is employed to validate and test the methodology and its FPGA implementation. Results demonstrate the effectiveness of the proposal for diagnosing the transformer condition as an accuracy of 96.82% is obtained.
Jose Huerta-Rosales; David Granados-Lieberman; Arturo Garcia-Perez; David Camarena-Martinez; Juan Amezquita-Sanchez; Martin Valtierra-Rodriguez. Short-Circuited Turn Fault Diagnosis in Transformers by Using Vibration Signals, Statistical Time Features, and Support Vector Machines on FPGA. Sensors 2021, 21, 3598 .
AMA StyleJose Huerta-Rosales, David Granados-Lieberman, Arturo Garcia-Perez, David Camarena-Martinez, Juan Amezquita-Sanchez, Martin Valtierra-Rodriguez. Short-Circuited Turn Fault Diagnosis in Transformers by Using Vibration Signals, Statistical Time Features, and Support Vector Machines on FPGA. Sensors. 2021; 21 (11):3598.
Chicago/Turabian StyleJose Huerta-Rosales; David Granados-Lieberman; Arturo Garcia-Perez; David Camarena-Martinez; Juan Amezquita-Sanchez; Martin Valtierra-Rodriguez. 2021. "Short-Circuited Turn Fault Diagnosis in Transformers by Using Vibration Signals, Statistical Time Features, and Support Vector Machines on FPGA." Sensors 21, no. 11: 3598.
The fault diagnosis of electrical machines during startup transients has received increasing attention regarding the possibility of detecting faults early. Induction motors are no exception, and motor current signature analysis has become one of the most popular techniques for determining the condition of various motor components. However, in the case of inverter powered systems, the condition of a motor is difficult to determine from the stator current because fault signatures could overlap with other signatures produced by the inverter, low-slip operation, load oscillations, and other non-stationary conditions. This paper presents a speed signature analysis methodology for a reliable broken rotor bar diagnosis in inverter-fed induction motors. The proposed fault detection is based on tracking the speed fault signature in the time-frequency domain. As a result, different fault severity levels and load oscillations can be identified. The promising results show that this technique can be a good complement to the classic analysis of current signature analysis and reveals a high potential to overcome some of its drawbacks.
Tomas Garcia-Calva; Daniel Morinigo-Sotelo; Vanessa Fernandez-Cavero; Arturo Garcia-Perez; Rene Romero-Troncoso. Early Detection of Broken Rotor Bars in Inverter-Fed Induction Motors Using Speed Analysis of Startup Transients. Energies 2021, 14, 1469 .
AMA StyleTomas Garcia-Calva, Daniel Morinigo-Sotelo, Vanessa Fernandez-Cavero, Arturo Garcia-Perez, Rene Romero-Troncoso. Early Detection of Broken Rotor Bars in Inverter-Fed Induction Motors Using Speed Analysis of Startup Transients. Energies. 2021; 14 (5):1469.
Chicago/Turabian StyleTomas Garcia-Calva; Daniel Morinigo-Sotelo; Vanessa Fernandez-Cavero; Arturo Garcia-Perez; Rene Romero-Troncoso. 2021. "Early Detection of Broken Rotor Bars in Inverter-Fed Induction Motors Using Speed Analysis of Startup Transients." Energies 14, no. 5: 1469.
The electric spring (ES) is a contemporary device that has emerged as a viable alternative for solving problems associated with voltage and power stability in distributed generation-based smart grids (SG). In order to study the integration of ESs into the electrical network, the steady-state simulation models have been developed as an essential tool. Typically, these models require an equivalent electrical circuit of the in-test networks, which implies adding restrictions for its implementation in simulation software. These restrictions generate simplified models, limiting their application to specific scenarios, which, in some cases, do not fully apply to the needs of modern power systems. Therefore, a robust steady-state model for the ES is proposed in this work to adequately represent the power exchange of multiples ESs in radial micro-grids (µGs) and renewable energy sources regardless of their physical location and without the need of additional restrictions. For solving and controlling the model simulation, a modified backward–forward sweep method (MBFSM) is implemented. In contrast, the voltage control determines the operating conditions of the ESs from the steady-state solution and the reference voltages established for each ES. The model and algorithms of the solution and the control are validated with dynamic simulations. For the quasi-stationary case with distributed renewable generation, the results show an improvement higher than 95% when the ESs are installed. On the other hand, the MBFSM reduces the number of iterations by 34% on average compared to the BFSM.
Guillermo Tapia-Tinoco; David Granados-Lieberman; David A. Rodriguez-Alejandro; Martin Valtierra-Rodriguez; Arturo Garcia-Perez. A Robust Electric Spring Model and Modified Backward Forward Solution Method for Microgrids with Distributed Generation. Mathematics 2020, 8, 1326 .
AMA StyleGuillermo Tapia-Tinoco, David Granados-Lieberman, David A. Rodriguez-Alejandro, Martin Valtierra-Rodriguez, Arturo Garcia-Perez. A Robust Electric Spring Model and Modified Backward Forward Solution Method for Microgrids with Distributed Generation. Mathematics. 2020; 8 (8):1326.
Chicago/Turabian StyleGuillermo Tapia-Tinoco; David Granados-Lieberman; David A. Rodriguez-Alejandro; Martin Valtierra-Rodriguez; Arturo Garcia-Perez. 2020. "A Robust Electric Spring Model and Modified Backward Forward Solution Method for Microgrids with Distributed Generation." Mathematics 8, no. 8: 1326.
In this work, a new time-frequency tool based on minimum-norm spectral estimation is introduced for multiple fault detection in induction motors. Several diagnostic techniques are available to identify certain faults in induction machines; however, they generally give acceptable results only for machines operating under stationary conditions. Induction motors rarely operate under stationary conditions as they are constantly affected by load oscillations, speed waves, unbalanced voltages, and other external conditions. To overcome this issue, different time-frequency analysis techniques have been proposed for fault detection in induction motors under non-stationary regimes. However, most of them have low-resolution, low-accuracy or both. The proposed method employs the minimum-norm spectral estimation to provide high frequency resolution and accuracy in the time-frequency domain. This technique exploits the advantages of non-stationary conditions, where mechanical and electrical stresses in the machine are higher than in stationary conditions, improving the detectability of fault components. Numerical simulation and experimental results are provided to validate the effectiveness of the method in starting current analysis of induction motors.
Tomas A. Garcia-Calva; Daniel Morinigo-Sotelo; Oscar Duque-Perez; Arturo Garcia-Perez; Rene De J. Romero-Troncoso. Time-Frequency Analysis Based on Minimum-Norm Spectral Estimation to Detect Induction Motor Faults. Energies 2020, 13, 4102 .
AMA StyleTomas A. Garcia-Calva, Daniel Morinigo-Sotelo, Oscar Duque-Perez, Arturo Garcia-Perez, Rene De J. Romero-Troncoso. Time-Frequency Analysis Based on Minimum-Norm Spectral Estimation to Detect Induction Motor Faults. Energies. 2020; 13 (16):4102.
Chicago/Turabian StyleTomas A. Garcia-Calva; Daniel Morinigo-Sotelo; Oscar Duque-Perez; Arturo Garcia-Perez; Rene De J. Romero-Troncoso. 2020. "Time-Frequency Analysis Based on Minimum-Norm Spectral Estimation to Detect Induction Motor Faults." Energies 13, no. 16: 4102.
A new multiple signal classification (MUSIC)-based methodology is presented for detecting and locating multiple damage types in a truss-type structure subjected to dynamic excitations. The methodology is based mainly on two steps: in step 1, the MUSIC method is employed to obtain the pseudo-spectra of vibration signatures, healthy and damaged, to be used for damage detection. In step 2, a new damage index, based on the obtained pseudo-spectra, is proposed to measure the structure condition. Furthermore, the damage location is estimated according to the variation in the amplitudes of the estimated pseudo-spectra. The presented results show that the proposed methodology can make an accurate and reliable estimation of the condition and location of three specific damage conditions, i.e., loosened bolts, internal corrosion, and external corrosion.
Carlos A. Perez-Ramirez; Jose M. Machorro-Lopez; Martin Valtierra-Rodriguez; Juan P. Amezquita-Sanchez; Arturo Garcia-Perez; David Camarena-Martinez; Rene De J. Romero-Troncoso. Location of Multiple Damage Types in a Truss-Type Structure Using Multiple Signal Classification Method and Vibration Signals. Mathematics 2020, 8, 932 .
AMA StyleCarlos A. Perez-Ramirez, Jose M. Machorro-Lopez, Martin Valtierra-Rodriguez, Juan P. Amezquita-Sanchez, Arturo Garcia-Perez, David Camarena-Martinez, Rene De J. Romero-Troncoso. Location of Multiple Damage Types in a Truss-Type Structure Using Multiple Signal Classification Method and Vibration Signals. Mathematics. 2020; 8 (6):932.
Chicago/Turabian StyleCarlos A. Perez-Ramirez; Jose M. Machorro-Lopez; Martin Valtierra-Rodriguez; Juan P. Amezquita-Sanchez; Arturo Garcia-Perez; David Camarena-Martinez; Rene De J. Romero-Troncoso. 2020. "Location of Multiple Damage Types in a Truss-Type Structure Using Multiple Signal Classification Method and Vibration Signals." Mathematics 8, no. 6: 932.
Fault monitoring systems in Induction Motors (IMs) are in high demand since many production environments require yielding detection tools independent of their power supply. When IMs are inverter-fed, they become more complicated to diagnose via spectral techniques because those are susceptible to produce false positives. This paper proposes an innovative and reliable methodology to ease the monitoring and fault diagnosis of IMs. It employs fractional Gaussian windows determined from Caputo operators to stand out from spectral harmonic trajectories. This methodology was implemented and simulated to process real signals from an induction motor, in both healthy and faulty conditions. Results show that the proposed technique outperforms several traditional approaches by getting the clearest and most useful patterns for feature extraction purposes.
Nathaly Murcia-Sepúlveda; Jorge M. Cruz-Duarte; Ignacio Martin-Diaz; Arturo Garcia-Perez; J. Juan Rosales-García; Juan Gabriel Avina-Cervantes; Carlos Rodrigo Correa-Cely. Fractional Calculus-Based Processing for Feature Extraction in Harmonic-Polluted Fault Monitoring Systems. Energies 2019, 12, 3736 .
AMA StyleNathaly Murcia-Sepúlveda, Jorge M. Cruz-Duarte, Ignacio Martin-Diaz, Arturo Garcia-Perez, J. Juan Rosales-García, Juan Gabriel Avina-Cervantes, Carlos Rodrigo Correa-Cely. Fractional Calculus-Based Processing for Feature Extraction in Harmonic-Polluted Fault Monitoring Systems. Energies. 2019; 12 (19):3736.
Chicago/Turabian StyleNathaly Murcia-Sepúlveda; Jorge M. Cruz-Duarte; Ignacio Martin-Diaz; Arturo Garcia-Perez; J. Juan Rosales-García; Juan Gabriel Avina-Cervantes; Carlos Rodrigo Correa-Cely. 2019. "Fractional Calculus-Based Processing for Feature Extraction in Harmonic-Polluted Fault Monitoring Systems." Energies 12, no. 19: 3736.
Empirical mode decomposition (EMD)-based methods are powerful digital signal processing techniques because they do not need a priori information of the target signal due to their intrinsic adaptive behavior. Moreover, they can deal with non-linear and non-stationary signals. This paper presents the field programmable gate array (FPGA) implementation for the complete ensemble empirical mode decomposition (CEEMD) method, which is applied to the condition monitoring of an induction motor. The CEEMD method is chosen since it overcomes the performance of EMD and EEMD (ensemble empirical mode decomposition) methods. As a first application of the proposed FPGA-based system, the proposal is used as a processing technique for feature extraction in order to detect and classify broken rotor bar faults in induction motors. In order to obtain a complete online monitoring system, the feature extraction and classification modules are also implemented on the FPGA. Results show that an average effectiveness of 96% is obtained during the fault detection.
Martin Valtierra-Rodriguez; Juan Pablo Amezquita-Sanchez; Arturo Garcia-Perez; David Camarena-Martinez. Complete Ensemble Empirical Mode Decomposition on FPGA for Condition Monitoring of Broken Bars in Induction Motors. Mathematics 2019, 7, 783 .
AMA StyleMartin Valtierra-Rodriguez, Juan Pablo Amezquita-Sanchez, Arturo Garcia-Perez, David Camarena-Martinez. Complete Ensemble Empirical Mode Decomposition on FPGA for Condition Monitoring of Broken Bars in Induction Motors. Mathematics. 2019; 7 (9):783.
Chicago/Turabian StyleMartin Valtierra-Rodriguez; Juan Pablo Amezquita-Sanchez; Arturo Garcia-Perez; David Camarena-Martinez. 2019. "Complete Ensemble Empirical Mode Decomposition on FPGA for Condition Monitoring of Broken Bars in Induction Motors." Mathematics 7, no. 9: 783.
Transient motor current signature analysis has become a mature technique for fault detection in induction motors. By using start-up transients, the whole range of slip in the machine is exploited to generate well-defined fault frequency patterns. However, in the inverter-fed motor case, the fault-patterns are always close to the supply frequency and often of low amplitude. Therefore, it is difficult to distinguish and localize the fault-patterns. In this paper, a novel method is proposed to create a new fault-pattern; the proposed technique can concentrate the fault-harmonic in a specific frequency bandwidth and avoid the spectral leakage by reducing the supply frequency amplitude. The methodology has been validated through experimental tests carried out to detect broken rotor bar in an induction motor started through a voltage source inverter.
Tomas Alberto Garcia-Calva; Daniel Morinigo-Sotelo; Arturo Garcia-Perez; David Camarena-Martinez; Rene De Jesus Romero-Troncoso. Demodulation Technique for Broken Rotor Bar Detection in Inverter-Fed Induction Motor Under Non-Stationary Conditions. IEEE Transactions on Energy Conversion 2019, 34, 1496 -1503.
AMA StyleTomas Alberto Garcia-Calva, Daniel Morinigo-Sotelo, Arturo Garcia-Perez, David Camarena-Martinez, Rene De Jesus Romero-Troncoso. Demodulation Technique for Broken Rotor Bar Detection in Inverter-Fed Induction Motor Under Non-Stationary Conditions. IEEE Transactions on Energy Conversion. 2019; 34 (3):1496-1503.
Chicago/Turabian StyleTomas Alberto Garcia-Calva; Daniel Morinigo-Sotelo; Arturo Garcia-Perez; David Camarena-Martinez; Rene De Jesus Romero-Troncoso. 2019. "Demodulation Technique for Broken Rotor Bar Detection in Inverter-Fed Induction Motor Under Non-Stationary Conditions." IEEE Transactions on Energy Conversion 34, no. 3: 1496-1503.
Induction motor fault identification is essential to improve efficiency in industrial processes improving costs, production line and maintenance time. This paper presents a novel motor fault detection methodology based on Quaternion Signal Analysis (QSA). The proposed method establishes the quaternion coefficients as the value of motor current measurement and the variables x, y and z are the measurements from a triaxial-accelerometer mounted on the induction motor chassis. The method obtains the rotation of quaternions and applies quaternion rotation statistics such as mean, cluster shades and cluster prominence in order to get their features, and these are used to classify the motor state using the tree classification algorithm. This methodology is validated experimentally and compared to other methods to determine the efficiency of this method for feature detection and motor fault identification and classification.
Jose Luis Contreras Hernandez; Dora Luz Almanza-Ojeda; Sergio Ledesma-Orozco; Arturo Garcia-Perez; Rene De Jesus Romero-Troncoso; Mario-Alberto Ibarra-Manzano. Quaternion Signal Analysis Algorithm for Induction Motor Fault Detection. IEEE Transactions on Industrial Electronics 2019, 66, 8843 -8850.
AMA StyleJose Luis Contreras Hernandez, Dora Luz Almanza-Ojeda, Sergio Ledesma-Orozco, Arturo Garcia-Perez, Rene De Jesus Romero-Troncoso, Mario-Alberto Ibarra-Manzano. Quaternion Signal Analysis Algorithm for Induction Motor Fault Detection. IEEE Transactions on Industrial Electronics. 2019; 66 (11):8843-8850.
Chicago/Turabian StyleJose Luis Contreras Hernandez; Dora Luz Almanza-Ojeda; Sergio Ledesma-Orozco; Arturo Garcia-Perez; Rene De Jesus Romero-Troncoso; Mario-Alberto Ibarra-Manzano. 2019. "Quaternion Signal Analysis Algorithm for Induction Motor Fault Detection." IEEE Transactions on Industrial Electronics 66, no. 11: 8843-8850.
The Gaussian function has been employed in a vast number of practical and theoretical applications since it was proposed. Likewise, Gaussian function and its ordinary derivatives are considered as powerful tools for signal processing and control applications, e.g., smoothing, sampling, change detection, blob detection, and transforms based on the Hermite polynomials. Nonetheless, it has impressive characteristics hidden amongst its fractional derivatives eager to be explored and studied in-depth. This work proposes a closed formula for the (n+ν)(n+ν)–order fractional derivative of the Gaussian function, based on the Caputo–Fabrizio definition, as an approach for analysing those attributes. The obtained expression was numerically tested with several fractional orders, and their resulting behaviours were eventually analysed. Finally, three practical applications on signal processing via this closed formula were discussed, i.e., customisable wavelets, image processing filters, and Rayleigh distributions.
Jorge M. Cruz–Duarte; Juan Rosales–Garcia; C. Rodrigo Correa–Cely; Arturo Garcia–Perez; Juan Gabriel Avina–Cervantes. A closed form expression for the Gaussian–based Caputo–Fabrizio fractional derivative for signal processing applications. Communications in Nonlinear Science and Numerical Simulation 2018, 61, 138 -148.
AMA StyleJorge M. Cruz–Duarte, Juan Rosales–Garcia, C. Rodrigo Correa–Cely, Arturo Garcia–Perez, Juan Gabriel Avina–Cervantes. A closed form expression for the Gaussian–based Caputo–Fabrizio fractional derivative for signal processing applications. Communications in Nonlinear Science and Numerical Simulation. 2018; 61 ():138-148.
Chicago/Turabian StyleJorge M. Cruz–Duarte; Juan Rosales–Garcia; C. Rodrigo Correa–Cely; Arturo Garcia–Perez; Juan Gabriel Avina–Cervantes. 2018. "A closed form expression for the Gaussian–based Caputo–Fabrizio fractional derivative for signal processing applications." Communications in Nonlinear Science and Numerical Simulation 61, no. : 138-148.
PurposeAbout 13 to 44 per cent of motor faults are caused by bearing failures in induction motors (IMs), where lubrication plays a significant role in maintaining rotating equipment because it minimizes friction and prevents wear by separating parts that move next to each other, and more than 35 per cent of bearing failures can be attributed to improper lubrication. An excessive amount of grease causes the rollers or balls to slide along the race instead of turning, and the grease will actually churn. This churning action will eventually wear down the base oil of the grease and all that will be left to lubricate the bearing is a thickener system with little or no lubricating properties. The heat generated from the churning, insufficient lubricating oil will begin to harden the grease, and this will prevent any new grease added to the bearing from reaching the rolling elements, with the consequence of bearing failure and equipment downtime. Regarding the case of grease excess in bearings, this case has not been sufficiently studied. This work aims to present an effective methodology applied to the detection and automatic classification of mechanical bearing faults and bearing excessively lubricated conditions in an IM through the Margenau-Hill distribution (MHD) and artificial neural networks (ANNs), where the obtained results demonstrate the correct classification of the studied cases.Design/methodology/approachThis work proposed an effective methodology applied to the detection and automatic classification of mechanical bearing faults and bearing excessively lubricated conditions in an IM through the MHD and ANNs.FindingsIn this paper, three cases of study for a bearing in an IM are studied, detected and classified correctly by combining some methods. The marginal frequency is obtained from the MHD, which in turn is achieved from the stator current signal, and a total of six features are estimated from the power spectrum, and these features are forwarded to the designed ANN with three output neurons, where each one represents a condition in the IM: healthy bearing, mechanical bearing fault and excessively lubricated bearing.Practical implicationsThe proposed methodology can be applied to other applications; it could be useful to use a time–frequency representation through the MHD for obtaining the energy density distribution of the signal frequency components through time for analysis, evaluation and identification of faults or conditions in the IM for example; therefore, the proposed methodology has a generalized nature that allows its application for detecting other conditions or even multiple conditions under different working conditions by a proper calibration.Originality/valueThe lubrication plays a significant role in maintaining rotating equipment because it minimizes friction and prevents wear by separating parts that move next to each other, and more than 35 per cent of bearing failures can be attributed to improper lubrication and it negatively affects the efficiency of the motor, resulting in higher operating costs. Therefore, in this work, a new methodology is proposed for the detection and automatic classification of mechanical bearing faults and bearing excessively lubricated conditions in an IM through the MHD and ANNs. The proposed methodology uses a total of six features estimated from the power spectrum, and these features are sent to the designed ANN with three output neurons, where each one represents a condition in the IM: healthy bearing, mechanical bearing fault and excessively lubricated bearing. From the obtained results, it was demonstrated that the proposed approach achieves higher classification performance, compared to short-time Fourier transform, Gabor transform and Wigner-Ville distribution methods, allowing to identify mechanical bearing faults and bearing excessively lubricated conditions in an IM, with a remarkable 100 per cent effectiveness during classification for treated cases. Also, the proposed methodology has a generalized nature that allows its application for detecting other conditions or even multiple conditions under different working conditions by a proper calibration.
Misael Lopez-Ramirez; Rene J. Romero-Troncoso; Daniel Moriningo-Sotelo; Oscar Duque-Perez; David Camarena-Martinez; Arturo Garcia-Perez. Discriminating the lubrication condition from the rotor bearing fault in induction motors using Margenau-Hill frequency distribution and artificial neural networks. Industrial Lubrication and Tribology 2017, 69, 970 -979.
AMA StyleMisael Lopez-Ramirez, Rene J. Romero-Troncoso, Daniel Moriningo-Sotelo, Oscar Duque-Perez, David Camarena-Martinez, Arturo Garcia-Perez. Discriminating the lubrication condition from the rotor bearing fault in induction motors using Margenau-Hill frequency distribution and artificial neural networks. Industrial Lubrication and Tribology. 2017; 69 (6):970-979.
Chicago/Turabian StyleMisael Lopez-Ramirez; Rene J. Romero-Troncoso; Daniel Moriningo-Sotelo; Oscar Duque-Perez; David Camarena-Martinez; Arturo Garcia-Perez. 2017. "Discriminating the lubrication condition from the rotor bearing fault in induction motors using Margenau-Hill frequency distribution and artificial neural networks." Industrial Lubrication and Tribology 69, no. 6: 970-979.
Motor Current signature analysis (MCSA) is a proven method for fault detection in induction motors. Usually, it is used classical techniques based on Fast Fourier Transform (FFT) or its extensions for MCSA. However, these techniques have disadvantages such as limitations in frequency resolution or time resolution, so it is difficult to know when a frequency component occurs, which is relevant for the study of non-stationary signals in the frequency domain. The present work presents the reassignment technique for the detection of broken rotor bar in induction motors fed with the line during the startup transient, which is tested with real signals, then the results are compared to the classic Short Time Fourier Transform (STFT); where it is obtained better resolution in time and frequency with the technique proposed compared with STFT, making possible to detect clearly when appear a frequency component related with the broken rotor bar fault.
Noe A. Ojeda-Aguirre; David Camarena- Martinez; Arturo Garcia-Perez; Rene J. Romero- Troncoso; Martin Valtierra-Rodriguez; Juan P. Amezquita-Sanchez. Reassignment technique for detection of broken rotor bar fault in induction motors. 2017 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) 2017, 1 -6.
AMA StyleNoe A. Ojeda-Aguirre, David Camarena- Martinez, Arturo Garcia-Perez, Rene J. Romero- Troncoso, Martin Valtierra-Rodriguez, Juan P. Amezquita-Sanchez. Reassignment technique for detection of broken rotor bar fault in induction motors. 2017 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC). 2017; ():1-6.
Chicago/Turabian StyleNoe A. Ojeda-Aguirre; David Camarena- Martinez; Arturo Garcia-Perez; Rene J. Romero- Troncoso; Martin Valtierra-Rodriguez; Juan P. Amezquita-Sanchez. 2017. "Reassignment technique for detection of broken rotor bar fault in induction motors." 2017 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) , no. : 1-6.
This paper proposes a conceptual design strategy for an optimal cooling system, typically used in several microelectronic devices. The methodology was implemented using the entropy generation minimization (EGM) criterion, powered by the cuckoo search (CS) algorithm. EGM-CS methodology was addressed to design rectangular microchannel heat sinks, utilizing high thermal conductive graphite as build material and a water-based colloid as coolant. This strategy was implemented under different hydrodynamic conditions, where it showed strong capability to achieve optimal designs with flowing nanofluids in either laminar or turbulent regimes. In addition, two types of nanoparticles were considered, i.e., Al and TiO₂, with several values of volume fraction, as a passive mechanism for enhancing overall system performance. It was corroborated that all considered flowing colloids in laminar regime improve the thermal efficiency of the system. Moreover, an additional enhancement of this performance was observed with smaller nanoparticle concentrations (e.g., 0.01 wt/wt%), which also reduces the side effects due to nanoclustering. Furthermore, fluids with titanium dioxide nanoparticles showed slightly better thermal performance enhancement compared to aluminum particles.
Jorge M. Cruz-Duarte; Arturo Garcia-Perez; Ivan Amaya; C. Rodrigo Correa-Cely; Rene J. Romero-Troncoso; Juan Gabriel Avina-Cervantes. Design of Microelectronic Cooling Systems Using a Thermodynamic Optimization Strategy Based on Cuckoo Search. IEEE Transactions on Components, Packaging and Manufacturing Technology 2017, 7, 1804 -1812.
AMA StyleJorge M. Cruz-Duarte, Arturo Garcia-Perez, Ivan Amaya, C. Rodrigo Correa-Cely, Rene J. Romero-Troncoso, Juan Gabriel Avina-Cervantes. Design of Microelectronic Cooling Systems Using a Thermodynamic Optimization Strategy Based on Cuckoo Search. IEEE Transactions on Components, Packaging and Manufacturing Technology. 2017; 7 (11):1804-1812.
Chicago/Turabian StyleJorge M. Cruz-Duarte; Arturo Garcia-Perez; Ivan Amaya; C. Rodrigo Correa-Cely; Rene J. Romero-Troncoso; Juan Gabriel Avina-Cervantes. 2017. "Design of Microelectronic Cooling Systems Using a Thermodynamic Optimization Strategy Based on Cuckoo Search." IEEE Transactions on Components, Packaging and Manufacturing Technology 7, no. 11: 1804-1812.
Squirrel-cage induction motors (SCIMs) are key machines in many industrial applications. In this regard, the monitoring of their operating condition aiming at avoiding damage and reducing economical losses has become a demanding task for industry. In the literature, several techniques and methodologies to detect faults that affect the integrity and performance of SCIMs have been proposed. However, they have only been focused on analyzing either the start-up transient or the steady-state operation regimes, two common operating scenarios in real practice. In this work, a novel methodology for broken rotor bar (BRB) detection in SCIMs during both start-up and steady-state operation regimes is proposed. It consists of two main steps. In the first one, the analysis of three-axis vibration signals using fractal dimension (FD) theory is carried out. Since different FD-based algorithms can give different results, three algorithms named Katz' FD, Higuchi's FD, and box dimension, are tested. In the second step, a fuzzy logic system for each regime is presented for automatic diagnosis. To validate the proposal, a motor with different damage levels has been tested: one with a partially BRB, a second with one fully BRB, and the third with two BRBs. The obtained results demonstrate the proposed effectiveness.
Juan P Amezquita-Sanchez; Martin Valtierra-Rodriguez; Carlos Andres Perez-Ramirez; David Camarena-Martinez; Arturo Garcia-Perez; Rene J Romero-Troncoso. Fractal dimension and fuzzy logic systems for broken rotor bar detection in induction motors at start-up and steady-state regimes. Measurement Science and Technology 2017, 28, 075001 .
AMA StyleJuan P Amezquita-Sanchez, Martin Valtierra-Rodriguez, Carlos Andres Perez-Ramirez, David Camarena-Martinez, Arturo Garcia-Perez, Rene J Romero-Troncoso. Fractal dimension and fuzzy logic systems for broken rotor bar detection in induction motors at start-up and steady-state regimes. Measurement Science and Technology. 2017; 28 (7):075001.
Chicago/Turabian StyleJuan P Amezquita-Sanchez; Martin Valtierra-Rodriguez; Carlos Andres Perez-Ramirez; David Camarena-Martinez; Arturo Garcia-Perez; Rene J Romero-Troncoso. 2017. "Fractal dimension and fuzzy logic systems for broken rotor bar detection in induction motors at start-up and steady-state regimes." Measurement Science and Technology 28, no. 7: 075001.
Elisee Ilunga-Mbuyamba; Juan Gabriel Avina–Cervantes; Arturo Garcia–Perez; Rene De Jesus Romero–Troncoso; Hugo Aguirre–Ramos; Ivan Cruz–Aceves; Claire Chalopin. Localized active contour model with background intensity compensation applied on automatic MR brain tumor segmentation. Neurocomputing 2017, 220, 84 -97.
AMA StyleElisee Ilunga-Mbuyamba, Juan Gabriel Avina–Cervantes, Arturo Garcia–Perez, Rene De Jesus Romero–Troncoso, Hugo Aguirre–Ramos, Ivan Cruz–Aceves, Claire Chalopin. Localized active contour model with background intensity compensation applied on automatic MR brain tumor segmentation. Neurocomputing. 2017; 220 ():84-97.
Chicago/Turabian StyleElisee Ilunga-Mbuyamba; Juan Gabriel Avina–Cervantes; Arturo Garcia–Perez; Rene De Jesus Romero–Troncoso; Hugo Aguirre–Ramos; Ivan Cruz–Aceves; Claire Chalopin. 2017. "Localized active contour model with background intensity compensation applied on automatic MR brain tumor segmentation." Neurocomputing 220, no. : 84-97.
Multiple signal classification (MUSIC) algorithm has been widely used to obtain high-resolution frequency estimation for an accurate identification of frequency components in low signal-to-noise ratios. One of the main drawbacks associated with the use of the MUSIC algorithm is that its performance is fully deteriorated when a wrong frequency signal dimension order is chosen, producing that some spurious frequencies could appear or some signal frequencies could be missing. In this paper, it is proposed a multi-objective optimization method to address the frequency signal dimension order problem. The proposed approach is based on a novel feature extraction of frequency components, which allows determining an adequate frequency signal dimension order. The methodology has been integrated as part of the MUSIC algorithm, and it can find the optimal order within a predefined frequency bandwidth, where the user is interested to find a frequency component. To evaluate the effectiveness of the proposed methodology, experimental results from several current signals obtained in the detection of broken rotor bar fault in induction motors have been tested.
Gerardo Trejo-Caballero; Horacio Rostro-Gonzalez; Rene De Jesus Romero-Troncoso; Carlos Hugo Garcia Capulin; Oscar G Ibarra-Manzano; Juan Gabriel Avina-Cervantes; Arturo Garcia-Perez. Multiple signal classification based on automatic order selection method for broken rotor bar detection in induction motors. Electrical Engineering 2016, 99, 987 -996.
AMA StyleGerardo Trejo-Caballero, Horacio Rostro-Gonzalez, Rene De Jesus Romero-Troncoso, Carlos Hugo Garcia Capulin, Oscar G Ibarra-Manzano, Juan Gabriel Avina-Cervantes, Arturo Garcia-Perez. Multiple signal classification based on automatic order selection method for broken rotor bar detection in induction motors. Electrical Engineering. 2016; 99 (3):987-996.
Chicago/Turabian StyleGerardo Trejo-Caballero; Horacio Rostro-Gonzalez; Rene De Jesus Romero-Troncoso; Carlos Hugo Garcia Capulin; Oscar G Ibarra-Manzano; Juan Gabriel Avina-Cervantes; Arturo Garcia-Perez. 2016. "Multiple signal classification based on automatic order selection method for broken rotor bar detection in induction motors." Electrical Engineering 99, no. 3: 987-996.
For industry, the induction motors are essential elements in production chains. Despite the robustness of induction motors, they are susceptible to failures. The broken rotor bar (BRB) fault in induction motors has received special attention since one of its characteristics is that the motor can continue operating with apparent normality; however, at certain point the fault may cause severe damage to the motor. In this work, a methodology to detect BRBs using vibration signals is proposed. The methodology uses the Shannon entropy to quantify the amount of information provided by the vibration signals, which changes due to the presence of new frequency components associated with the fault. For automatic diagnosis, the -means cluster algorithm and a decision-making unit that looks for the nearest cluster through the Euclidian distance are applied. Unlike other reported works, the proposal can diagnose the BRB condition during startup transient and steady state regimes of operation. Additionally, the proposal is also implemented into a field programmable gate array in order to offer a low-cost and low-complex online monitoring system. The obtained results demonstrate the proposal effectiveness to diagnose half, one, and two BRBs.1. IntroductionInductions motors are widely used in many applications because of their easy maintenance, ruggedness, low cost, versatility, and ease control [1]. During their service life, they are subject to unavoidable failures as a result of mechanical, environmental, thermal, and electrical stresses [2]. These faults such as bearing faults, air gap eccentricity, and broken rotor bars (BRBs) can yield a reduction on production, product quality, and an increase on costs, besides being a hazard for people and machinery [3]. Among the different faults that can occur in induction machines, BRB is a silent failure that allows operating the motor with apparent normality, but it can cause an excessive vibration, a change in current consumption, and higher thermal stress with catastrophic consequences if the situation is not solved at early stages [4, 5]. In this regard, condition monitoring equipment has become an essential tool in many industrial areas. Yet, this task is very challenging because depending on the application the motor may be subject to transient and/or steady (nominal) regimes of operation, which changes its mechanical and electrical conditions by affecting and limiting the performance of equipment that only operates in a specific regime. From this point of view, an online and real-time monitoring system for an early detection of BRB in transient and steady regimes is a needed equipment in many industrial areas, since it will allow scheduling maintenance operations in order to minimize its negative impact as well as saving time and money.During the last decade, several vibration and current analysis-based processing techniques for BRB detection have been proposed. The conventional signal processing technique used to perform this task is the fast Fourier transform (FFT) [6–8]. However, it is limited in its capability for extracting features from signals that exhibit nonlinear and nonstationary characteristics, besides being susceptible to noise, making a correct identification of features related to the BRB fault difficult [9]. More recently, other powerful signal processing techniques, such as multiple signal classification (MUSIC) algorithm [10, 11], wavelet transform (WT) [9, 12–16], Empirical mode decomposition combined with Hilbert transform known as Hilbert-Huang transform (HHT) [16], and Wigner-Ville distribution (WVD) [17], have been used for BRB detection. Nevertheless, although prominent results have been obtained, the aforementioned signal processing techniques present some unresolved difficulties. For instance, MUSIC requires a priori knowledge of the interest frequencies and consumes significant computational resources [11]. The WT capabilities are significantly degraded in noisy signals, and the mother wavelet has to be appropriately chosen to obtain reliable results [4]. On the other hand, the WVD introduces cross-term interference in the estimated signal components, which inhibits the efficient estimation of the instantaneous frequencies, besides suffering aliasing problem [18]. The HHT suffers from the mode mixing effect, which means that waves with the same frequency are assigned to different intrinsic mode functions, affecting the accurate estimation of the instantaneous frequencies. In general, many advantages and disadvantages of the aforementioned techniques may be further discussed; yet, from a monitoring equipment viewpoint, two aspects become important. The first one is the performance capabilities; it means that the equipment does not degrade its performance when it analyzes transient or stationary signals. This desirable feature may be achieved either using a nonsusceptible signal processing technique or using different techniques for each scenario. The second one is the complexity since it may compromise the online analysis if low-end digital signal processors are used. In this regard, it would be desirable to have a signal processing algorithm with both the ability of identifying suitable and reliable features of signals for identifying BRB fault in different operating states and a low complexity for online analysis.Similar to signal processing techniques, the classification algorithms play an important role in the automatic diagnosis of faults [19]. Different classification techniques such as neural networks [20, 21] and fuzzy logic [22, 23] have been successfully applied for monitoring the condition of induction motors. Unfortunately, the neural networks and other conventional artificial intelligent techniques require enough samples and have limitations on generalization of results in models that can overfit the samples [13]. Therefore, having in mind that online monitoring equipment may require low-complexity procedures, a classification algorithm with a suitable accuracy without the need of complicated processing, that requires a small number of samples, and, mainly, that allows developing a methodology capable of identifying several faults in different scenarios is a desirable tool. A promising classification technique is the -means algorithm. It is a well-known signal classification technique that has been successfully utilized in many applications such as neuroscience [24], structural engineering [25], and mechanics [26]. This approach provides a high accuracy and good generalization for a small number of features; besides its computational cost during and after its design is relatively low.In this work, a methodology to detect automatically the BRB fault in induction motors using vibration signals is presented. The proposal considers the analysis of both the startup transient and the steady state of operation, which is very important since the induction motor may be subject to both scenarios in real applications; besides, an implementation into a field programmable gate array (FPGA) is also presented as system-on-chip (SoC) solution. This allows offering a system for online and continuous monitoring. Regarding the BRB condition, half, one, and two bars are considered. For the analysis, the Shannon entropy is used as a measure of the information contained in the vibration signals. This information presents changes associated with the fault. Then, the obtained entropy values are classified for automatic diagnosis using the -means algorithm. The results show that the proposal can be a low-complex and suitable tool for BRB detection in both the startup transient and the steady state of operation.2. Theoretical BackgroundIn this section, the two main topics of the proposed methodology are briefly described.2.1. EntropyIn information theory, entropy describes how much information about the data randomness is provided by a signal or event [27]. It has been used in image processing [28], in gearbox fault detection [29], in structural health monitoring [30], for analysis of electroencephalogram signals to diagnose the patient’s clinical condition [31], and in fault motor diagnosis [15, 19, 32] among others. In particular, Shannon entropy, named after Claude Shannon, of a random signal with possible outcomes and with a probability of can be computed as follows: where it is bounded by .Due to the number of applications and to the requirements of processing time, a hardware processing unit based on FPGA for entropy estimation is presented by [33], where a simplified mathematical expression is given as follows:where is the incidence rate or histogram of a signal ; therefore, is . In general, this expression follows the structure shown in Figure 1.Figure 1: General structure for the entropy processor.2.2. -MeansIn general, cluster analysis consists of creating groups of objects with similar features [34]. It implies that an object has to comply or has certain features for belonging to a specific group. In this regard, a classification task for unseen data can be carried out by looking for a group that fits better.-means is a simple and popular algorithm to solve clustering problems. The goal of the algorithm is to divide a data set with data into clusters. The number of clusters is fixed a priori. The objective function based on squared Euclidian distances is calculated as follows [35]:where is the number of objects of each th cluster, is the th object of the th cluster, and is the center of the th cluster, which is defined asThe overall procedure is summarized as follows [34]: (1) select randomly the initial positions of the -centroids; (2) assign the data set to the closest centroid; (3) relocate iteratively the -centroids in order to minimize the objective function. It is worth noting that -means algorithm is sensitive to the initial clusters; however, it can be applied a number of times in order to find either the global objective function minimum o
David Camarena-Martinez; Martin Valtierra-Rodriguez; Juan P. Amezquita-Sanchez; David Granados-Lieberman; Rene J. Romero-Troncoso; Arturo Garcia-Perez. Shannon Entropy and K -Means Method for Automatic Diagnosis of Broken Rotor Bars in Induction Motors Using Vibration Signals. Shock and Vibration 2016, 2016, 1 -10.
AMA StyleDavid Camarena-Martinez, Martin Valtierra-Rodriguez, Juan P. Amezquita-Sanchez, David Granados-Lieberman, Rene J. Romero-Troncoso, Arturo Garcia-Perez. Shannon Entropy and K -Means Method for Automatic Diagnosis of Broken Rotor Bars in Induction Motors Using Vibration Signals. Shock and Vibration. 2016; 2016 ():1-10.
Chicago/Turabian StyleDavid Camarena-Martinez; Martin Valtierra-Rodriguez; Juan P. Amezquita-Sanchez; David Granados-Lieberman; Rene J. Romero-Troncoso; Arturo Garcia-Perez. 2016. "Shannon Entropy and K -Means Method for Automatic Diagnosis of Broken Rotor Bars in Induction Motors Using Vibration Signals." Shock and Vibration 2016, no. : 1-10.
In electric power systems, there are always power quality disturbances (PQDs). Usually, noise contamination interferes with their detection and classification. Common methods perform frequency or time-frequency analyses on the power distribution signal for detecting and classifying a limited number of PQDs with some difficulties at low signal-to-noise ratio (SNR). In this regard, recently proposed methodologies for PQD detection estimate several parameters and apply distinct signal processing techniques to improve the detection of PQD. In this work, a novel methodology that merges empirical mode decomposition (EMD), the moments of a random variable, and an artificial neural network (ANN) is proposed for detecting and classifying different PQD. The proposed method estimates skewness, kurtosis, and Shannon entropy from the EMD of one-phase voltage/current signal. Then, an ANN is in charge of classifying the input signal into one of nine different classes for PQD, receiving these parameters as inputs. The effectiveness of the proposed method was verified through computer simulations and experimentation with real data. Obtained results demonstrate its high effectiveness reaching an outstanding 100% of accuracy in detecting and classifying all treated PQD through a few number of parameters, outperforming most of previously proposed approaches.
Misael Lopez-Ramirez; Luis Ledesma-Carrillo; Eduardo Cabal-Yepez; Carlos Rodriguez-Donate; Homero Miranda-Vidales; Arturo Garcia-Perez. EMD-Based Feature Extraction for Power Quality Disturbance Classification Using Moments. Energies 2016, 9, 565 .
AMA StyleMisael Lopez-Ramirez, Luis Ledesma-Carrillo, Eduardo Cabal-Yepez, Carlos Rodriguez-Donate, Homero Miranda-Vidales, Arturo Garcia-Perez. EMD-Based Feature Extraction for Power Quality Disturbance Classification Using Moments. Energies. 2016; 9 (7):565.
Chicago/Turabian StyleMisael Lopez-Ramirez; Luis Ledesma-Carrillo; Eduardo Cabal-Yepez; Carlos Rodriguez-Donate; Homero Miranda-Vidales; Arturo Garcia-Perez. 2016. "EMD-Based Feature Extraction for Power Quality Disturbance Classification Using Moments." Energies 9, no. 7: 565.
Arturo Garcia-Perez; Juan Pablo Amezquita-Sanchez; Daniel Morinigo-Sotelo; Konstantinos N. Gyftakis. Vibration Analysis as a Diagnosis Tool for Health Monitoring of Industrial Machines. Shock and Vibration 2016, 2016, 1 -2.
AMA StyleArturo Garcia-Perez, Juan Pablo Amezquita-Sanchez, Daniel Morinigo-Sotelo, Konstantinos N. Gyftakis. Vibration Analysis as a Diagnosis Tool for Health Monitoring of Industrial Machines. Shock and Vibration. 2016; 2016 ():1-2.
Chicago/Turabian StyleArturo Garcia-Perez; Juan Pablo Amezquita-Sanchez; Daniel Morinigo-Sotelo; Konstantinos N. Gyftakis. 2016. "Vibration Analysis as a Diagnosis Tool for Health Monitoring of Industrial Machines." Shock and Vibration 2016, no. : 1-2.
This article presents an alternative approach for the design of a heat sink as a solution of the thermal management problem in microelectronic components. The design was carried out based on the entropy generation minimisation criterion and it was optimised through three different non-conventional methods, such as: Unified Particle Swarm Optimisation, Spiral Optimisation and Cuckoo Search. A rectangular microchannel heat sink manufactured from a High Thermal Conductive Graphite was considered and a colloid of H2O–TiO2 was used as working fluid. The performance of this nanofluid was compared against traditional fluids like air and ammonia gas. Also, nanoparticles were considered and studied at different volume fractions. Moreover, the performance solving the thermal problem was compared for the implemented global optimisation techniques. It was noticed that nanofluids represent a good alternative for designs with lower volume flow rates, requiring a lower nanoparticle volume fraction. Furthermore, the Cuckoo Search algorithm was more accurate and faster than the other two methods.
Jorge M. Cruz-Duarte; Arturo Garcia-Perez; Ivan M. Amaya-Contreras; C. Rodrigo Correa-Cely. Designing a microchannel heat sink with colloidal coolants through the entropy generation minimisation criterion and global optimisation algorithms. Applied Thermal Engineering 2016, 100, 1052 -1062.
AMA StyleJorge M. Cruz-Duarte, Arturo Garcia-Perez, Ivan M. Amaya-Contreras, C. Rodrigo Correa-Cely. Designing a microchannel heat sink with colloidal coolants through the entropy generation minimisation criterion and global optimisation algorithms. Applied Thermal Engineering. 2016; 100 ():1052-1062.
Chicago/Turabian StyleJorge M. Cruz-Duarte; Arturo Garcia-Perez; Ivan M. Amaya-Contreras; C. Rodrigo Correa-Cely. 2016. "Designing a microchannel heat sink with colloidal coolants through the entropy generation minimisation criterion and global optimisation algorithms." Applied Thermal Engineering 100, no. : 1052-1062.