Document Type : Original Article
Authors
Iran University of Science and Technology
Abstract
Permanent magnet synchronous motors (PMSMs) have rencently attracted much attention of scientists due to high reliability, high efficiency, high density of power and torque. However, Permanent magnet (PM) motros suffer from irreversible demagnetization which is one of the most probable problems occurring in these motors. One of the recently developed permanent magnet synchronous motors, is the Flux Switching Permanent Magnet (FSPM) motor whose PM is placed on the stator. In this paper, a novel approach is proposed for the diagnosis of the demagnetization fault in a flux switching permanent magnet motor using electromagnetic torque. In this approach, the harmonic components of the electromagnetic torque are obtained, and then, ReliefF feature selection method is utilized to select the best-ranked harmonic component. The selected harmonic component is then applied to the Support Vector Machine (SVM) classifier to classify different states of the motor’s PM. The proposed approach is validated by Finite Element Model (FEM) simulation.
Keywords
[1] M. H. B. Bafghi, A. Vahedi, A. M. Alikhani, and N. Takorabet, “Shaft twisting effect on steady state performance of flux switching motor with long rotor,” International Journal of Applied Electromagnetics and Mechanics, vol. Preprint, no. Preprint, pp. 1–13, Apr. 2019.
[2] M. H. B. Bafghi and A. Vahedi, “A Comparison of Electric Motors for Electrical Submersible Pumps Used in the Oil and Gas Industry,” IOP Conference Series: Materials Science and Engineering, vol. 433, no. 1, p. 012091, Nov. 2018.
[3] S. S. Moosavi, A. Djerdir, Y. A. Amirat, and D. A. Khaburi, “Demagnetization fault diagnosis in permanent magnet synchronous motors: A review of the state-of-the-art,” Journal of Magnetism and Magnetic Materials, vol. 391, pp. 203–212, Oct. 2015.
[4] J. Faiz and E. Mazaheri-Tehrani, “Demagnetization Modeling and Fault Diagnosing Techniques in Permanent Magnet Machines Under Stationary and Nonstationary Conditions: An Overview,” IEEE Transactions on Industry Applications, vol. 53, no. 3, pp. 2772–2785, May 2017.
[5] S. Moon, J. Lee, H. Jeong, and S. W. Kim, “Demagnetization Fault Diagnosis of a PMSM Based on Structure Analysis of Motor Inductance,” IEEE Transactions on Industrial Electronics, vol. 63, no. 6, pp. 3795–3803, Jun. 2016.
[6] T. Ishikawa and N. Igarashi, “Failure Diagnosis of Demagnetization in Interior Permanent Magnet Synchronous Motors Using Vibration Characteristics,” Applied Sciences, vol. 9, no. 15, p. 3111, Aug. 2019.
[7] Y. Da, X. Shi, and M. Krishnamurthy, “A New Approach to Fault Diagnostics for Permanent Magnet Synchronous Machines Using Electromagnetic Signature Analysis,” IEEE Transactions on Power Electronics, vol. 28, no. 8, pp. 4104–4112, Aug. 2013.
[8] M. Zhu, W. Hu, and N. C. Kar, “Torque-Ripple-Based Interior Permanent-Magnet Synchronous Machine Rotor Demagnetization Fault Detection and Current Regulation,” IEEE Transactions on Industry Applications, vol. 53, no. 3, pp. 2795–2804, May 2017.
[9] F. Mahmouditabar, A. Vahedi, and M. . Bafghi, “Investigation of Direct & Quadrature Current Effects on Demagnetization of Flux Switching Permanent Magnet Motors,” IOP Conference Series: Materials Science and Engineering, vol. 433, no. 1, p. 012092, Nov. 2018.
[10] S. Zhu, M. Cheng, W. Hua, X. Cai, and M. Tong, “Finite Element Analysis of Flux-Switching PM Machine Considering Oversaturation and Irreversible Demagnetization,” IEEE Transactions on Magnetics, vol. 51, no. 11, pp. 1–4, Nov. 2015.
[11] J. D. McFarland, T. M. Jahns, and A. M. El-Refaie, “Demagnetization performance characteristics of flux switching permanent magnet machines,” in 2014 International Conference on Electrical Machines (ICEM), 2014, pp. 2001–2007.
[12] Silong Li, Yingjie Li, and B. Sarlioglu, “Partial Irreversible Demagnetization Assessment of Flux-Switching Permanent Magnet Machine Using Ferrite Permanent Magnet Material,” IEEE Transactions on Magnetics, vol. 51, no. 7, pp. 1–9, Jul. 2015.
[13] M. Rahnama, A. Vahedi, A. M. Alikhani, N. Takorabet, and B. Fazelbakhsheshi, “A novel diode open circuit fault detection in three phase rectifier based on k-means method,” in 2018 IEEE International Conference on Industrial Technology (ICIT), 2018, pp. 600–605.
[14] M. Rahnama, A. Vahedi, A. M. Alikhani, and N. Takorabet, “Diode Open-Circuit Fault Detection in Rectifier Bridge of the Brushless Synchronous Generator,” in 2018 XIII International Conference on Electrical Machines (ICEM), 2018.
[15] M. Rahnama, A. Vahedi, A. Mohammad Alikhani, and A. Montazeri, “Machine-learning approach for fault detection in brushless synchronous generator using vibration signals,” IET Science, Measurement & Technology, vol. 13, no. 6, pp. 852–861, Apr. 2019.
[16] F. Mahmouditabar, A. Vahedi, and P. Ojaghlu, “Investigation of demagnetization effect in an interior V-Shaped magnet synchronous motor at dynamic and static conditions,” Iranian Journal of Electrical and Electronic Engineering, vol. 14, no. 1, 2018.
[17] G. Choi and T. M. Jahns, “Analysis and Design Recommendations to Mitigate Demagnetization Vulnerability in Surface PM Synchronous Machines,” IEEE Transactions on Industry Applications, vol. 54, no. 2, pp. 1292–1301, Mar. 2018.
[18] F. Mahmouditabar, A. Vahedi, and P. Ojaghlu, “Investigation of demagnetization phenomenon in novel ring winding AFPM motor with modified algorithm,” Journal of Magnetism and Magnetic Materials, vol. 491, p. 165539, Dec. 2019.
[19] T. A. Huynh and M.-F. Hsieh, “Irreversible Demagnetization Analysis for Multilayer Magnets of Permanent Magnet-Assisted Synchronous Reluctance Machines Considering Current Phase Angle,” IEEE Transactions on Magnetics, vol. 55, no. 7, pp. 1–9, Jul. 2019.
[20] I. Kononenko, “Estimating attributes: Analysis and extensions of RELIEF,” in European Conference on Machine Learning, 1994, pp. 171–182.
[21] K. Kira and L. A. Rendell, “A Practical Approach to Feature Selection,” Machine Learning Proceedings 1992, pp. 249–256, Jan. 1992.
[22] I. Kononenko, E. Šimec, and M. Robnik-Šikonja, “Overcoming the Myopia of Inductive Learning Algorithms with RELIEFF,” Applied Intelligence, vol. 7, no. 1, pp. 39–55, 1997.
[23] M. Robnik-Šikonja and I. Kononenko, “Theoretical and Empirical Analysis of ReliefF and RReliefF,” Machine Learning, vol. 53, no. 1/2, pp. 23–69, 2003.
)