Feature extraction of power transformer vibration signals based on empirical wavelet transform and multiscale entropy
Feature extraction of power transformer vibration signals based on empirical wavelet transform and multiscale entropy
 Author(s): Miaoying Zhao^{ 1} and Gang Xu^{ 1}
 DOI: 10.1049/ietsmt.2017.0188
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 Author(s): Miaoying Zhao^{ 1} and Gang Xu^{ 1}


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Affiliations:
1:
Department of Electrical and Electronic Engineering , North China Electric Power University , No. 2, Beinong Road , Beijing , People's Republic of China

Affiliations:
1:
Department of Electrical and Electronic Engineering , North China Electric Power University , No. 2, Beinong Road , Beijing , People's Republic of China
 Source:
Volume 12, Issue 1,
January
2018,
p.
63 – 71
DOI: 10.1049/ietsmt.2017.0188 , Print ISSN 17518822, Online ISSN 17518830
To achieve an effective feature extraction for power transformer vibration signals, the authors propose a method for signal feature extraction based on empirical wavelet transform (EWT) and multiscale entropy (MSE). First, transformer vibration signals are decomposed into several empirical wavelet functions (EWFs) with the method of EWT. Then, the frequency characteristics of signals are demonstrated in the timefrequency representation by applying a Hilbert transform to each EWF component. Finally, in order to quantify the extracted features, the MSEs of components being highly correlated with the original signals are calculated to construct the eigenvectors of transformer vibration signals. Several experiments are presented showing the effectiveness of this method compared with the classic empirical mode decomposition method.
Inspec keywords: entropy; power transformers; wavelet transforms; eigenvalues and eigenfunctions; Hilbert transforms; feature extraction; timefrequency analysis; vibrational signal processing
Other keywords: feature extraction; multiscale entropy; timefrequency representation; Hilbert transform; empirical wavelet transform; power transformer vibration signals; empirical wavelet functions; eigenvectors
Subjects: Signal processing and detection; Vibrations and shock waves (mechanical engineering); Numerical analysis; Information theory; Mathematical analysis; Information theory; Mechanical engineering applications of IT; Linear algebra (numerical analysis); Integral transforms; Mathematical analysis; Integral transforms; Digital signal processing; Transformers and reactors; Mathematical analysis; Linear algebra (numerical analysis)
References


1)

22. Ming, M., , Shaona, L., Haitao, M., et al: ‘Feature extraction method of motor imagery EEG based on DTCWT sample entropy’. Chinese Control Conf., Hangzhou, China, July 2015, pp. 3964–3968.


2)

21. Fu, L., He, Z.Y., Ro, Z.Q.: ‘Wavelet transform and approximate entropy based identification of faults in power swings’. IET 9th Int. Conf. on Developments in Power System Protection, Glasgow, UK, March 2008, pp. 590–594.


3)

12. Wang, J., Du, H., Guo, M.: ‘Feature extraction using HHTbased locally optimized shorttime fractional Fourier transform for speaker recognition’. IEEE Int. Conf. on Imaging, Vision & Pattern Recognition (iclVPR), 2017, pp. 1–5.


4)

4. Ding, Q.L., Yuan, Y.M., Li, Z.: ‘A wavelet analysis of threedimensional surface vibration signal of running power transformer’. 4th Int. Conf. on Intelligent System and Applied Material, Taiyuan, China, August 2014, pp. 634–637.


5)

15. Li, L., Zhu, Y., Song, Y.: ‘Feature extraction for vibration signal of transformer winding with multiple faults’, Electr. Power Autom. Equip., 2014, 34, (08), pp. 140–146.


6)

30. Wang, S., Huang, Y., Gong, L.: ‘Improved feature extraction using structured fisher discrimination sparse coding scheme for machinery fault diagnosis’, Adv. Mech. Eng., 2016, 8, (12), pp. 155–162.


7)

29. Yafei, J., Yongli, Z., Liuwang, W., et al: ‘Feature extraction and classification on partial discharge signals of power transformers based on VMD and multiscale entropy’, Trans. China Electrotech. Soc., 2016, 31, (19), pp. 208–216.


8)

25. Wu, S.D., Wu, C.W., Lin, S.G., et al: ‘Feature extraction for bearing fault diagnosis using composite multiscale entropy’, IEEE/ASME Int. Conf. on Advanced Intelligent Mechatronics, Wollongong, NSW, Australia, 2013, pp. 1615–1618.


9)

18. Jerome, G.: ‘Empirical wavelet transform’, IEEE Trans. Signal Process., 2013, 61, (16), pp. 3999–4010.


10)

9. Wang, F., Cai, Y., Li, S.: ‘Fault feature extraction of diesel engine based on second generation wavelet and HHT’. 7th Int. Conf. on Mechatronics, Control and Materials (ICMCM), 2016, vol. 104, pp. 653–656.


11)

5. Evagoou, D., Kyprianou, A., Lewin, P.L., et al: ‘Feature extraction of partial discharge signals using the wavelet packet transform and classification with a probabilistic neural network’, IET Sci. Meas. Technol., 2010, 4, (3), pp. 177–192.


12)

2. Munir, B.S., Smit, J.J., Rinaldi, I.G.M.R.: ‘Diagnosing winding and core condition of power transformer by vibration signal analysis’. IEEE Int. Conf. on Condition Monitoring and Diagnosis, Bali, Indonesia, 2012, pp. 429–432.


13)

20. Pan, J., Chen, J., Zi, Y., et al: ‘Monocomponent feature extraction for mechanical fault diagnosis using modified empirical wavelet transform via datadriven adaptive Fourier spectrum segment’, Mech. Syst. Signal Process., 2016, 72, (73), pp. 160–183.


14)

1. Saponara, S., Fanucci, L., Bernardo, F.: ‘Predictive diagnosis of highpower transformer faults by networking vibration measuring nodes with integrated signal processing’, IEEE Trans. Instrum. Meas., 2016, 65, (8), pp. 1749–1760.


15)

13. Canxun, D., Weihua, G., Zhikun, H.: ‘Empirical mode decomposition and Hilbert spectrum analysis based bearing faults diagnosis’. IET Int. Conf. on Information Science and Control Engineering, 2012, pp. 1–5.


16)

17. Singh, J., Darpe, A.K, Singh, S.P.: ‘Bearing damage assessment using JensenRenyi divergence based on EEMD’, Mech. Syst. Signal Process., 2017, 87, pp. 307–339.


17)

7. Kim, J.: ‘Discrete wavelet transformbased feature extraction of experimental voltage signal for Liion cell consistency’, IEEE Trans. Veh. Technol., 2016, 65, (3), pp. 1150–1161.


18)

11. Abdenour, S., Kamal, M., Noureddine, Z.: ‘Bearing health monitoring based on Hilbert–Huang transform, support vector machine, and regression’, IEEE J. Mag., 2015, 64, (1), pp. 52–62.


19)

10. Wu, S., Huang, W., Kong, F.: ‘Vibration features extraction of power transformer using a timescalefrequency analysis method based on WPT and HHT’. 6th IEEE Int. Power Electronics and Motion Control Conf., 2009, pp. 1977–1981.


20)

27. Haikun, S., Jinsha, Y., Yu, W., et al: ‘Feature extraction for partial discharge based on crosswavelet transform and correlation coefficient matrix’, Trans. China Electrotech. Soc., 2014, 29, (4), pp. 274–281.


21)

28. Zhang, K., Zhao, B., Ding, Q., et al: ‘Intrinsic mode selection of transformer vibration signal based on correlation coefficient’. 2015 IEEE Int. Conf. on Information and Automation, Li Jiang, China, October 2015, pp. 1953–1956.


22)

6. Mota, H.d.O., Vasconcelos, F.H., de Castro, C.L.: ‘A comparison of cycle spinning versus stationary wavelet transform for the extraction of features of partial discharge signals’, IEEE Trans. Dielectr. Electr. Insul., 2016, 23, (02), pp. 1106–1118.


23)

8. Hidayat, R., Kristomo, D., Togarma, I.: ‘Feature extraction of the Indonesian phonemes using discrete wavelet and wavelet packet transform’. 8th Int. Conf. on Information Technology and Electrical Engineering (ICITEE), 2016, pp. 6–11.


24)

14. Hongshan, Z., Fanhao, X., Wenqi, X., et al: ‘Feature extraction method of transformer vibration based on ensemble empirical mode decomposition subband’. 2016 IEEE Int. Conf. on Power System Technology (POWERCON), 2016, pp. 1–6.


25)

23. Costa, M., Goldberger, A. L., Peng, C.K.: ‘Multiscale entropy to distinguish physiologic and synthetic RR time series’, Comput. Cardiol., 2002, 29, 137–140.


26)

19. Gilles, J., Heal, K.: ‘A parameterless scalespace approach to find meaningful modes in histogramsapplication to image and spectrum segmentation’, Int. J. Wavelets, Multiresolution Inf. Process., 2014, 12, (6), pp. 1–13.


27)

16. Ling, T., Lv, H., Yu, L.: ‘An EEMDbased multiscale fuzzy entropy approach for complexity analysis in clean energy markets’, Appl. Soft Comput., 2017, 56, pp. 124–133.


28)

24. Li, H., Pan, H., Ren, H.: ‘Highspeed automaton fault detection and diagnosis based on multivariate multiscale entropy’. Int. Conf. on Ubiquitous Robots and Ambient Intelligence, Xi'an, China, 2016, pp. 252–255.


29)

3. Wu, Z.L., Zhu, Y.L.: ‘Features of vibration signal of power transformer using local wave method’. Proc. Int. Conf. Machine Learning and Cybernetics, USA, 2009, pp. 388–393.


30)

26. King, F.W.: ‘Hilbert transforms: Encyclopedia of Mathematics and Its Application’ (Cambridge Univ. Press, Cambridge, UK, 2009).


1)