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Sensorless speed estimation has been extensively studied for its use in control schemes. Nevertheless, it is also a key step when applying Motor Current Signature Analysis to induction motor diagnosis: accurate speed estimation is vital to locate fault harmonics, and prevent false positives and false negatives, as shown at the beginning of the paper through a real industrial case. Unfortunately, existing sensorless speed estimation techniques either do not provide enough precision for this purpose or have limited applicability. Currently, this is preventing Industry 4.0 from having a precise and automatic system to monitor the motor condition. Despite its importance, there is no research published reviewing this topic. To fill this gap, this paper investigates, from both theoretical background and an industrial application perspective, the reasons behind these problems. Therefore, the families of sensorless speed estimation techniques, mainly conceived for sensorless control, are here reviewed and thoroughly analyzed from the perspective of their use for diagnosis. Moreover, the algorithms implemented in the two leading commercial diagnostic devices are analyzed using real examples from a database of industrial measurements belonging to 79 induction motors. The analysis and discussion through the paper are synthesized to summarize the lacks and weaknesses of the industry application of these methods, which helps to highlight the open problems, challenges and research prospects, showing the direction in which research efforts have to be made to solve this important problem.
Jorge Bonet-Jara; Alfredo Quijano-Lopez; Daniel Morinigo-Sotelo; Joan Pons-Llinares. Sensorless Speed Estimation for the Diagnosis of Induction Motors via MCSA. Review and Commercial Devices Analysis. Sensors 2021, 21, 5037 .
AMA StyleJorge Bonet-Jara, Alfredo Quijano-Lopez, Daniel Morinigo-Sotelo, Joan Pons-Llinares. Sensorless Speed Estimation for the Diagnosis of Induction Motors via MCSA. Review and Commercial Devices Analysis. Sensors. 2021; 21 (15):5037.
Chicago/Turabian StyleJorge Bonet-Jara; Alfredo Quijano-Lopez; Daniel Morinigo-Sotelo; Joan Pons-Llinares. 2021. "Sensorless Speed Estimation for the Diagnosis of Induction Motors via MCSA. Review and Commercial Devices Analysis." Sensors 21, no. 15: 5037.
Induction motors are very robust, with low operating and maintenance costs, and are therefore widely used in industry. They are, however, not fault-free, with bearings and rotor bars accounting for about 50% of the total failures. This work presents a two-stage approach for three-phase induction motors diagnosis based on mutual information measures of the current signals, principal component analysis, and intelligent systems. In a first stage, the fault is identified, and, in a second stage, the severity of the defect is diagnosed. A case study is presented where different severities of bearing wear and bar breakage are analyzed. To test the robustness of the proposed method, voltage imbalances and load torque variations are considered. The results reveal the promising performance of the proposal with overall accuracies above 90% in all cases, and in many scenarios 100% of the cases are correctly classified. This work also evaluates different strategies for extracting the signals, showing the possibility of reducing the amount of information needed. Results show a satisfactory relation between efficiency and computational cost, with decreases in accuracy of less than 4% but reducing the amount of data by more than 90%, facilitating the efficient use of this method in embedded systems.
Gustavo Bazan; Alessandro Goedtel; Oscar Duque-Perez; Daniel Morinigo-Sotelo. Multi-Fault Diagnosis in Three-Phase Induction Motors Using Data Optimization and Machine Learning Techniques. Electronics 2021, 10, 1462 .
AMA StyleGustavo Bazan, Alessandro Goedtel, Oscar Duque-Perez, Daniel Morinigo-Sotelo. Multi-Fault Diagnosis in Three-Phase Induction Motors Using Data Optimization and Machine Learning Techniques. Electronics. 2021; 10 (12):1462.
Chicago/Turabian StyleGustavo Bazan; Alessandro Goedtel; Oscar Duque-Perez; Daniel Morinigo-Sotelo. 2021. "Multi-Fault Diagnosis in Three-Phase Induction Motors Using Data Optimization and Machine Learning Techniques." Electronics 10, no. 12: 1462.
The study of power quality (PQ) has gained relevance over the years due to the increase in non-linear loads connected to the grid. Therefore, it is important to study the propagation of power quality disturbances (PQDs) to determine the propagation points in the grid, and their source of generation. Some papers in the state of the art perform the analysis of punctual measurements of a limited number of PQDs, some of them using high-cost commercial equipment. The proposed method is based upon a developed proprietary system, composed of a data logger FPGA with GPS, that allows the performance of synchronized measurements merged with the full parameterized PQD model, allowing the detection and tracking of disturbances propagating through the grid using wavelet transform (WT), fast Fourier transform (FFT), Hilbert–Huang transform (HHT), genetic algorithms (GAs), and particle swarm optimization (PSO). Measurements have been performed in an industrial installation, detecting the propagation of three PQDs: impulsive transients propagated at two locations in the grid, voltage fluctuation, and harmonic content propagated to all the locations. The results obtained show that the low-cost system and the developed methodology allow the detection of several PQDs, and track their propagation within a grid with 100% accuracy.
Oscar Pardo-Zamora; Rene Romero-Troncoso; Jesus Millan-Almaraz; Daniel Morinigo-Sotelo; Roque Osornio-Rios; Jose Antonino-Daviu. Power Quality Disturbance Tracking Based on a Proprietary FPGA Sensor with GPS Synchronization. Sensors 2021, 21, 3910 .
AMA StyleOscar Pardo-Zamora, Rene Romero-Troncoso, Jesus Millan-Almaraz, Daniel Morinigo-Sotelo, Roque Osornio-Rios, Jose Antonino-Daviu. Power Quality Disturbance Tracking Based on a Proprietary FPGA Sensor with GPS Synchronization. Sensors. 2021; 21 (11):3910.
Chicago/Turabian StyleOscar Pardo-Zamora; Rene Romero-Troncoso; Jesus Millan-Almaraz; Daniel Morinigo-Sotelo; Roque Osornio-Rios; Jose Antonino-Daviu. 2021. "Power Quality Disturbance Tracking Based on a Proprietary FPGA Sensor with GPS Synchronization." Sensors 21, no. 11: 3910.
A proper diagnosis of the state of an induction motor is of great interest to industry given the great importance of the extended use of this motor. Presently, the use of this motor driven by a frequency converter is very widespread. However, operation by means of an inverter introduces certain difficulties for a correct diagnosis, which results in a signal with higher harmonic content and noise level, which makes it difficult to perform a correct diagnosis. To solve these problems, this article proposes the use of a time-frequency technique known as Dragon Transform together with the functional ANOVA statistical technique to carry out a proper diagnosis of the state of the motor by working directly with the curves obtained from the application of the transform. A case study is presented showing the good results obtained by applying the methodology in which the state of the rotor bars of an inverter-fed motor is diagnosed considering three failure states and operating at different load levels.
Vanesa Fernandez-Cavero; Luis García-Escudero; Joan Pons-Llinares; Miguel Fernández-Temprano; Oscar Duque-Perez; Daniel Morinigo-Sotelo. Diagnosis of Broken Rotor Bars during the Startup of Inverter-Fed Induction Motors Using the Dragon Transform and Functional ANOVA. Applied Sciences 2021, 11, 3769 .
AMA StyleVanesa Fernandez-Cavero, Luis García-Escudero, Joan Pons-Llinares, Miguel Fernández-Temprano, Oscar Duque-Perez, Daniel Morinigo-Sotelo. Diagnosis of Broken Rotor Bars during the Startup of Inverter-Fed Induction Motors Using the Dragon Transform and Functional ANOVA. Applied Sciences. 2021; 11 (9):3769.
Chicago/Turabian StyleVanesa Fernandez-Cavero; Luis García-Escudero; Joan Pons-Llinares; Miguel Fernández-Temprano; Oscar Duque-Perez; Daniel Morinigo-Sotelo. 2021. "Diagnosis of Broken Rotor Bars during the Startup of Inverter-Fed Induction Motors Using the Dragon Transform and Functional ANOVA." Applied Sciences 11, no. 9: 3769.
Fault detection in induction motors powered by inverters operating in non-stationary regimes remains a challenge. The trajectory in the time-frequency plane of harmonics related to broken rotor bar develops very in proximity to the path described by the fundamental component. In addition, their energy is much lower than the amplitude of the first harmonic. These two characteristics make it challenging to observe them. The Dragon Transform (DT), here presented, is developed to overcome the described problem. In this paper, the DT is assessed with non-linear inverter-fed startups, where its high time and frequency resolutions facilitate the monitoring of fault harmonics even with highly adjacent trajectories to the first harmonic path.
Vanessa Fernandez-Cavero; Joan Pons-Llinares; Oscar Duque-Perez; Daniel Morinigo-Sotelo. Detection of Broken Rotor Bars in Nonlinear Startups of Inverter-Fed Induction Motors. IEEE Transactions on Industry Applications 2021, 57, 2559 -2568.
AMA StyleVanessa Fernandez-Cavero, Joan Pons-Llinares, Oscar Duque-Perez, Daniel Morinigo-Sotelo. Detection of Broken Rotor Bars in Nonlinear Startups of Inverter-Fed Induction Motors. IEEE Transactions on Industry Applications. 2021; 57 (3):2559-2568.
Chicago/Turabian StyleVanessa Fernandez-Cavero; Joan Pons-Llinares; Oscar Duque-Perez; Daniel Morinigo-Sotelo. 2021. "Detection of Broken Rotor Bars in Nonlinear Startups of Inverter-Fed Induction Motors." IEEE Transactions on Industry Applications 57, no. 3: 2559-2568.
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.
Three-phase induction motors are extensively used in industrial processes due to their robustness, adaptability to different operating conditions, and low operation and maintenance costs. Induction motor fault diagnosis has received special attention from industry since it can reduce process losses and ensure the reliable operation of industrial systems. Therefore, this paper presents a study on the use of meta-heuristic tools in the diagnosis of bearing failures in induction motors. The extraction of the fault characteristics is performed based on mutual information measurements between the stator current signals in the time domain. Then, the Artificial Bee Colony algorithm is used to select the relevant mutual information values and optimize the pattern classifier input data. To evaluate the classification accuracy under various levels of failure severity, the performance of two different pattern classifiers was compared: The C4.5 decision tree and the multi-layer artificial perceptron neural networks. The experimental results confirm the effectiveness of the proposed approach.
Gustavo Henrique Bazan; Alessandro Goedtel; Marcelo Favoretto Castoldi; Wagner Fontes Godoy; Oscar Duque-Perez; Daniel Morinigo-Sotelo. Mutual Information and Meta-Heuristic Classifiers Applied to Bearing Fault Diagnosis in Three-Phase Induction Motors. Applied Sciences 2020, 11, 314 .
AMA StyleGustavo Henrique Bazan, Alessandro Goedtel, Marcelo Favoretto Castoldi, Wagner Fontes Godoy, Oscar Duque-Perez, Daniel Morinigo-Sotelo. Mutual Information and Meta-Heuristic Classifiers Applied to Bearing Fault Diagnosis in Three-Phase Induction Motors. Applied Sciences. 2020; 11 (1):314.
Chicago/Turabian StyleGustavo Henrique Bazan; Alessandro Goedtel; Marcelo Favoretto Castoldi; Wagner Fontes Godoy; Oscar Duque-Perez; Daniel Morinigo-Sotelo. 2020. "Mutual Information and Meta-Heuristic Classifiers Applied to Bearing Fault Diagnosis in Three-Phase Induction Motors." Applied Sciences 11, no. 1: 314.
Power quality disturbances (PQD) are generated by non-linear loads and they propagate through the electrical grid to other sensitive devices causing malfunction. To study the propagation of PQD, it is important to perform synchronized and simultaneous measurements at multiple locations in the electrical network. This paper proposes a methodology to synchronize measurements of electrical variables at multiple locations in the electrical grid based on the synchronization of the internal time reference of several data loggers with the pulse per second (PPS) reference provided by a global positioning system (GPS) receiver module. As the PPS signal is synchronized to the atomic clocks of the satellites in the GPS, a precise time reference in multiple sites can be obtained simultaneously. The proposed methodology has been tested in two stages. The first stage is a validation experiment that compares the number of samples obtained between three GPS synchronized data loggers and two data loggers with internal synchronization only. The PPS signal has been interrupted in one GPS synchronized data logger to simulate a signal loss and test the resynchronization capability of the methodology. A very low synchronization error has been obtained for the GPS synchronized data loggers whereas the data loggers with the internal synchronization show greater synchronization errors. The second stage consists in experimental case of study that measuring electrical variables in multiple locations of a real electrical grid where the propagation of multiple disturbances are tracked with the GPS synchronized data loggers.
Oscar N. Pardo-Zamora; Rene De J. Romero-Troncoso; Jesus R. Millan-Almaraz; Daniel Morinigo-Sotelo; Roque A. Osornio-Rios. Methodology for Power Quality Measurement Synchronization Based on GPS Pulse-Per-Second Algorithm. IEEE Transactions on Instrumentation and Measurement 2020, 70, 1 -9.
AMA StyleOscar N. Pardo-Zamora, Rene De J. Romero-Troncoso, Jesus R. Millan-Almaraz, Daniel Morinigo-Sotelo, Roque A. Osornio-Rios. Methodology for Power Quality Measurement Synchronization Based on GPS Pulse-Per-Second Algorithm. IEEE Transactions on Instrumentation and Measurement. 2020; 70 (99):1-9.
Chicago/Turabian StyleOscar N. Pardo-Zamora; Rene De J. Romero-Troncoso; Jesus R. Millan-Almaraz; Daniel Morinigo-Sotelo; Roque A. Osornio-Rios. 2020. "Methodology for Power Quality Measurement Synchronization Based on GPS Pulse-Per-Second Algorithm." IEEE Transactions on Instrumentation and Measurement 70, no. 99: 1-9.
The problem of detecting and quantifying bar breakage harmonics in inverter-fed induction motors has not been solved by the time–frequency transforms present in the technical literature. The paper proposes a new transform, called dragon transform, to solve this problem. The dragon atoms are defined with shapes perfectly adapted to the harmonic trajectories in the time–frequency plane, no matter how complex they are, enabling the precise tracing of the harmonics to be detected. A quantification method is also proposed, which obtains for the first time in the technical literature, the time evolutions of the harmonic amplitudes during a complex transient such as the start-up and the steady state of an inverter-fed motor. The transform performance is validated testing the induction motor under different load levels.
V. Fernandez-Cavero; J. Pons-Llinares; O. Duque-Perez; D. Morinigo-Sotelo. Detection and quantification of bar breakage harmonics evolutions in inverter-fed motors through the dragon transform. ISA Transactions 2020, 109, 352 -367.
AMA StyleV. Fernandez-Cavero, J. Pons-Llinares, O. Duque-Perez, D. Morinigo-Sotelo. Detection and quantification of bar breakage harmonics evolutions in inverter-fed motors through the dragon transform. ISA Transactions. 2020; 109 ():352-367.
Chicago/Turabian StyleV. Fernandez-Cavero; J. Pons-Llinares; O. Duque-Perez; D. Morinigo-Sotelo. 2020. "Detection and quantification of bar breakage harmonics evolutions in inverter-fed motors through the dragon transform." ISA Transactions 109, no. : 352-367.
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.
This paper addresses a comprehensive evaluation of a bearing fault evolution and its consequent prediction concerning the remaining useful life. The proper prediction of bearing faults in their early stage is a crucial factor for predictive maintenance and mainly for the production management schedule. The detection and estimation of the progressive evolution of a bearing fault are performed by monitoring the amplitude of the current signals at the time domain. Data gathered from line-fed and inverter-fed three-phase induction motors were used to validate the proposed approach. To assess classification accuracy and fault estimation, the models described in this paper are investigated by using Artificial Neural Networks models. The paper also provides process flowcharts and classification tables to present the prognostic models used to estimate the remaining useful life of a defective bearing. Experimental results confirmed the method robustness and provide an accurate diagnosis regardless of the bearing fault stage, motor speed, load level, and type of supply.
Wagner Fontes Godoy; Daniel Morinigo-Sotelo; Oscar Duque-Perez; Ivan Nunes Da Silva; Alessandro Goedtel; Rodrigo Henrique Cunha Palácios. Estimation of Bearing Fault Severity in Line-Connected and Inverter-Fed Three-Phase Induction Motors. Energies 2020, 13, 3481 .
AMA StyleWagner Fontes Godoy, Daniel Morinigo-Sotelo, Oscar Duque-Perez, Ivan Nunes Da Silva, Alessandro Goedtel, Rodrigo Henrique Cunha Palácios. Estimation of Bearing Fault Severity in Line-Connected and Inverter-Fed Three-Phase Induction Motors. Energies. 2020; 13 (13):3481.
Chicago/Turabian StyleWagner Fontes Godoy; Daniel Morinigo-Sotelo; Oscar Duque-Perez; Ivan Nunes Da Silva; Alessandro Goedtel; Rodrigo Henrique Cunha Palácios. 2020. "Estimation of Bearing Fault Severity in Line-Connected and Inverter-Fed Three-Phase Induction Motors." Energies 13, no. 13: 3481.
Broken rotor bar (BRB) is one of the most common failures in induction motors (IMs) these days; however, its identification is complicated since the frequencies associated with the fault condition appear near the fundamental frequency component (FFC). This situation gets worse when the IM slip or the operation frequency is low. In these circumstances, the common techniques for condition monitoring may experience troubles in the identification of a faulty condition. By suppressing the FFC, the fault detection is enhanced, allowing the identification of BRB even at low slip conditions. The main contribution of this work consists of the development of a preprocessing technique that estimates the FFC from an optimization point of view. This way, it is possible to remove a single frequency component instead of removing a complete frequency band from the current signals of an IM. Experimentation is performed on an IM operating at two different frequencies and at three different load levels. The proposed methodology is compared with two different approaches and the results show that the use of the proposed methodology allows to enhance the performance delivered by the common methodologies for the detection of BRB in steady state.
Daivd A. Elvira-Ortiz; Daniel Morinigo-Sotelo; Angel L. Zorita-Lamadrid; Roque A. Osornio-Rios; Rene De J. Romero-Troncoso. Fundamental Frequency Suppression for the Detection of Broken Bar in Induction Motors at Low Slip and Frequency. Applied Sciences 2020, 10, 4160 .
AMA StyleDaivd A. Elvira-Ortiz, Daniel Morinigo-Sotelo, Angel L. Zorita-Lamadrid, Roque A. Osornio-Rios, Rene De J. Romero-Troncoso. Fundamental Frequency Suppression for the Detection of Broken Bar in Induction Motors at Low Slip and Frequency. Applied Sciences. 2020; 10 (12):4160.
Chicago/Turabian StyleDaivd A. Elvira-Ortiz; Daniel Morinigo-Sotelo; Angel L. Zorita-Lamadrid; Roque A. Osornio-Rios; Rene De J. Romero-Troncoso. 2020. "Fundamental Frequency Suppression for the Detection of Broken Bar in Induction Motors at Low Slip and Frequency." Applied Sciences 10, no. 12: 4160.
Renewable generation sources like photovoltaic plants are weather dependent and it is hard to predict their behavior. This work proposes a methodology for obtaining a parameterized model that estimates the generated power in a photovoltaic generation system. The proposed methodology uses a genetic algorithm to obtain the mathematical model that best fits the behavior of the generated power through the day. Additionally, using the same methodology, a mathematical model is developed for harmonic distortion estimation that allows one to predict the produced power and its quality. Experimentation is performed using real signals from a photovoltaic system. Eight days from different seasons of the year are selected considering different irradiance conditions to assess the performance of the methodology under different environmental and electrical conditions. The proposed methodology is compared with an artificial neural network, with the results showing an improved performance when using the genetic algorithm methodology.
David A. Elvira-Ortiz; Arturo Y. Jaen-Cuellar; Daniel Morinigo-Sotelo; Luis Morales-Velazquez; Roque A. Osornio-Rios; Rene De J. Romero-Troncoso. Genetic Algorithm Methodology for the Estimation of Generated Power and Harmonic Content in Photovoltaic Generation. Applied Sciences 2020, 10, 542 .
AMA StyleDavid A. Elvira-Ortiz, Arturo Y. Jaen-Cuellar, Daniel Morinigo-Sotelo, Luis Morales-Velazquez, Roque A. Osornio-Rios, Rene De J. Romero-Troncoso. Genetic Algorithm Methodology for the Estimation of Generated Power and Harmonic Content in Photovoltaic Generation. Applied Sciences. 2020; 10 (2):542.
Chicago/Turabian StyleDavid A. Elvira-Ortiz; Arturo Y. Jaen-Cuellar; Daniel Morinigo-Sotelo; Luis Morales-Velazquez; Roque A. Osornio-Rios; Rene De J. Romero-Troncoso. 2020. "Genetic Algorithm Methodology for the Estimation of Generated Power and Harmonic Content in Photovoltaic Generation." Applied Sciences 10, no. 2: 542.
The increasing incorporation of power electronics and other non-linear loads, in addition to their energy advantages, also implies a poor power quality, especially as regards harmonic pollution. Different solutions have been proposed to measure harmonic content, taking the International Electrotechnical Commission (IEC) standards as a reference. However, there are still some issues related to the measurement of the harmonic, and especially, interharmonic content. Some of those questions are addressed in this work, such as the problem derived from the instability of the values obtained by applying the discrete Fourier transform to each sampling window, or the appearance of local peaks when there are tones separated by multiples of the resolution. Solutions were proposed based on time aggregation and the overlapping of windows. The results demonstrate that aggregation time, window type, and overlapping can improve the accuracy in harmonic measurement using Fourier transform-based methods, as defined in the standards. The paper shows the need to consider spectral and time groupings together, improving results by using an appropriate percentage of overlap and an adaptation of the aggregation time to the harmonic content.
Angel Arranz-Gimon; Angel Zorita-Lamadrid; Daniel Morinigo-Sotelo; Oscar Duque-Perez. A Study of the Effects of Time Aggregation and Overlapping within the Framework of IEC Standards for the Measurement of Harmonics and Interharmonics. Applied Sciences 2019, 9, 4549 .
AMA StyleAngel Arranz-Gimon, Angel Zorita-Lamadrid, Daniel Morinigo-Sotelo, Oscar Duque-Perez. A Study of the Effects of Time Aggregation and Overlapping within the Framework of IEC Standards for the Measurement of Harmonics and Interharmonics. Applied Sciences. 2019; 9 (21):4549.
Chicago/Turabian StyleAngel Arranz-Gimon; Angel Zorita-Lamadrid; Daniel Morinigo-Sotelo; Oscar Duque-Perez. 2019. "A Study of the Effects of Time Aggregation and Overlapping within the Framework of IEC Standards for the Measurement of Harmonics and Interharmonics." Applied Sciences 9, no. 21: 4549.
Condition monitoring of bearings is an open issue. The use of the stator current to monitor induction motors has been validated as a very advantageous and practical way to detect several types of faults. Nevertheless, for bearing faults, the use of vibrations or sound generally offers better results in the accuracy of the detection, although with some disadvantages related to the sensors used for monitoring. To improve the performance of bearing monitoring, it is proposed to take advantage of more information available in the current spectra, beyond the usually employed, incorporating the amplitude of a significant number of sidebands around the first eleven harmonics, growing exponentially the number of fault signatures. This is especially interesting for inverter-fed motors. But, in turn, this leads to the problem of overfitting when applying a classifier to perform the fault diagnosis. To overcome this problem, and still exploit all the useful information available in the spectra, it is proposed to use shrinkage methods, which have been lately proposed in machine learning to solve the overfitting issue when the problem has many more variables than examples to classify. A case study with a motor is shown to prove the validity of the proposal.
Oscar Duque-Perez; Carlos Del Pozo-Gallego; Daniel Morinigo-Sotelo; Wagner Fontes Godoy. Condition Monitoring of Bearing Faults Using the Stator Current and Shrinkage Methods. Energies 2019, 12, 3392 .
AMA StyleOscar Duque-Perez, Carlos Del Pozo-Gallego, Daniel Morinigo-Sotelo, Wagner Fontes Godoy. Condition Monitoring of Bearing Faults Using the Stator Current and Shrinkage Methods. Energies. 2019; 12 (17):3392.
Chicago/Turabian StyleOscar Duque-Perez; Carlos Del Pozo-Gallego; Daniel Morinigo-Sotelo; Wagner Fontes Godoy. 2019. "Condition Monitoring of Bearing Faults Using the Stator Current and Shrinkage Methods." Energies 12, no. 17: 3392.
Energy from the fundamental frequency component (FFC) in electrical signals is usually much higher than the energy from the rest of spectral components. This situation makes it difficult to obtain a proper analysis of the whole frequencies involved in a particular signal. To improve the spectral analysis of electrical signals, some works have proposed the use of filters and digital signal processing techniques for suppressing the influence of the FFC. However, these methodologies suppress a frequency band, introducing undesired effects in the frequencies close to the FFC. Thus, this paper proposes the use of non-linear least squares to identify the amplitude, frequency and phase that describe the FFC to suppress only one frequency component instead of a frequency band. The methodology is tested using synthetic signals and experimentation is performed with real electric signals from three different scenarios: current signals from two induction motors operating at two different frequencies (60 Hz and 31 Hz), and a voltage signal from a photovoltaic generation plant. Results show that the methodology can adequately recognize and subtract the FFC. This methodology aims to be a tool to enhance the results delivered by methodologies for condition monitoring of induction motors and power quality assessment.
David Alejandro Elvira-Ortiz; Daniel Morinigo-Sotelo; Luis Morales-Velazquez; Roque A. Osornio-Rios; Rene J. Romero-Troncoso. Non-linear least squares methodology for suppressing the fundamental frequency in the analysis of electric signals. Electric Power Systems Research 2019, 175, 105924 .
AMA StyleDavid Alejandro Elvira-Ortiz, Daniel Morinigo-Sotelo, Luis Morales-Velazquez, Roque A. Osornio-Rios, Rene J. Romero-Troncoso. Non-linear least squares methodology for suppressing the fundamental frequency in the analysis of electric signals. Electric Power Systems Research. 2019; 175 ():105924.
Chicago/Turabian StyleDavid Alejandro Elvira-Ortiz; Daniel Morinigo-Sotelo; Luis Morales-Velazquez; Roque A. Osornio-Rios; Rene J. Romero-Troncoso. 2019. "Non-linear least squares methodology for suppressing the fundamental frequency in the analysis of electric signals." Electric Power Systems Research 175, no. : 105924.
David A. Elvira-Ortiz; Daniel Morinigo-Sotelo; Oscar Duque-Perez; Roque A. Osornio-Rios; Rene J. Romero-Troncoso. Study of the harmonic and interharmonic content in electrical signals from photovoltaic generation and their relationship with environmental factors. Journal of Renewable and Sustainable Energy 2019, 11, 043502 .
AMA StyleDavid A. Elvira-Ortiz, Daniel Morinigo-Sotelo, Oscar Duque-Perez, Roque A. Osornio-Rios, Rene J. Romero-Troncoso. Study of the harmonic and interharmonic content in electrical signals from photovoltaic generation and their relationship with environmental factors. Journal of Renewable and Sustainable Energy. 2019; 11 (4):043502.
Chicago/Turabian StyleDavid A. Elvira-Ortiz; Daniel Morinigo-Sotelo; Oscar Duque-Perez; Roque A. Osornio-Rios; Rene J. Romero-Troncoso. 2019. "Study of the harmonic and interharmonic content in electrical signals from photovoltaic generation and their relationship with environmental factors." Journal of Renewable and Sustainable Energy 11, no. 4: 043502.
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.
N. A. Ojeda-Aguirre; A. Garcia-Perez; R. J. Romero-Troncoso; D. Morinigo-Sotelo; O. Duque-Perez; D. Camarena-Martinez. Reassigned Short Time Fourier Transform and K-means Method for Diagnosis of Broken Rotor Bar Detection in VSD-fed Induction Motors. Advances in Electrical and Computer Engineering 2019, 19, 61 -68.
AMA StyleN. A. Ojeda-Aguirre, A. Garcia-Perez, R. J. Romero-Troncoso, D. Morinigo-Sotelo, O. Duque-Perez, D. Camarena-Martinez. Reassigned Short Time Fourier Transform and K-means Method for Diagnosis of Broken Rotor Bar Detection in VSD-fed Induction Motors. Advances in Electrical and Computer Engineering. 2019; 19 (2):61-68.
Chicago/Turabian StyleN. A. Ojeda-Aguirre; A. Garcia-Perez; R. J. Romero-Troncoso; D. Morinigo-Sotelo; O. Duque-Perez; D. Camarena-Martinez. 2019. "Reassigned Short Time Fourier Transform and K-means Method for Diagnosis of Broken Rotor Bar Detection in VSD-fed Induction Motors." Advances in Electrical and Computer Engineering 19, no. 2: 61-68.
A large number of wind turbines from different manufacturers have been installed worldwide. They have different operating principles and are designed according to the climatic conditions of the installation site. The objective of this study is to perform a comparative analysis of the behavior of the different types of internal and external failures, their frequency and their duration for different types of wind turbines. To accomplish this, data collected directly from the Supervisory Control and Data Acquisition (SCADA) system is used to monitor two wind farms installed in Soria (Spain). The study reveals the typical faults of each type of wind turbine. Synchronous wind turbines and wind turbines with variable-pitch blades experience more types of faults; however, the frequency and duration of faults are greater in wind turbines with fixed-pitch blades and asynchronous generators. Keywords: Wind turbine, stall control, pitch control, synchronous and asynchronous generators, faults, lost time.
Yuri Merizalde; Luis Hernández Callejo; Javier Gracia Bernal; Oscar Duque Perez; Luis-Miguel Bonilla Morte; Angel Luis Zorita Lamadrid; Daniel Morinigo Sotelo. COMPARATIVE ANALYSIS OF FAULTS FROM STALL CONTROLLED WIND TURBINES WITH ASYNCHRONOUS GENERATORS AND PITCH CONTROLLED WIND TURBINES WITH SYNCHRONOUS GENERATORS. DYNA 2018, 93, 541 -548.
AMA StyleYuri Merizalde, Luis Hernández Callejo, Javier Gracia Bernal, Oscar Duque Perez, Luis-Miguel Bonilla Morte, Angel Luis Zorita Lamadrid, Daniel Morinigo Sotelo. COMPARATIVE ANALYSIS OF FAULTS FROM STALL CONTROLLED WIND TURBINES WITH ASYNCHRONOUS GENERATORS AND PITCH CONTROLLED WIND TURBINES WITH SYNCHRONOUS GENERATORS. DYNA. 2018; 93 (1):541-548.
Chicago/Turabian StyleYuri Merizalde; Luis Hernández Callejo; Javier Gracia Bernal; Oscar Duque Perez; Luis-Miguel Bonilla Morte; Angel Luis Zorita Lamadrid; Daniel Morinigo Sotelo. 2018. "COMPARATIVE ANALYSIS OF FAULTS FROM STALL CONTROLLED WIND TURBINES WITH ASYNCHRONOUS GENERATORS AND PITCH CONTROLLED WIND TURBINES WITH SYNCHRONOUS GENERATORS." DYNA 93, no. 1: 541-548.