A Diagnosis of Stator Winding Fault Based on Empirical Mode Decomposition in PMSMs

Yıl 2020, Cilt: 8 Sayı: 1 , 73 - 80 , 31.01.2020

Zafer Doğan , Rumeysa Selçuk

https://doi.org/10.17694/bajece.650484

Cited By: 4

https://izlik.org/JA93SJ74HU

Öz

Stator winding faults
may cause severe damages in Permanent Magnet Synchronous Motors (PMSM) if not
detected early on. The earliest fault detection in motors should be made during
transient states throughout the initial starting period. A new approach based
on Empirical Mode Decomposition (EMD) and
statistical analysis was presented for detecting stator winding fault by way of
transient state phase current of PMSM in this study. Models based on finite
elements method were developed for the PMSM representing the healthy and faulty
states in order to implement the suggested fault detection method. Afterwards,
transient state stator phase winding currents were measured for healthy and
faulty states under nominal load in accordance with motor models. These
non-linear current signals monitored were separated into its Intrinsic Mode
Functions (IMF) via the EMD method. Pearson Correlation Coefficient was used
for determining the IMF that most resembles the characteristics of the main
signal. Statistical parameter-based feature extractions were carried out for
the IMF signals determined for the healthy and faulty states. Fault and fault
level detection were carried out successfully by comparing the obtained feature
vectors. The acquired results have put forth that the suggested method can be
used securely for fault detection in electrical machines especially for early
fault detection.

Anahtar Kelimeler

Permanent Magnet Synchronous Motors , Stator winding faults , EMD

Kaynakça

• Referans1. 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,” J. Magn. Magn. Mater., vol. 391, pp. 203–212, 2015.
• Referans2.H. Li, J. Hang, J. Fang, P. Zhang, S. Ding, and Q. Wang, “Inter-turn fault diagnosis of permanent magnet synchronous machine based on variational mode decomposition,” Proc. 13th IEEE Conf. Ind. Electron. Appl. ICIEA 2018, pp. 2422–2425, 2018.
• Referans3.J. C. Urresty, J. R. Riba, and L. Romeral, “Diagnosis of interturn faults in pmsms operating under nonstationary conditions by applying order tracking filtering,” IEEE Trans. Power Electron., vol. 28, no. 1, pp. 507–515, 2013.
• Referans4.H. Lee, H. Jeong, and S. W. Kim, “Diagnosis of Interturn Short-Circuit Fault in PMSM by Residual Voltage Analysis,” SPEEDAM 2018 - Proc. Int. Symp. Power Electron. Electr. Drives, Autom. Motion, pp. 160–164, 2018.
• Referans5.L. ERGENE and C. EKİN, “Cross Saturation Inductance Analysis of a Permanent Magnet Synchronous Motor,” Balk. J. Electr. Comput. Eng., vol. 6, no. 3, pp. 8–13, 2018.
• Referans6.Y. B. Yakut, S. Sünter, and M. Özdemir, “Simulation of Matrix Converter-Fed Permanent Magnet Synchronous Motor With Neural Fuzzy Controller,” vol. 6, no. 2, 2016.
• Referans7.J. Härsjö, Modeling and Analysis of PMSM with Turn-To-Turn Fault. 2016.
• Referans8.S. Moon, H. Jeong, H. Lee, and S. W. Kim, “Interturn short fault diagnosis in a PMSM by voltage and current residual analysis with the faulty winding model,” IEEE Trans. Energy Convers., vol. 33, no. 1, pp. 190–198, 2018.
• Referans9.H. Jeong, S. Moon, J. Lee, and S. W. Kim, “Inter-turn short fault diagnosis of permanent magnet synchronous machines using negative sequence components,” Proceedings of the IEEE International Conference on Industrial Technology. pp. 170–174, 2016.
• Referans10.H. Liang, Y. Chen, S. Liang, and C. Wang, “Fault Detection of Stator Inter-Turn Short-Circuit in PMSM on Stator Current and Vibration Signal,” Applied Sciences, vol. 8, no. 9. p. 1677, 2018.
• Referans11.Y. Maanani and A. Menacer, “Modeling and Diagnosis of the Inter-Turn Short Circuit Fault for the Sensorless Input-Output Linearization Control of the PMSM,” Period. Polytech. Electr. Eng. Comput. Sci., vol. 63, no. 3, pp. 159–168, 2019.
• Referans12.A. Mohammed, J. I. Melecio, and Š. Djurović, “Open-Circuit Fault Detection in Stranded PMSM Windings Using Embedded FBG Thermal Sensors,” IEEE Sens. J., vol. 19, no. 9, pp. 3358–3367, 2019.
• Referans13.G. C. Stone, “Condition monitoring and diagnostics of motor and stator windings - A review,” IEEE Trans. Dielectr. Electr. Insul., vol. 20, no. 6, pp. 2073–2080, 2013.
• Referans14.T. J. Kang, J. Hong, S. Bin Lee, Y. W. Yoon, D. H. Hwang, and D. Kang, “The influence of the rotor on surge pd testing of low voltage AC motor stator windings,” IEEE Trans. Dielectr. Electr. Insul., vol. 20, no. 3, pp. 762–769, 2013.
• Referans15.C. Chuang, Z. Wei, W. Zhifu, and L. Zhi, “The Diagnosis Method of Stator Winding Faults in PMSMs Based on SOM Neural Networks,” Energy Procedia, vol. 105, pp. 2295–2301, 2017.
• Referans16.L. Otava, “Implementation of PMSM Inter-turn Short Fault Detection Using Frequency Analysis of Stator Currents,” IFAC-PapersOnLine, vol. 49, no. 25, pp. 86–91, 2016.
• Referans17.J. C. Urresty, J. R. Riba, and L. Romeral, “Application of the zero-sequence voltage component to detect stator winding inter-turn faults in PMSMs,” Electr. Power Syst. Res., vol. 89, pp. 38–44, 2012.
• Referans18.M. Fitouri, Y. Bensalem, and M. N. Abdelkrim, “Modeling and detection of the short-circuit fault in PMSM using Finite Element Analysis,” IFAC-PapersOnLine, vol. 49, no. 12, pp. 1418–1423, 2016.
• Referans19.S. Liang, Y. Chen, H. Liang, and X. Li, “Sparse representation and SVM diagnosis method inter-turn short-circuit fault in PMSM,” Appl. Sci., vol. 9, no. 2, 2019.
• Referans20.T. Yang, H. Pen, Z. Wang, and C. S. Chang, “Feature Knowledge Based Fault Detection of Induction Motors Through the Analysis of Stator Current Data,” IEEE Trans. Instrum. Meas., vol. 65, no. 3, pp. 549–558, 2016.
• Referans21. Y. Chen, S. Liang, W. Li, H. Liang, and C. Wang, “Faults and diagnosis methods of permanent magnet synchronous motors: A review,” Appl. Sci., vol. 9, no. 10, 2019.
• Referans22.E. G. Strangas, S. Aviyente, and S. S. H. Zaidi, “Time-frequency analysis for efficient fault diagnosis and failure prognosis for interior permanent-magnet AC motors,” IEEE Trans. Ind. Electron., vol. 55, no. 12, pp. 4191–4199, 2008.
• Referans23.S. Ahsanullah, Kazi; Jeyasankar, Elango; Panda, S. K. ; Shanmukha, Ramakrishna; Nadarajan, “Detection and Analysis of Winding and Demagnetization Faults in PMSM based Marine Propulsion Motors” 2017 IEEE International Electric Machines and Drives Conference (IEMDC), pp. 1–7, 2017.
• Referans24.Y. Yang, J. Cheng, and K. Zhang, “An ensemble local means decomposition method and its application to local rub-impact fault diagnosis of the rotor systems,” Meas. J. Int. Meas. Confed., vol. 45, no. 3, pp. 561–570, 2012.
• Referans25.J. Rosero, L. Romeral, J. A. Ortega, and E. Rosero, “Short circuit fault detection in PMSM by means of empirical mode decomposition (EMD) and wigner ville distribution (WVD),” Conf. Proc. - IEEE Appl. Power Electron. Conf. Expo. - APEC, pp. 98–103, 2008.
• Referans26.A. Mejia-Barron, M. Valtierra-Rodriguez, D. Granados-Lieberman, J. C. Olivares-Galvan, and R. Escarela-Perez, “The application of EMD-based methods for diagnosis of winding faults in a transformer using transient and steady state currents,” Meas. J. Int. Meas. Confed., vol. 117, pp. 371–379, 2018.
• Referans27.V. T. Tran, R. Cattley, A. Ball, B. Liang, and S. Iwnicki, “Fault diagnosis of induction motor based on a novel intelligent framework and transient current signals,” Chem. Eng. Trans., vol. 33, pp. 691–696, 2013.
• Referans28.J. A. Antonino-Daviu, A. Quijano-Lopez, V. Fuster-Roig, and C. Nevot, “Case stories of induction motors fault diagnosis based on current analysis,” Pet. Chem. Ind. Conf. Eur. Conf. Proceedings, PCIC Eur., vol. 2016-October, pp. 1–9, 2016.
• Referans29.H. Douglas and P. Pillay, “The impact of wavelet selection on transient motor currefnt signature analysis,” 2005 IEEE Int. Conf. Electr. Mach. Drives, pp. 80–85, 2005.
• Referans30.M, Tezcan. İ, Çanakoğlu., "Asenkron Motorlarda Kırık Rotor Barı Arızalarının Sonlu Elemanlar Yöntemi ile İncelenmesi", IATS' 09, Uluslarası İleri Teknolojiler Sempozyumu, 2009. (in Turkish).
• Referans31.A. T. Çelebi and S. Ertürk, “Sonar imgelerinde ampirik kip ayrışımı ve morfolojik işlemler kullanarak hedef tespiti,” 2010 IEEE 18th Signal Processing and Communications Applications Conference, pp. 760–763, 2010. (in Turkish).
• Referans32.O. Sayli, “Hilbert-huang dönüşümü ile solunum seslerindeki üfürümün saptanmasi,” 2014 22nd Signal Process. Commun. Appl. Conf. SIU 2014 - Proc., no. April, pp. 2194–2197, 2014.(in Turkish).
• Referans33. H. Zhou, Z. Deng, Y. Xia, and M. Fu, “A new sampling method in particle filter based on Pearson correlation coefficient,” Neurocomputing, vol. 216, pp. 208–215, 2016.