Induction Machine Broken Bar and Stator Short-Circuit Fault Diagnostics Based on Three Phase Stator Current Envelopes

A new method for the fault diagnosis of a broken rotor bar and interturn short circuits in induction machines (IMs) is presented. The method is based on the analysis of the three-phase stator current envelopes of IMs using reconstructed phase space transforms. The signatures of each type of fault are created from the three-phase current envelope of each fault. The resulting fault signatures for the new so-called ldquounseen signalsrdquo are classified using Gaussian mixture models and a Bayesian maximum likelihood classifier. The presented method yields a high degree of accuracy in fault identification as evidenced by the given experimental results, which validate this method

Cost-Effective Reduced Envelope of the Stator Current via Synchronous Sampling for the Diagnosis of Rotor Asymmetries in Induction Machines Working at Very Low Slip

[EN] Fault diagnosis of rotor asymmetries of induction machines (IMs) using the stator current relies on the detection of the characteristic signatures of the fault harmonics in the current spectrum. In some scenarios, such as large induction machines running at a very low slip, or unloaded machines tested offline, this technique may fail. In these scenarios, the fault harmonics are very close to the frequency of the fundamental component, and have a low amplitude, so that they may remain undetected, buried under the fundamental's leakage, until the damage is severe. To avoid false positives, a proven approach is to search for the fault harmonics in the current envelope, instead of the current itself, because in this case the spectrum is free from the leakage of the fundamental. Besides, the fault harmonics appear at a very low frequency. Nevertheless, building the current spectrum is costly in terms of computing complexity, as in the case of the Hilbert transform, or hardware resources, as in the need for simultaneously sampling three stator currents in the case of the extended current Park's vector approach (EPVA). In this work, a novel method is proposed to avoid this problem. It is based on sampling a phase current just twice per current cycle, with a fixed delay with respect to its zero crossings. It is shown that the spectrum of this reduced set of current samples contains the same fault harmonics as the spectrum of the full-length current envelope, despite using a minimal amount of computing resources. The proposed approach is cost-effective, because the computational requirements for building the current envelope are reduced to less than 1% of those required by other conventional methods, in terms of storage and computing time. In this way, it can be implemented with low-cost embedded devices for on-line fault diagnosis. The proposed approach is introduced theoretically and validated experimentally, using a commercial induction motor with a broken bar under different load and supply conditions. Besides, the proposed approach has been implemented on a low-cost embedded device, which can be accessed on-line for remote fault diagnosis.This research was funded by the Spanish "Ministerio de Ciencia, Innovacion y Universidades (MCIU)", the "Agencia Estatal de Investigacion (AEI)" and the "Fondo Europeo de Desarrollo Regional (FEDER)" in the framework of the "Proyectos I+D+i - Retos Investigacion 2018", project reference RTI2018-102175-B-I00 (MCIU/AEI/FEDER, UE).Burriel-Valencia, J.; Puche-Panadero, R.; Martinez-Roman, J.; Sapena-Bano, A.; Pineda-Sanchez, M. (2019). Cost-Effective Reduced Envelope of the Stator Current via Synchronous Sampling for the Diagnosis of Rotor Asymmetries in Induction Machines Working at Very Low Slip. Sensors. 19(16)(3471):1-16. https://doi.org/10.3390/s19163471S11619(16)3471Chang, H.-C., Jheng, Y.-M., Kuo, C.-C., & Hsueh, Y.-M. (2019). Induction Motors Condition Monitoring System with Fault Diagnosis Using a Hybrid Approach. Energies, 12(8), 1471. doi:10.3390/en12081471Artigao, E., Koukoura, S., Honrubia-Escribano, A., Carroll, J., McDonald, A., & Gómez-Lázaro, E. (2018). Current Signature and Vibration Analyses to Diagnose an In-Service Wind Turbine Drive Train. Energies, 11(4), 960. doi:10.3390/en11040960Climente-Alarcon, V., Antonino-Daviu, J. A., Strangas, E. G., & Riera-Guasp, M. (2015). Rotor-Bar Breakage Mechanism and Prognosis in an Induction Motor. IEEE Transactions on Industrial Electronics, 62(3), 1814-1825. doi:10.1109/tie.2014.2336604Culbert, I., & Letal, J. (2017). Signature Analysis for Online Motor Diagnostics: Early Detection of Rotating Machine Problems Prior to Failure. IEEE Industry Applications Magazine, 23(4), 76-81. doi:10.1109/mias.2016.2600684Pandarakone, S. E., Mizuno, Y., & Nakamura, H. (2017). Distinct Fault Analysis of Induction Motor Bearing Using Frequency Spectrum Determination and Support Vector Machine. IEEE Transactions on Industry Applications, 53(3), 3049-3056. doi:10.1109/tia.2016.2639453Kang, T.-J., Yang, C., Park, Y., Hyun, D., Lee, S. B., & Teska, M. (2018). Electrical Monitoring of Mechanical Defects in Induction Motor-Driven V-Belt–Pulley Speed Reduction Couplings. IEEE Transactions on Industry Applications, 54(3), 2255-2264. doi:10.1109/tia.2018.2805840Puche-Panadero, R., Pineda-Sanchez, M., Riera-Guasp, M., Roger-Folch, J., Hurtado-Perez, E., & Perez-Cruz, J. (2009). Improved Resolution of the MCSA Method Via Hilbert Transform, Enabling the Diagnosis of Rotor Asymmetries at Very Low Slip. IEEE Transactions on Energy Conversion, 24(1), 52-59. doi:10.1109/tec.2008.2003207Mirzaeva, G., & Saad, K. I. (2018). Advanced Diagnosis of Stator Turn-to-Turn Faults and Static Eccentricity in Induction Motors Based on Internal Flux Measurement. IEEE Transactions on Industry Applications, 54(4), 3961-3970. doi:10.1109/tia.2018.2821098Mirzaeva, G., & Saad, K. I. (2018). Advanced Diagnosis of Rotor Faults and Eccentricity in Induction Motors Based on Internal Flux Measurement. IEEE Transactions on Industry Applications, 54(3), 2981-2991. doi:10.1109/tia.2018.2805730Jian, X., Li, W., Guo, X., & Wang, R. (2019). Fault Diagnosis of Motor Bearings Based on a One-Dimensional Fusion Neural Network. Sensors, 19(1), 122. doi:10.3390/s19010122Yan, X., Sun, Z., Zhao, J., Shi, Z., & Zhang, C.-A. (2019). Fault Diagnosis of Active Magnetic Bearing–Rotor System via Vibration Images. Sensors, 19(2), 244. doi:10.3390/s19020244Martinez, J., Belahcen, A., & Muetze, A. (2017). Analysis of the Vibration Magnitude of an Induction Motor With Different Numbers of Broken Bars. IEEE Transactions on Industry Applications, 53(3), 2711-2720. doi:10.1109/tia.2017.2657478Delgado-Arredondo, P. A., Morinigo-Sotelo, D., Osornio-Rios, R. A., Avina-Cervantes, J. G., Rostro-Gonzalez, H., & Romero-Troncoso, R. de J. (2017). Methodology for fault detection in induction motors via sound and vibration signals. Mechanical Systems and Signal Processing, 83, 568-589. doi:10.1016/j.ymssp.2016.06.032Ghanbari, T. (2016). Autocorrelation function-based technique for stator turn-fault detection of induction motor. IET Science, Measurement & Technology, 10(2), 100-110. doi:10.1049/iet-smt.2015.0118Abd-el -Malek, M., Abdelsalam, A. K., & Hassan, O. E. (2017). Induction motor broken rotor bar fault location detection through envelope analysis of start-up current using Hilbert transform. Mechanical Systems and Signal Processing, 93, 332-350. doi:10.1016/j.ymssp.2017.02.014Leite, V. C. M. N., Borges da Silva, J. G., Veloso, G. F. C., Borges da Silva, L. E., Lambert-Torres, G., Bonaldi, E. L., & de Lacerda de Oliveira, L. E. (2015). Detection of Localized Bearing Faults in Induction Machines by Spectral Kurtosis and Envelope Analysis of Stator Current. IEEE Transactions on Industrial Electronics, 62(3), 1855-1865. doi:10.1109/tie.2014.2345330Aydin, I., Karakose, M., & Akin, E. (2011). A new method for early fault detection and diagnosis of broken rotor bars. Energy Conversion and Management, 52(4), 1790-1799. doi:10.1016/j.enconman.2010.11.018Duan, J., Shi, T., Zhou, H., Xuan, J., & Zhang, Y. (2018). Multiband Envelope Spectra Extraction for Fault Diagnosis of Rolling Element Bearings. Sensors, 18(5), 1466. doi:10.3390/s18051466Wang, J., Liu, S., Gao, R. X., & Yan, R. (2012). Current envelope analysis for defect identification and diagnosis in induction motors. Journal of Manufacturing Systems, 31(4), 380-387. doi:10.1016/j.jmsy.2012.06.005Sapena-Bano, A., Pineda-Sanchez, M., Puche-Panadero, R., Martinez-Roman, J., & Kanovic, Z. (2015). Low-Cost Diagnosis of Rotor Asymmetries in Induction Machines Working at a Very Low Slip Using the Reduced Envelope of the Stator Current. IEEE Transactions on Energy Conversion, 30(4), 1409-1419. doi:10.1109/tec.2015.2445216Wu, T. Y., Lai, C. H., & Liu, D. C. (2016). Defect diagnostics of roller bearing using instantaneous frequency normalization under fluctuant rotating speed. Journal of Mechanical Science and Technology, 30(3), 1037-1048. doi:10.1007/s12206-016-0206-6M. A. Cruz, A. J. Marques Cardoso, S. (2000). Rotor Cage Fault Diagnosis in Three-Phase Induction Motors by Extended Park’s Vector Approach. Electric Machines & Power Systems, 28(4), 289-299. doi:10.1080/073135600268261Henao, H., Capolino, G.-A., Fernandez-Cabanas, M., Filippetti, F., Bruzzese, C., Strangas, E., … Hedayati-Kia, S. (2014). Trends in Fault Diagnosis for Electrical Machines: A Review of Diagnostic Techniques. IEEE Industrial Electronics Magazine, 8(2), 31-42. doi:10.1109/mie.2013.2287651Cruz, S. M. A., & Cardoso, A. J. M. (2001). Stator winding fault diagnosis in three-phase synchronous and asynchronous motors, by the extended Park’s vector approach. IEEE Transactions on Industry Applications, 37(5), 1227-1233. doi:10.1109/28.952496Tsoumas, I. P., Georgoulas, G., Mitronikas, E. D., & Safacas, A. N. (2008). Asynchronous Machine Rotor Fault Diagnosis Technique Using Complex Wavelets. IEEE Transactions on Energy Conversion, 23(2), 444-459. doi:10.1109/tec.2007.895872Corne, B., Vervisch, B., Derammelaere, S., Knockaert, J., & Desmet, J. (2018). The reflection of evolving bearing faults in the stator current’s extended park vector approach for induction machines. Mechanical Systems and Signal Processing, 107, 168-182. doi:10.1016/j.ymssp.2017.12.010Georgakopoulos, I. P., Mitronikas, E. D., & Safacas, A. N. (2011). Detection of Induction Motor Faults in Inverter Drives Using Inverter Input Current Analysis. IEEE Transactions on Industrial Electronics, 58(9), 4365-4373. doi:10.1109/tie.2010.2093476Choi, S., Akin, B., Rahimian, M. M., & Toliyat, H. A. (2011). Implementation of a Fault-Diagnosis Algorithm for Induction Machines Based on Advanced Digital-Signal-Processing Techniques. IEEE Transactions on Industrial Electronics, 58(3), 937-948. doi:10.1109/tie.2010.2048837White, D., William, P., Hoffman, M., & Balkir, S. (2013). Low-Power Analog Processing for Sensing Applications: Low-Frequency Harmonic Signal Classification. Sensors, 13(8), 9604-9623. doi:10.3390/s130809604Wu, F., & Zhao, J. (2016). A Real-Time Multiple Open-Circuit Fault Diagnosis Method in Voltage-Source-Inverter Fed Vector Controlled Drives. IEEE Transactions on Power Electronics, 31(2), 1425-1437. doi:10.1109/tpel.2015.2422131Estima, J. O., & Marques Cardoso, A. J. (2013). A New Algorithm for Real-Time Multiple Open-Circuit Fault Diagnosis in Voltage-Fed PWM Motor Drives by the Reference Current Errors. IEEE Transactions on Industrial Electronics, 60(8), 3496-3505. doi:10.1109/tie.2012.2188877Naha, A., Samanta, A. K., Routray, A., & Deb, A. K. (2017). Low Complexity Motor Current Signature Analysis Using Sub-Nyquist Strategy With Reduced Data Length. IEEE Transactions on Instrumentation and Measurement, 66(12), 3249-3259. doi:10.1109/tim.2017.2737879Moussa, M. A., Boucherma, M., & Khezzar, A. (2017). A Detection Method for Induction Motor Bar Fault Using Sidelobes Leakage Phenomenon of the Sliding Discrete Fourier Transform. IEEE Transactions on Power Electronics, 32(7), 5560-5572. doi:10.1109/tpel.2016.2605821Shahbazi, M., Saadate, S., Poure, P., & Zolghadri, M. (2016). Open-circuit switch fault tolerant wind energy conversion system based on six/five-leg reconfigurable converter. Electric Power Systems Research, 137, 104-112. doi:10.1016/j.epsr.2016.04.004Kamel, T., Biletskiy, Y., & Chang, L. (2015). Fault Diagnoses for Industrial Grid-Connected Converters in the Power Distribution Systems. IEEE Transactions on Industrial Electronics, 62(10), 6496-6507. doi:10.1109/tie.2015.2420627Nguyen, H., Kim, J., & Kim, J.-M. (2018). Optimal Sub-Band Analysis Based on the Envelope Power Spectrum for Effective Fault Detection in Bearing under Variable, Low Speeds. Sensors, 18(5), 1389. doi:10.3390/s1805138

Rotor Fault Study of an Induction Motor using Fuzzy Controller

Quality control is applied in production process. The good condition of electrical machines can be obtained by using diagnostic. There are lots of methods that can be used for diagnostics of electrical machine. In the literature, popular methods are based on a study of electrical signals. This project work proposes a novel automated practical implementation for non contiguous rotor side broken bars detection and diagnosis in induction motors. In this work a method for detection and diagnosis of rotor side broken bars is proposed based on the spectral analysis via fast Fourier transform (FFT) and classification of the spectral response based on fuzzy controlled identifier. For the fault diagnosis objective, two features are pick out from the spectrum of the stator current, 1st is the amplitude of the harmonics representing the broken bars defect 2sf (where s is the slip and f is the fundamental harmonics) and the second is the dc value. By using these obtained parameters a fuzzy identifier is proposed to identify the number of broken bars. For the designing of the proposed fuzzy identifier these two parameters will be used as inputs where the decision about the state of rotor will be made. After the implementation of the proposed work it is expected that the proposed technique will able to efficiently detect the number of broken bars at rotor side. DOI: 10.17762/ijritcc2321-8169.150510

Closed-Loop Drive Detection and Diagnosis of Multiple Combined Faults in Induction Motor Through Model-Based and Neuro-Fuzzy Network Techniques

In this paper, a fault detection and diagnosis approach adopted for an input-output feedback linearization (IOFL) control of induction motor (IM) drive is proposed. This approach has been employed to detect and identify the simple and mixed broken rotor bars and static air-gap eccentricity faults right from the start its operation by utilizing advanced techniques. Therefore, two techniques are applied: the model-based strategy, which is an online method used to generate residual stator current signal in order to indicate the presence of possible failures by means of the sliding mode observer (SMO) in the closed-loop drive. However, this strategy is not able to recognise the fault types and it can be affected by the other disturbances. Therefore, the offline method using the multi-adaptive neuro-fuzzy inference system (MANAFIS) technique is proposed to identify the faults and distinguish them. However, the MANAFIS required a relevant database to achieve satisfactory results. Hence, the stator current analysis based on the HFFT combination of the Hilbert transform (HT) and Fast Fourier transform (FFT) is applied to extract the amplitude of harmonics due to defects occur and used them as an input data set for the MANFIS under different loads and fault severities. The simulation results show the efficiency of the proposed techniques and its ability to detect and diagnose any minor faults in a closed-loop drive of IM

Wavelet packet analysis for rotor bar breakage in an inverter induction motor

Introduction. In various industrial processes, squirrel cage induction motors are widely employed. These motors can be used in harsh situations, such as non-ventilated spaces, due to their high strength and longevity. These machines are subject to malfunctions such as short circuits and broken bars. Indeed, for the diagnosis several techniques are offered and used. Novelty of the proposed work provides the use of wavelet analysis technology in a continuous and discrete system to detect faults affecting the rotating part of an induction motor fed by a three-phase inverter. Purpose. This paper aims to present a novel technique for diagnosing broken rotor bars in the low-load, stationary induction machine proposed. The technique is used to address the problem of using the traditional Techniques like Fourier Transforms signal processing algorithm by analyzing the stator current envelope. The suggested method is based on the use of discrete wavelet transform and continuous wavelet transform. Methods. A waveform can be monitored at any frequency of interest using the suggested discrete wavelet transform and continuous wavelet transform. To identify the rotor broken bar fault, stator current frequency spectrum is analyzed and then examined. Based on a suitable index, the algorithm separates the healthy motor from the defective one, with 1, 2 and 3 broken bars at no-load. Results. In comparison to the healthy conditions, the recommended index significantly raises under the broken bars conditions. It can identify the problematic conditions with clarity. The possibility of detecting potential faults has been demonstrated (broken bars), using discrete wavelet transform and continuous wavelet transform. The diagnostic method is adaptable to temporary situations brought on by alterations in load and speed. Performance and efficacy of the suggested diagnostic method are demonstrated through simulation in Simulink® MATLAB environment.Вступ. У різних промислових процесах широко використовуються асинхронні двигуни із короткозамкненим ротором. Ці двигуни можуть використовуватися в суворих умовах, наприклад, в приміщеннях, що не вентилюються, завдяки їх високій міцності і довговічності. Ці машини схильні до несправностей, таких як коротке замикання і зламані стрижні. Зрозуміло, що для діагностики пропонується та використовується кілька методик. Новизна запропонованої роботи полягає у використанні технології вейвлет-аналізу в безперервній і дискретній системі для виявлення несправностей, що впливають на частину асинхронного двигуна, що обертається, що живиться від трифазного інвертора. Мета. У цій статті представлена нова методика діагностики зламаних стрижнів ротора в малонавантаженій стаціонарній асинхронній машині. Цей метод використовується для вирішення проблеми використання традиційних методів, таких як алгоритм обробки сигналів перетворення Фур’є, шляхом аналізу огинаючої струму статора. Пропонований метод заснований на використанні дискретного вейвлет-перетворення та безперервного вейвлет-перетворення. Методи. Форма сигналу може відстежуватися на будь-якій частоті, що цікавить, з використанням запропонованого дискретного вейвлет-перетворення і безперервного вейвлет-перетворення. Для виявлення несправності обриву стрижня ротора частотний спектр статора аналізується, а потім досліджується. На основі відповідного індексу алгоритм відокремлює справний двигун від несправного з 1, 2 і 3 зламаними стрижнями на холостому ході. Результати. Порівняно із нормальними умовами рекомендований показник значно підвищується за умов зламаних стрижнів. Він може чітко визначити проблемні умови. Було продемонстровано можливість виявлення потенційних несправностей (зламані стрижні) з використанням дискретного вейвлет-перетворення та безперервного вейвлет-перетворення. Метод діагностики адаптується до тимчасових ситуацій, викликаних змінами навантаження та швидкості. Працездатність та ефективність запропонованого методу діагностики продемонстровано за допомогою моделювання у середовищі Simulink® MATLAB

State of the art and trends in the monitoring, detection and diagnosis of failures in electric induction motors

Producción CientíficaDespite the complex mathematical models and physical phenomena on which it is based, the simplicity of its construction, its affordability, the versatility of its applications and the relative ease of its control have made the electric induction motor an essential element in a considerable number of processes at the industrial and domestic levels, in which it converts electrical energy into mechanical energy. The importance of this type of machine for the continuity of operation, mainly in industry, is such that, in addition to being an important part of the study programs of careers related to this branch of electrical engineering, a large number of investigations into monitoring, detecting and quickly diagnosing its incipient faults due to a variety of factors have been conducted. This bibliographic research aims to analyze the conceptual aspects of the first discoveries that served as the basis for the invention of the induction motor, ranging from the development of the Fourier series, the Fourier transform mathematical formula in its different forms and the measurement, treatment and analysis of signals to techniques based on artificial intelligence and soft computing. This research also includes topics of interest such as fault types and their classification according to the engine, software and hardware parts used and modern approaches or maintenance strategies

Low-Cost Diagnosis of Rotor Asymmetries in Induction Machines Working at a Very Low Slip Using the Reduced Envelope of the Stator Current

(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] Fault diagnosis of rotor asymmetries in induction machines working at a very low slip, through Fourier-based methods,usually requires a long acquisition time to achieve a high spectral resolution and a high sampling frequency to reduce aliasing effects. However, this approach generates a huge amount of data, which makes its implementation difficult using embedded devices with small internal memory, such as digital signal processors and field programmable gate arrays or devices with low computing power. In this paper, a new simplified diagnostic signal designated as the reduced envelope of the stator current is introduced to address this problem. The reduced envelope signal is built using only one sample of the current per cycle without any further processing, and it is demonstrated that it carries the same spectral information about the fault as the full-length current signal. Based on this approach, an embedded device has only to store and process a minimal set of samples compared with the raw current signal for a desired resolution. In this paper, the theoretical basis of the proposed method is presented, as well as its experimental validation using two different motors with broken bars: 1) a high-power induction motor working in a factory; and 2) a low-power induction motor mounted in a laboratory test bed.This work was supported by the Spanish "Ministerio de Economia y Competitividad" in the framework of the "Programa Estatal de Investigacion, Desarrollo e Innovacion Orientada a los Retos de la Sociedad" under Project DPI2014-60881-R. Paper no. TEC-00762-2014.Sapena-Bano, A.; Pineda-Sanchez, M.; Puche-Panadero, R.; Martinez-Roman, J.; Kanovic, Z. (2015). Low-Cost Diagnosis of Rotor Asymmetries in Induction Machines Working at a Very Low Slip Using the Reduced Envelope of the Stator Current. IEEE Transactions on Energy Conversion. 30(4):1409-1419. doi:10.1109/TEC.2015.24452161409141930

Automatic Fault Diagnostic System for Induction Motors under Transient Regime Optimized with Expert Systems

[EN] Induction machines (IMs) power most modern industrial processes (induction motors) and generate an increasing portion of our electricity (doubly fed induction generators). A continuous monitoring of the machine's condition can identify faults at an early stage, and it can avoid costly, unexpected shutdowns of production processes, with economic losses well beyond the cost of the machine itself. Machine current signature analysis (MCSA), has become a prominent technique for condition-based maintenance, because, in its basic approach, it is non-invasive, requires just a current sensor, and can process the current signal using a standard fast Fourier transform (FFT). Nevertheless, the industrial application of MCSA requires well-trained maintenance personnel, able to interpret the current spectra and to avoid false diagnostics that can appear due to electrical noise in harsh industrial environments. This task faces increasing difficulties, especially when dealing with machines that work under non-stationary conditions, such as wind generators under variable wind regime, or motors fed from variable speed drives. In these cases, the resulting spectra are no longer simple one-dimensional plots in the time domain; instead, they become two-dimensional images in the joint time-frequency domain, requiring highly specialized personnel to evaluate the machine condition. To alleviate these problems, supporting the maintenance staff in their decision process, and simplifying the correct use of fault diagnosis systems, expert systems based on neural networks have been proposed for automatic fault diagnosis. However, all these systems, up to the best knowledge of the authors, operate under steady-state conditions, and are not applicable in a transient regime. To solve this problem, this paper presents an automatic system for generating optimized expert diagnostic systems for fault detection when the machine works under transient conditions. The proposed method is first theoretically introduced, and then it is applied to the experimental diagnosis of broken bars in a commercial cage induction motor.Burriel-Valencia, J.; Puche-Panadero, R.; Martinez-Roman, J.; Sapena-Bano, A.; Pineda-Sanchez, M.; Pérez-Cruz, J.; Riera-Guasp, M. (2019). Automatic Fault Diagnostic System for Induction Motors under Transient Regime Optimized with Expert Systems. Electronics. 8(1):1-16. https://doi.org/10.3390/electronics8010006S11681Puche-Panadero, R., Pineda-Sanchez, M., Riera-Guasp, M., Roger-Folch, J., Hurtado-Perez, E., & Perez-Cruz, J. (2009). Improved Resolution of the MCSA Method Via Hilbert Transform, Enabling the Diagnosis of Rotor Asymmetries at Very Low Slip. IEEE Transactions on Energy Conversion, 24(1), 52-59. doi:10.1109/tec.2008.2003207Abd-el -Malek, M., Abdelsalam, A. K., & Hassan, O. E. (2017). Induction motor broken rotor bar fault location detection through envelope analysis of start-up current using Hilbert transform. Mechanical Systems and Signal Processing, 93, 332-350. doi:10.1016/j.ymssp.2017.02.014Martinez, J., Belahcen, A., & Muetze, A. (2017). Analysis of the Vibration Magnitude of an Induction Motor With Different Numbers of Broken Bars. IEEE Transactions on Industry Applications, 53(3), 2711-2720. doi:10.1109/tia.2017.2657478Sapena-Bano, A., Pineda-Sanchez, M., Puche-Panadero, R., Perez-Cruz, J., Roger-Folch, J., Riera-Guasp, M., & Martinez-Roman, J. (2015). Harmonic Order Tracking Analysis: A Novel Method for Fault Diagnosis in Induction Machines. IEEE Transactions on Energy Conversion, 30(3), 833-841. doi:10.1109/tec.2015.2416973Sapena-Bano, A., Burriel-Valencia, J., Pineda-Sanchez, M., Puche-Panadero, R., & Riera-Guasp, M. (2017). The Harmonic Order Tracking Analysis Method for the Fault Diagnosis in Induction Motors Under Time-Varying Conditions. IEEE Transactions on Energy Conversion, 32(1), 244-256. doi:10.1109/tec.2016.2626008Burriel-Valencia, J., Puche-Panadero, R., Martinez-Roman, J., Sapena-Bano, A., & Pineda-Sanchez, M. (2017). Short-Frequency Fourier Transform for Fault Diagnosis of Induction Machines Working in Transient Regime. IEEE Transactions on Instrumentation and Measurement, 66(3), 432-440. doi:10.1109/tim.2016.2647458Yin, Z., & Hou, J. (2016). Recent advances on SVM based fault diagnosis and process monitoring in complicated industrial processes. Neurocomputing, 174, 643-650. doi:10.1016/j.neucom.2015.09.081Bazan, G. H., Scalassara, P. R., Endo, W., Goedtel, A., Godoy, W. F., & Palácios, R. H. C. (2017). Stator fault analysis of three-phase induction motors using information measures and artificial neural networks. Electric Power Systems Research, 143, 347-356. doi:10.1016/j.epsr.2016.09.031Mustafidah, H., Hartati, S., Wardoyo, R., & Harjoko, A. (2014). Selection of Most Appropriate Backpropagation Training Algorithm in Data Pattern Recognition. International Journal of Computer Trends and Technology, 14(2), 92-95. doi:10.14445/22312803/ijctt-v14p120Godoy, W. F., da Silva, I. N., Lopes, T. D., Goedtel, A., & Palácios, R. H. C. (2016). Application of intelligent tools to detect and classify broken rotor bars in three-phase induction motors fed by an inverter. IET Electric Power Applications, 10(5), 430-439. doi:10.1049/iet-epa.2015.0469Ghorbanian, V., & Faiz, J. (2015). A survey on time and frequency characteristics of induction motors with broken rotor bars in line-start and inverter-fed modes. Mechanical Systems and Signal Processing, 54-55, 427-456. doi:10.1016/j.ymssp.2014.08.022Valles-Novo, R., de Jesus Rangel-Magdaleno, J., Ramirez-Cortes, J. M., Peregrina-Barreto, H., & Morales-Caporal, R. (2015). Empirical Mode Decomposition Analysis for Broken-Bar Detection on Squirrel Cage Induction Motors. IEEE Transactions on Instrumentation and Measurement, 64(5), 1118-1128. doi:10.1109/tim.2014.2373513De Santiago-Perez, J. J., Rivera-Guillen, J. R., Amezquita-Sanchez, J. P., Valtierra-Rodriguez, M., Romero-Troncoso, R. J., & Dominguez-Gonzalez, A. (2018). Fourier transform and image processing for automatic detection of broken rotor bars in induction motors. Measurement Science and Technology, 29(9), 095008. doi:10.1088/1361-6501/aad3aaMerabet, H., Bahi, T., Drici, D., Halam, N., & Bedoud, K. (2017). Diagnosis of rotor fault using neuro-fuzzy inference system. Journal of Fundamental and Applied Sciences, 9(1), 170. doi:10.4314/jfas.v9i1.12Riera-Guasp, M., Pineda-Sanchez, M., Perez-Cruz, J., Puche-Panadero, R., Roger-Folch, J., & Antonino-Daviu, J. A. (2012). Diagnosis of Induction Motor Faults via Gabor Analysis of the Current in Transient Regime. IEEE Transactions on Instrumentation and Measurement, 61(6), 1583-1596. doi:10.1109/tim.2012.2186650Gyftakis, K. N., Marques Cardoso, A. J., & Antonino-Daviu, J. A. (2017). Introducing the Filtered Park’s and Filtered Extended Park’s Vector Approach to detect broken rotor bars in induction motors independently from the rotor slots number. Mechanical Systems and Signal Processing, 93, 30-50. doi:10.1016/j.ymssp.2017.01.04