4,523 research outputs found

    The Detection of Shaft Misalignments using Motor Current Signals from a Sensorless Variable Speed Drive

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    Shaft misalignments are common problems in rotating machines which cause additional dynamic and static loads, and vibrations in the system, leading to early damages and energy loss. It has been shown previously that it is possible to use motor current signature analysis to detect and diagnose this fault in motor drives. However, with a variable speed drive (VSD) system, it becomes dif-ficult to detect faults as the drive compensates for the small changes from fault ef-fects and increased noise in the measured data. In this paper, motor current signa-tures including dynamic and static data have been investigated for misalignment diagnosis in a VSD system. The study has made a systemic comparison of differ-ent control parameters between two common operation modes: open loop and sen-sorless control. Results show that fault detection features on the motor current from the sensorless mode can be the same as those of the open loop mode, however, the detection and diagnosis is significantly more difficult. In contrast, because of the additional frictional load, features from static data show results of early detection and diagnosis of different degrees of misalignment is as good as that from conventional vibration methods

    Advances in the Field of Electrical Machines and Drives

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    Electrical machines and drives dominate our everyday lives. This is due to their numerous applications in industry, power production, home appliances, and transportation systems such as electric and hybrid electric vehicles, ships, and aircrafts. Their development follows rapid advances in science, engineering, and technology. Researchers around the world are extensively investigating electrical machines and drives because of their reliability, efficiency, performance, and fault-tolerant structure. In particular, there is a focus on the importance of utilizing these new trends in technology for energy saving and reducing greenhouse gas emissions. This Special Issue will provide the platform for researchers to present their recent work on advances in the field of electrical machines and drives, including special machines and their applications; new materials, including the insulation of electrical machines; new trends in diagnostics and condition monitoring; power electronics, control schemes, and algorithms for electrical drives; new topologies; and innovative applications

    Manufacturing process applications team (MATeam)

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    Activities of the manufacturing applications team (MATeam) in effecting widespread transfer of NASA technology to aid in the solution of manufacturing problems in the industrial sector are described. During the program's first year of operation, 450 companies, industry associations, and government agencies were contacted, 150 manufacturing problems were documented, and 20 potential technology transfers were identified. Although none of the technology transfers has been commercialized and put in use, several are in the applications engineering phase, and others are in the early stages of implementation. The technology transfer process is described and guidelines used for the preparation of problems statements are included

    Non-invasive load monitoring of induction motor drives using magnetic flux sensors

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    Existing load monitoring methods for induction machines are generally effective, but suffer from sensitivity problems at low speeds and non-linearity problems at high supply frequencies. This study proposes a new noninvasive load monitoring method based on giant magnetoresistance flux sensors to trace stray flux leaking from induction motors. Finite element analysis is applied to analyse stray flux features of test machines. Contrary to the conventional methods of measuring stator and/or rotator rotor voltage and current, the proposed method measures the dynamic magnetic field at specific locations and provides time-spectrum features (e.g. spectrograms), response time load and stator/rotor characteristics. Three induction motors with different starting loading profiles are tested at two separate test benches and their results are analysed in the time-frequency domain. Their steady features and dynamic load response time through spectrograms under variable loads are extracted to correlate with load variations based on spectrogram information. In addition, the transient stray flux spectrogram and time information are more effective for load monitoring than steady state information from numerical and experimental studies. The proposed method is proven to be a low-cost and non-invasive method for induction machine load monitoring

    Preliminary power train design for a state-of-the-art electric vehicle

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    The state-of-the-art (SOTA) of electric vehicles built since 1965 was reviewed to establish a base for the preliminary design of a power train for a SOTA electric vehicle. The performance of existing electric vehicles were evaluated to establish preliminary specifications for a power train design using state-of-the-art technology and commercially available components. Power train components were evaluated and selected using a computer simulation of the SAE J227a Schedule D driving cycle. Predicted range was determined for a number of motor and controller combinations in conjunction with the mechanical elements of power trains and a battery pack of sixteen lead-acid batteries - 471.7 kg at 0.093 MJ/Kg (1040 lbs. at 11.7 Whr/lb). On the basis of maximum range and overall system efficiency using the Schedule D cycle, an induction motor and 3 phase inverter/controller was selected as the optimum combination when used with a two-speed transaxle and steel belted radial tires. The predicted Schedule D range is 90.4 km (56.2 mi). Four near term improvements to the SOTA were identified, evaluated, and predicted to increase range approximately 7%

    Studies in Electrical Machines & Wind Turbines associated with developing Reliable Power Generation

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    The publications listed in date order in this document are offered for the Degree of Doctor of Science in Durham University and have been selected from the author’s full publication list. The papers in this thesis constitute a continuum of original work in fundamental and applied electrical science, spanning 30 years, deployed on real industrial problems, making a significant contribution to conventional and renewable energy power generation. This is the basis of a claim of high distinction, constituting an original and substantial contribution to engineering science

    Power quality and electromagnetic compatibility: special report, session 2

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    The scope of Session 2 (S2) has been defined as follows by the Session Advisory Group and the Technical Committee: Power Quality (PQ), with the more general concept of electromagnetic compatibility (EMC) and with some related safety problems in electricity distribution systems. Special focus is put on voltage continuity (supply reliability, problem of outages) and voltage quality (voltage level, flicker, unbalance, harmonics). This session will also look at electromagnetic compatibility (mains frequency to 150 kHz), electromagnetic interferences and electric and magnetic fields issues. Also addressed in this session are electrical safety and immunity concerns (lightning issues, step, touch and transferred voltages). The aim of this special report is to present a synthesis of the present concerns in PQ&EMC, based on all selected papers of session 2 and related papers from other sessions, (152 papers in total). The report is divided in the following 4 blocks: Block 1: Electric and Magnetic Fields, EMC, Earthing systems Block 2: Harmonics Block 3: Voltage Variation Block 4: Power Quality Monitoring Two Round Tables will be organised: - Power quality and EMC in the Future Grid (CIGRE/CIRED WG C4.24, RT 13) - Reliability Benchmarking - why we should do it? What should be done in future? (RT 15

    Electrical monitoring under transient conditions: a new paradigm in electric motors predictive maintenance

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    [EN] Electric motors condition monitoring is a field of paramount importance for industry. In recent decades, there has been a continuous effort to investigate on new techniques and methods that are able to determine the health of these machines with high accuracy and reliability. Classical methods based on the analysis of diverse machine quantities under stationary condition are being replaced by modern methodologies that are adapted to any operation regime of the machine (including transients). These new methods (especially those based on motor startup signal monitoring), that imply the use of advanced signal processing tools, have shown a great potential and have provided spectacular advantages versus conventional approaches enabling, among other facts, a much more reliable determination of the machine health. This paper reviews the background of this recent condition monitoring trend and shows the advantages of this new approach, with regards to its application to the analysis of electrical quantities. Examples referred to its application to real motors operating in industry are included, proving the huge potential of the transient-based approach and its benefits versus conventional methods.This research was funded by by the Spanish 'Ministerio de Ciencia Innovacion y Universidades' and FEDER program in the framework of the 'Proyectos de I + D de Generacion de Conocimiento del Programa Estatal de Generacion de Conocimiento y Fortalecimiento Cientifico y Tecnologico del Sistema de I + D + i, Subprograma Estatal de Generacion de Conocimiento' (ref: PGC2018-095747-B-I00).Antonino Daviu, JA. (2020). Electrical monitoring under transient conditions: a new paradigm in electric motors predictive maintenance. 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A., Quijano-Lopez, A., Rubbiolo, M., & Climente-Alarcon, V. (2018). Advanced Analysis of Motor Currents for the Diagnosis of the Rotor Condition in Electric Motors Operating in Mining Facilities. IEEE Transactions on Industry Applications, 54(4), 3934-3942. doi:10.1109/tia.2018.2818671Antonino-Daviu, J. A., Pons-Llinares, J., & Lee, S. B. (2016). Advanced Rotor Fault Diagnosis for Medium-Voltage Induction Motors Via Continuous Transforms. IEEE Transactions on Industry Applications, 52(5), 4503-4509. doi:10.1109/tia.2016.2582720Antonino-Daviu, J. A., Riera-Guasp, M., Folch, J. R., & Palomares, M. P. M. (2006). Validation of a new method for the diagnosis of rotor bar failures via wavelet transform in industrial induction machines. IEEE Transactions on Industry Applications, 42(4), 990-996. doi:10.1109/tia.2006.876082Schoen, R. R., & Habetler, T. G. (1997). Evaluation and implementation of a system to eliminate arbitrary load effects in current-based monitoring of induction machines. IEEE Transactions on Industry Applications, 33(6), 1571-1577. doi:10.1109/28.649970Lee, S., Hong, J., Lee, S. B., Wiedenbrug, E. J., Teska, M., & Kim, H. (2013). Evaluation of the Influence of Rotor Axial Air Ducts on Condition Monitoring of Induction Motors. IEEE Transactions on Industry Applications, 49(5), 2024-2033. doi:10.1109/tia.2013.2259132Yang, C., Kang, T.-J., Hyun, D., Lee, S. B., Antonino-Daviu, J. A., & Pons-Llinares, J. (2014). Reliable Detection of Induction Motor Rotor Faults Under the Rotor Axial Air Duct Influence. IEEE Transactions on Industry Applications, 50(4), 2493-2502. doi:10.1109/tia.2013.2297448Park, Y., Jeong, M., Lee, S. B., Antonino-Daviu, J. A., & Teska, M. (2017). Influence of Blade Pass Frequency Vibrations on MCSA-Based Rotor Fault Detection of Induction Motors. IEEE Transactions on Industry Applications, 53(3), 2049-2058. doi:10.1109/tia.2017.2672526Riera-Guasp, M., Cabanas, M. F., Antonino-Daviu, J. A., Pineda-Sanchez, M., & Garcia, C. H. R. (2010). Influence of Nonconsecutive Bar Breakages in Motor Current Signature Analysis for the Diagnosis of Rotor Faults in Induction Motors. IEEE Transactions on Energy Conversion, 25(1), 80-89. doi:10.1109/tec.2009.2032622Antonino-Daviu, J., Riera-Guasp, M., Pons-Llinares, J., Park, J., Lee, S. B., Yoo, J., & Kral, C. (2012). Detection of Broken Outer-Cage Bars for Double-Cage Induction Motors Under the Startup Transient. IEEE Transactions on Industry Applications, 48(5), 1539-1548. doi:10.1109/tia.2012.2210173Antonino-Daviu, J., Jover, P., Riera, M., Arkkio, A., & Roger-Folch, J. (2007). DWT analysis of numerical and experimental data for the diagnosis of dynamic eccentricities in induction motors. Mechanical Systems and Signal Processing, 21(6), 2575-2589. doi:10.1016/j.ymssp.2007.01.008Picot, A., Fournier, E., Regnier, J., TientcheuYamdeu, M., Andrejak, J.-M., & Maussion, P. (2017). Statistic-Based Method to Monitor Belt Transmission Looseness Through Motor Phase Currents. IEEE Transactions on Industrial Informatics, 13(3), 1332-1340. doi:10.1109/tii.2017.2661317Corral-Hernandez, J. A., Antonino-Daviu, J., Pons-Llinares, J., Climente-Alarcon, V., & Frances-Galiana, V. (2015). Transient-Based Rotor Cage Assessment in Induction Motors Operating With Soft Starters. IEEE Transactions on Industry Applications, 51(5), 3734-3742. doi:10.1109/tia.2015.2427271Pons-Llinares, J., Antonino-Daviu, J., Roger-Folch, J., Moríñigo-Sotelo, D., & Duque-Pérez, O. (2014). Mixed eccentricity diagnosis in Inverter-Fed Induction Motors via the Adaptive Slope Transform of transient stator currents. Mechanical Systems and Signal Processing, 48(1-2), 423-435. doi:10.1016/j.ymssp.2014.02.012Henao, H., Demian, C., & Capolino, G.-A. (2003). A frequency-domain detection of stator winding faults in induction machines using an external flux sensor. IEEE Transactions on Industry Applications, 39(5), 1272-1279. doi:10.1109/tia.2003.816531Ramirez-Nunez, J. A., Antonino-Daviu, J. A., Climente-Alarcon, V., Quijano-Lopez, A., Razik, H., Osornio-Rios, R. A., & Romero-Troncoso, R. de J. (2018). Evaluation of the Detectability of Electromechanical Faults in Induction Motors Via Transient Analysis of the Stray Flux. IEEE Transactions on Industry Applications, 54(5), 4324-4332. doi:10.1109/tia.2018.2843371Park, Y., Choi, H., Shin, J., Park, J., Lee, S. B., & Jo, H. (2020). Airgap Flux Based Detection and Classification of Induction Motor Rotor and Load Defects During the Starting Transient. IEEE Transactions on Industrial Electronics, 67(12), 10075-10084. doi:10.1109/tie.2019.2962470Gyftakis, K. N., Panagiotou, P. A., & Lee, S. B. (2020). Generation of Mechanical Frequency Related Harmonics in the Stray Flux Spectra of Induction Motors Suffering From Rotor Electrical Faults. IEEE Transactions on Industry Applications, 56(5), 4796-4803. doi:10.1109/tia.2020.3002975Park, Y., Yang, C., Kim, J., Kim, H., Lee, S. B., Gyftakis, K. N., … Capolino, G.-A. (2019). Stray Flux Monitoring for Reliable Detection of Rotor Faults Under the Influence of Rotor Axial Air Ducts. IEEE Transactions on Industrial Electronics, 66(10), 7561-7570. doi:10.1109/tie.2018.2880670ABB Ability Smart Sensor for Motorshttps://new.abb.com/motors-generators/service/advanced-services/smart-sensor/smart-sensor-for-motorsWEG Motor Scan—Whitepaperhttps://www.weg.net/wegmotorscan/enCeban, A., Pusca, R., & Romary, R. (2012). Study of Rotor Faults in Induction Motors Using External Magnetic Field Analysis. IEEE Transactions on Industrial Electronics, 59(5), 2082-2093. doi:10.1109/tie.2011.2163285ISHKOVA, I. (2016). Detection and classification of faults in induction motor by means of motor current signature analysis and stray flux monitoring. PRZEGLĄD ELEKTROTECHNICZNY, 1(4), 168-172. doi:10.15199/48.2016.04.36Pons-Llinares, J., Antonino-Daviu, J. A., Riera-Guasp, M., Bin Lee, S., Kang, T., & Yang, C. (2015). Advanced Induction Motor Rotor Fault Diagnosis Via Continuous and Discrete Time–Frequency Tools. IEEE Transactions on Industrial Electronics, 62(3), 1791-1802. doi:10.1109/tie.2014.2355816Antonino-Daviu, J., Quijano-Lopez, A., Climente-Alarcon, V., & Garin-Abellan, C. (2017). Reliable Detection of Rotor Winding Asymmetries in Wound Rotor Induction Motors via Integral Current Analysis. IEEE Transactions on Industry Applications, 53(3), 2040-2048. doi:10.1109/tia.2017.267252
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