Partial discharge fault diagnosis based on multi-scale dispersion entropy and a hypersphere multiclass support vector machine

Entropy

Volumn 21, Issue 1, 2019, Pages

  Shang, Haikun ^a   Li, Feng ^b   Wu, Yingjie ^a  

^a NORTHEAST ELECTRIC POWER UNIVERSITY   (China)

^b NARI Group   (China)

Author keywords

Fault diagnosis; HMSVM; Multi scale dispersion entropy; PD; Variational mode decomposition

Indexed keywords

EID: 85060371876     PISSN: None     EISSN: 10994300     Source Type: Journal    
DOI: 10.3390/e21010081     Document Type: Article

References (38)

1. Firuzi, K.; Vakilian, M.; Darabad, V.P.; Phung, B.T.; Blackburn, T.R. A novel method for differentiating and clustering multiple partial discharge sources using S transform and bag of words feature. IEEE Trans. Dielectr. Electr. Insul. 2018, 24, 3694-3702
2. Zhou, Z.L.; Zhou, Y.X.; Huang, X.; Zhang, Y.X. Feature Extraction and Comprehension of Partial Discharge Characteristics in Transformer Oil from Rated AC Frequency to Very Low Frequency. Energies 2018, 11, 1702
3. Hammarstrom, T.J.A. Partial discharge characteristics within motor insulation exposed to multi-level PWM waveforms. IEEE Trans. Dielectr. Electr. Insul. 2018, 25, 559-567
4. Mota, H.D.O.; Vasconcelos, F.H.; Castro, C.L.D. 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, 1106-1118
5. Castillo, J.; Mocquet, A.; Saracco, G. Wavelet transform: A tool for the interpretation of upper mantle converted phases at high frequency. Geophys. Res. Lett. 2018, 28, 4327-4330
6. Li, Y.B.; Xu, M.Q.; Liang, X.H.; Huang, W.H. Application of Bandwidth EMD and Adaptive Multiscale Morphology Analysis for Incipient Fault Diagnosis of Rolling Bearings. IEEE Trans. Ind. Electron. 2017, 64, 6506-6517
7. Bustos, A.; Rubio, H.; Castejon, C.; Garcia-prada, J.C. EMD-Based Methodology for the Identification of a High-Speed Train Running in a Gear Operating State. Sensors 2018, 18, 793
8. Xiao, B.; Fang, L.J.; Li, J.F.; Qi, X.S.; Bai, Y.R. An EMD Method for Ascertaining Maximal Value of Cellular Load in Spatial Load Forecasting. J. Northeast Electr. Power Univ. 2018, 38, 8-14
9. Dragomiretskiy, K.; Zosso, D. Variational Mode Decomposition. IEEE Trans. Signal Process. 2014, 62, 531-544
10. Yao, J.C.; Xiang, Y.; Qian, S.; Wang, S. Noise source identification of diesel engine based on variational mode decomposition and robust independent component analysis. Appl. Acoust. 2017, 116, 184-194
11. Zhang, L.; Veitch, D. Learning Entropy. Lect. Notes Comput. Sci. 2017, 6640, 15-27
12. Shannon, C.E. A mathematical theory of communications. Bell Syst. Tech. J. 1948, 27, 379-423
13. Pincus, S. Approximate entropy (ApEn) as a complexity measure. Chaos 1995, 5, 110-117
14. Wu, H.T.; Yang, C.C.; Lin, G.M.; Haryadi, B. Multiscale Cross-Approximate Entropy Analysis of Bilateral Fingertips Photoplethysmographic Pulse Amplitudes among Middle-to-Old Aged Individuals with or without Type 2 Diabetes. Entropy 2017, 19, 145
15. Richman, J.S.; Moorman, J.R. Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol. Heart Circ. Physiol. 2000, 278, H2039
16. George, M.; Md, A.; Roberto, S. Low Computational Cost for Sample Entropy. Entropy 2018, 20, 61
17. Bandt, C.; Pompe, B. Permutation entropy: A natural complexity measure for time series. Phys. Rev. Lett. 2002, 88, 174102
18. Zhou, S.H.; Qian, S.L.; Chang, W.B.; Xiao, Y.Y. A Novel Bearing Multi-Fault Diagnosis Approach Based on Weighted Permutation Entropy and an Improved SVM Ensemble Classifier. Sensors 2018, 18, 1934
19. Azami, H.; Rostaghi, M.; Fernandez, A.; Escudero, J. Dispersion entropy for the analysis of resting-state MEG regularity in Alzheimer's disease. In Proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society, Orlando, FL, USA, 16-20 August 2016; p. 6417
20. Baldini, G.; Giuliani, R.; Steri, G.; Neisse, R. Physical layer authentication of Internet of Things wireless devices through permutation and dispersion entropy. In Proceedings of the Global Internet of Things Summit, Geneva, Switzerland, 6-9 June 2017; pp. 1-6
21. Azami, H.; Rostaghi, M.; Abasolo, D.; Escudero, J. Refined Composite Multiscale Dispersion Entropy and its Application to Biomedical Signals. IEEE Trans. Bio-Med. Eng. 2017, 99, 1
22. Goldberger, A.L.; Bruce, A.; Peng, C.K.; Costa, M. Multiscale entropy analysis of biological signals. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 2005, 71, 1-9
23. Azami, H.; Escudero, J. Coarse-Graining Approaches in Univariate Multiscale Sample and Dispersion Entropy. Entropy 2018, 20, 138
24. Vapnik, V. Statistical Learning Theory; Wiley: New York, NY, USA, 1998
25. Velazquez-Pupo, R.; Sierra-Romero, A.; Torres-Roman, D.; Romero-Delgado, M. Vehicle detection with occlusion handling, tracking, and OC-SVM classification: A high performance vision-based system. Sensors 2018, 18, 374
26. Scholkopf, B.; Smola, A. Kernel Methods and Support Vector Machines; Academic Press Library in Signal Processing: Amsterdam, The Netherlands, 2003; pp. 857-881
27. Ai, Q.;Wang, A.;Wang, Y.; Sun, H.J. Improvements on twin-hypersphere support vector machine using local density information. In Progress in Artificial Intelligence; Springer: Berlin, Germany, 2018; pp. 1-9
28. Xu, T.; He, D.K. Theory of hypersphere multiclass SVM. Control Theory Appl. 2009, 26, 1293-1297
29. Guo, Y.; Xiao, H. Multiclass multiple kernel learning using hypersphere for pattern recognition. Appl. Intell. 2017, 48, 1-9
30. Kennedy, J.; Eberhart, R. Particle Swarm Optimization. In Proceedings of the IEEE International Conference on Neural Networks, Perth, Western Australia, 27 November-1 December 1995; pp. 1942-1948
31. Wang, Z.; Jia, L.; Qin, Y. Adaptive Diagnosis for Rotating Machineries Using Information Geometrical Kernel-ELM Based on VMD-SVD. Entropy 2018, 20, 73
32. Rostaghi, M.; Azami, H. Dispersion Entropy: A Measure for Time-Series Analysis. IEEE Signal Process. Lett. 2016, 23, 610-614
33. Chen, S.; Mclaughlin, S.; Mulgrew, B. Complex-valued radial basic function network, Part I: Network architecture and learning algorithms. Signal Process. 1994, 35, 19-31
34. Pearson, K. On Lines and Planes of Closest Fit to Systems of Points in Space. Philos. Mag. 1901, 2, 559-572
35. Peng, Z.K.; Tse, P.W.; Chu, F.L. A comparison study of improved Hilbert-Huang transform and wavelet transform: Application to fault diagnosis for rolling bearing. Mech. Syst. Signal Process. 2005, 19, 974-988
36. Mostafizur, R.M.; Anowarul, F.S. Mental Task Classification Scheme Utilizing Correlation Coefficient Extracted from Interchannel Intrinsic Mode Function. BioMed Res. Int. 2017, 1-11
37. Shang, H.K.; Kwok, L.; Li, F. Partial Discharge Feature Extraction Based on Ensemble Empirical Mode Decomposition and Sample Entropy. Entropy 2017, 19, 439
38. Folkes, S.R.; Lahav, O.; Maddox, S.J. An artificial neural network approach to the classification of galaxy spectra. Mon. Notices R. Astron. Soc. 2018, 283, 651-665