SMARTLAB MAGNETIC: A MODERN PARADIGM FOR STUDENT LABORATORIES

[EN] Undergraduate laboratories should provide means to help the student visualize often complex concepts, to achieve a more lasting understanding and, thus, a more significant learning of the ideas presented in the lectures. However, the laboratories often require students to struggle with complex instrumentation operation and tedious data collection and processing that affect negatively their motivation and distract off the learning objectives. This paper introduces through a specific implementation a recent paradigm for student laboratories, designated as SmartLabs, as an effort to help overcome these drawbacks. SmartLabs Magnetic is a combination of existing magnetic circuits test equipment plus a versatile sensors set combined with a standard and affordable data acquisition card and a portable device App. SmartLab Magnetic collects data during magnetic circuits laboratory tests, processes this data to provide results more easily related to the basic concepts being tested and manages the data into reports that can be sent to the student e-mail account within the App. Students' opinions of the relevance, usefulness, and motivational effect of the new SmartLab Magnetic were very positive.Martinez-Roman, J.; Sapena-Bano, A.; Pineda-Sanchez, M.; Puche-Panadero, R. (2016). SMARTLAB MAGNETIC: A MODERN PARADIGM FOR STUDENT LABORATORIES. Sensors & Transducers. 197(2):58-66. http://hdl.handle.net/10251/99063S5866197

A critical comparison between DWT and Hilbert-Huang-based methods for the diagnosis of rotor bar failures in induction machines

(c) 2012 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] In this paper, a cutting-edge time-frequency decomposition tool, i.e., the Hilbert-Huang transform (HHT), is applied to the stator startup current to diagnose the presence of rotor asymmetries in induction machines. The objective is to extract the evolution during the startup transient of the left sideband harmonic (LSH) caused by the asymmetry, which constitutes a reliable evidence of the presence of the fault. The validity of the diagnosis methodology is assessed through several tests developed using real experimental signals. Moreover, in this paper, an analytical comparison with an alternative time-frequency decomposition tool, i.e., the discrete wavelet transform (DWT), is carried out. This tool was applied in previous works to the transient extraction of fault-related components, with satisfactory results, even in cases in which the classical Fourier approach does not lead to correct results. The results of the application of the HHT and DWT are analyzed and compared, obtaining novel conclusions about their respective suitability for the transient extraction of asymmetry-related components, as well as the equivalence, with regard to the LSH extraction, between their basic components, namely: 1) intrinsic mode function, for the HHT, and 2) approximation signal for the DWT.This work was supported in part by the Spanish “Ministerio de Educación y Ciencia,” in the framework of the “Programa Nacional de proyectos de Investigación Fundamental,” project reference DPI2008-06583/DPI and in part by “Vicerrectorado de Investigación, Desarrollo e Innovación of the Universidad Politécnica de Valencia” through the Programa de Apoyo a la Investigación y Desarrollo under Contract PAID-06-07.Antonino-Daviu, J.; Riera-Guasp, M.; Pineda-Sanchez, M.; Pérez, RB. (2009). A critical comparison between DWT and Hilbert-Huang-based methods for the diagnosis of rotor bar failures in induction machines. IEEE Transactions on Industry Applications. 45(5):1794-1803. https://doi.org/10.1109/TIA.2009.2027558S1794180345

Predicting toxicity properties through machine learning

It is currently known that the high power of a drug does not fully determine its efficacy. Several properties must also be considered, including absorption, distribution, metabolism, excretion and toxicity [8]. These are the ADME-Tox properties, which are fundamental in the discovery of new effective and safe drugs. Since ignoring these properties is the main cause of failure in the development of new drugs, it is understandable that some techniques arise, such as machine learning, which apply some predictor variables as molecular characteristics to obtain models to determine some of these ADME-Tox properties. In silico models are booming because of the exorbitant expenses involved in discovering a new drug using traditional trial-and-error methods [2], and they have proven to be an effective approach to increase efficiency in drug discovery and development processes. The objective of this study is to analyze the best current machine learning techniques for predicting toxicity as an ADME-Tox property

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

Fault Diagnosis of Rotating Electrical Machines in Transient Regime Using a Single Stator Current's FFT

© 2015 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng 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] The discrete wavelet transform (DWT) has attracted a rising interest in recent years to monitor the condition of rotating electrical machines in transient regime, because it can reveal the time-frequency behavior of the current's components associated to fault conditions. Nevertheless, the implementation of the wavelet transform (WT), especially on embedded or low-power devices, faces practical problems, such as the election of the mother wavelet, the tuning of its parameters, the coordination between the sampling frequency and the levels of the transform, and the construction of the bank of wavelet filters, with highly different bandwidths that constitute the core of the DWT. In this paper, a diagnostic system using the harmonic WT is proposed, which can alleviate these practical problems because it is built using a single fast Fourier transform of one phase's current. The harmonic wavelet was conceived to perform musical analysis, hence its name, and it has spread into many fields, but, to the best of the authors' knowledge, it has not been applied before to perform fault diagnosis of rotating electrical machines in transient regime using the stator current. The simplicity and performance of the proposed approach are assessed by comparison with other types of WTs, and it has been validated with the experimental diagnosis of a 3.15-MW induction motor with broken bars.This work was supported by the Spanish Ministerio de Ciencia e Innovacion through the Programa Nacional de Proyectos de Investigacion Fundamental under Project DPI2011-23740. The Associate Editor coordinating the review process was Dr. Ruqiang Yan.Sapena-Bano, A.; Pineda-Sanchez, M.; Puche-Panadero, R.; Martinez-Roman, J.; Matic, D. (2015). Fault Diagnosis of Rotating Electrical Machines in Transient Regime Using a Single Stator Current's FFT. IEEE Transactions on Instrumentation and Measurement. 64(11):3137-3146. https://doi.org/10.1109/TIM.2015.2444240S31373146641

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

The Harmonic Order Tracking Analysis (HOTA) for the Diagnosis of Induction Generators Working Under Steady State Regime

[EN] Improved fault diagnostic techniques in induction generators is a field of growing interest given the negative impact * that unexpected breakdowns have on energy production and on the electrical system. New diagnostic techniques based on induction generator currents monitoring have recently been developed, but their use is still irrelevant despite the advantages that presents to detect electrical faults in the generator. This situation is due to the needs of high computing power and memory resources which are not available in embedded devices for on-line monitoring, also, to the use of signal processing techniques that generate volumes of data difficult to transfer to control centres, where they could be processed. This paper proposes the use of a recent methodology known as the harmonic order tracking analysis (HOTA) that solve these problems to for the diagnosis of induction generators. This approach can be implemented in low cost digital devices; the resultant patterns are very simple and easily interpretable, even by nonqualified personnel. Moreover, these patterns are characterized by a very low number of parameters, which make easy their transmission to remote control centres. In this paper the practical application of this approach is proposed using a laboratory test bed.This work was supported by the Spanish "Ministerio de Economía y Competitividad" in the framework of the "Programa Programa Estatal de Investigación, Desarrollo e Innovación Orientada a los Retos de la Sociedad¿ (project reference DPI2014-60881-R)Pérez-Cruz, J.; Pérez Vázquez, M.; Pineda-Sanchez, M.; Puche-Panadero, R.; Sapena-Bano, A. (2017). The Harmonic Order Tracking Analysis (HOTA) for the Diagnosis of Induction Generators Working Under Steady State Regime. DEStech Publications. 1864-1869. http://hdl.handle.net/10251/139470S1864186

The Harmonic Order Tracking Analysis Method for the Fault Diagnosis in Induction Motors Under Time-Varying Conditions

[EN] This paper introduces a new approach for improving the fault diagnosis in induction motors under time-varying conditions. A significant amount of published approaches in this field rely on representing the stator current in the time-frequency domain, and identifying the characteristic signatures that each type of fault generates in this domain. However, time-frequency transforms produce three-dimensional (3-D) representations, very costly in terms of storage and processing resources. Moreover, the identification and evaluation of the fault components in the time-frequency plane requires a skilled staff or advanced pattern detection algorithms. The proposed methodology solves these problem by transforming the complex 3-D spectrograms supplied by time-frequency tools into simple x-y graphs, similar to conventional Fourier spectra. These graphs display a unique pattern for each type of fault, even under supply or load time-varying conditions, making easy and reliable the diagnostic decision even for nonskilled staff. Moreover, the resulting patterns can be condensed in a very small dataset, reducing greatly the storage or transmission requirements regarding to conventional spectrograms. The proposed method is an extension to nonstationary conditions of the harmonic order tracking approach. It is introduced theoretically and validated experimentally by using the commercial induction motors feed through electronic converters.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" (Project reference DPI2014-60881-R). Paper no. TEC-00176-2016.Sapena-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.2016.2626008S24425632

Enhanced Simulink Induction Motor Model for Education and Maintenance Training

[EN] The training of technicians in maintenance requires the use of signals produced by faulty machines in different operating conditions, which are difficult to obtain either from the industry or through destructive testing. Some tasks in electricity and control courses can also be complemented by an interactive induction machine model having a wider internal parameter configuration. This paper presents a new analytical model of induction machine under fault, which is able to simulate induction machines with rotor asymmetries and eccentricity in different load conditions, both stationary and transient states and yielding magnitudes such as currents, speed and torque. This model is faster computationally than the traditional method of simulating induction machine faults based on the Finite Element Method and also than other analytical models due to the rapid calculation of the inductances. The model is presented in Simulink by Matlab for the comprehension and interactivity with the students or lecturers and also to allow the easy combination of the effect of the fault with external influences, studying their consequences on a determined load or control system. An associated diagnosis tool is also presented.This work was supported by the Spanish Ministerio de Ciencia e Innovación under the framework of the Programa Nacional de Proyectos de Investigación Fundamental, Project Reference DPI2011-23740Pineda-Sanchez, M.; Climente Alarcón, V.; Riera-Guasp, M.; Puche-Panadero, R.; Pons Llinares, J. (2012). Enhanced Simulink Induction Motor Model for Education and Maintenance Training. Journal of Systemics, Cybernetics and Informatics. 10(2):92-97. http://hdl.handle.net/10251/105282S929710

Short-Frequency Fourier Transform for Fault Diagnosis of Induction Machines Working in Transient Regime

[EN] Transient-based methods for fault diagnosis of induction machines (IMs) are attracting a rising interest, due to their reliability and ability to adapt to a wide range of IM's working conditions. These methods compute the time-frequency (TF) distribution of the stator current, where the patterns of the related fault components can be detected. A significant amount of recent proposals in this field have focused on improving the resolution of the TF distributions, allowing a better discrimination and identification of fault harmonic components. Nevertheless, as the resolution improves, computational requirements (power computing and memory) greatly increase, restricting its implementation in low-cost devices for performing on-line fault diagnosis. To address these drawbacks, in this paper, the use of the short-frequency Fourier transform (SFFT) for fault diagnosis of induction machines working under transient regimes is proposed. The SFFT not only keeps the resolution of traditional techniques, such as the short-time Fourier transform, but also achieves a drastic reduction of computing time and memory resources, making this proposal suitable for on-line fault diagnosis. This method is theoretically introduced and experimentally validated using a laboratory test bench.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. The Associate Editor coordinating the review process was Dr. Edoardo Fiorucci.Burriel-Valencia, J.; Puche-Panadero, R.; Martinez-Roman, J.; Sapena-Bañó, Á.; 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.2016.2647458S43244066