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Tytuł artykułu

Application of Spectral and Wavelet Analysis of Stator Current to Detect Angular Misalignment in PMSM Drive Systems

Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper deals with the selected methods of detecting angular misalignment in drive systems with a permanent magnet synchronous motor (PMSM), which are based on the analysis of the stator phase current signal, as well as their experimental verification and comparison. The proposed and compared methods are spectral analysis and wavelet analysis of the stator current, stator current envelope, stator current space vector module. Furthermore, the influence of power supply frequency and load torque on the performance of the proposed diagnostic methods is also discussed. The experimental tests were carried out for an undamaged motor and for two levels of angular misalignment. The article discusses the question of exactly what damage symptoms can be extracted from each of the methods. In the case of spectral analyses, it is demonstrated which multiplicities of the failure frequency are the most sensitive to misalignment and the least sensitive to changes in motor operating condition, which may be considered novel in the case of drive systems with permanent magnet motors. It is also proven that discrete wavelet transform (DWT) of the envelope and monitoring of the value of the relevant components allows the detection of misalignment with the availability of measuring current only in one phase in various motor operating conditions.
Wydawca
Rocznik
Strony
42--60
Opis fizyczny
Bibliogr. 20 poz., rys., tab.
Twórcy
  • Wrocław University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland
  • Wrocław University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland
Bibliografia
  • Antonino-Daviu, J. and Popalent, P. (2018). Detection of Induction Motor Coupling Unbalanced and Misalignment via Advanced Transient Current Signature Analysis. 2018 XIII International Conference on Electrical Machines (ICEM), pp. 2359–2364.
  • Behera, D. P., Behera, R. and Naikan, V. N. (2014). Virtual Fault Simulation for Diagnosis of Shaft Misalignment of Rotating Machine. 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 2476–2480.
  • Bossio, J. M., Bossio, G. R. and De Angelo, C. H. (2009). Angular Misalignment in Induction Motors with Flexible Coupling. 35th Annual Conference of IEEE Industrial Electronics (IECON), pp. 1033–1038.
  • Chacon, J. L, Artigao, E. A., Kappatos, V., Asfis, G., Gan, T. H. and Balachandran, W. (2014). Shaft Angular Misalignment Detection using Acoustic Emission. Applied Acoustics, 85, pp. 12–22.
  • Ewert, P. and Musial, M. (2017). Detecting of Misalignment of the Drive Systems with Induction Motor Supplied by a Frequency Converter. Przegląd elektrotechniczny, 93(2), pp. 34–38.
  • Hang, J., Ding, S., Zhang, J., Cheng, M., Chen, W. and Wang, Q. (2016) Detection of Interturn Short-Circuit Fault for PMSM with Simple Fault Indicator. IEEE Transactions on Energy Conversion, 31(4), pp. 1697–1699.
  • Kim, K. H. (2011). Simple Online Fault Detecting Scheme for Short-Circuited Turn in a PMSM through Current Harmonic Monitoring. IEEE Transactions on Industrial Electronics, 58(1), pp. 2565–2568.
  • Liu, X., Liang, D., Du, J., Yu, Y., Yang, X. and Luo, Z. (2014). Effects Analysis of Misalignments on Dynamic Characteristics Test for Permanent Magnet Synchronous Motor. 2014 17th International Conference on Electrical Machines and Systems (ICEMS), pp. 1543–1547.
  • Olkkonen, J. T. (2011). Discrete Wavelet Transforms – Theory and Applications, InTech, Rijeka, Croatia.
  • Piotrowski, J. (1995). Shaft Alignment Handbook. 2nd ed., Marcel Dekker Inc., New York.
  • Raj, V. P., Natarajan, K. and Girikumar, S. T. (2013). Induction Motor Fault Detection and Diagnosis by Vibration Analysis using MEMS Accelerometer. 2013 International Conference on Emerging Trends in Communication, Control, Signal Processing and Computing Applications (C2SPCA), pp. 1–6.
  • Saputra, P. P., Eliyani, M., Firmansyah, R. and Lastomo, D. (2019a). Haar and Symlet Discrete Wavelete Transform for Identification Misalignment on Three Phase Induction Motor Using Energy Level and Feature Extraction. Journal of Physics: Conference Series 1179 (ICCOMSET), pp. 1–6.
  • Saputra, P. P., Murdianto, F. D., Firmansyah, R. and Widarsono, K. (2019b). Combination of Quadratic Discrimination Analysis and Daubechis Wavelet for Classification Level of Misalignment on Induction Motor. 2019 International Symposium on Electronics and Smart Devices (ISESD), Badung-Bali, Indonesia, pp. 1–5.
  • Tarchala, G., Wolkiewicz, M. and Krzysztofiak, M. (2020). Diagnosis of Short-circuits in Induction Motor Stator Winding Using a Modified Park Transformation. Power Electronics and Drives, 5(41), pp. 123–133.
  • Thomsons, W. T. and Culbert, I. (2017). Current Signature Analysis for Condition Monitoring of Cage Induction Motors. IEEE Press, Wiley, New Jersey.
  • Umbrajkaar, A., Krishnamoorthy, A. (2018). Shaft Misalignment Prediction On Basis Of Discrete Wavelet Transform. International Journal of Mechanical Engineering and Technology (IJMET), 9(7), pp. 336–344.
  • Usman, A., Joshi, B. M. and Rajpurohit B. S. (2017). Review of Fault Modeling Methods for Permanent Magnet Synchronous Motors and their Comparison. 2017 IEEE 11th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), pp. 141–146.
  • Verma, A. K., Sarangi, S. and Kolekar, M. H. (2014). Experimental Investigation of Misalignment Effects on Rotor Shaft Vibration and on Stator Current Signature. Journal of Failure Analysis and Prevention, 14(2), pp. 125–138.
  • Wang, X., Wang, Z., Xu, Z., Cheng, M., Wang, W. and Hu, Y. (2018). Fault Diagnosis and Tolerance of Dual Three-phase PMSM Drives. 2018 IEEE Energy Conversion Congress and Exposition (ECCE), pp. 325–330.
  • Zhang, J., Tounzi, A., Benabou, A. and Le Manach, Y. (2021). Detection of magnetization loss in a PMSM with Hilbert Huang transform applied to non-invasive search coil voltage. Mathematics and Computers in Simulation, 184, pp. 184–195.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-266dbf3f-d630-48be-aabb-775192bb9bc8
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