본문 바로가기
KERD
논문KCI 등재

Empirical mode decomposition based on Fourier transform and band-pass filter

0

조회

0

다운로드

1

인용

0

참고문헌

키워드

주제분류조선공학

초록

A novel empirical mode decomposition strategy based on Fourier transform and band-pass filter techniques, contributing to efficient instantaneous vibration analyses, is developed in this study. Two key improvements are proposed. The first is associated with the adoption of a band-pass filter technique for intrinsic mode function sifting. The primary characteristic of decomposed components is that their bandwidths do not overlap in the frequency domain. The second improvement concerns an attempt to design narrowband constraints as the essential requirements for intrinsic mode function to make it physically meaningful. Because all decomposed components are generated with respect to their intrinsic narrow bandwidth and strict sifting from high to low frequencies successively, they are orthogonal to each other and are thus suitable for an instantaneous frequency analysis. The direct Hilbert spectrum is employed to illustrate the instantaneous time-frequency-energy distribution. Commendable agreement between the illustrations of the proposed direct Hilbert spectrum and the traditional Fourier spectrum was observed. This method provides robust identifications of vibration modes embedded in vibration processes, deemed to be an efficient means to obtain valuable instantaneous information.

Abstract

A novel empirical mode decomposition strategy based on Fourier transform and band-pass filter techniques, contributing to efficient instantaneous vibration analyses, is developed in this study. Two key improvements are proposed. The first is associated with the adoption of a band-pass filter technique for intrinsic mode function sifting. The primary characteristic of decomposed components is that their bandwidths do not overlap in the frequency domain. The second improvement concerns an attempt to design narrowband constraints as the essential requirements for intrinsic mode function to make it physically meaningful. Because all decomposed components are generated with respect to their intrinsic narrow bandwidth and strict sifting from high to low frequencies successively, they are orthogonal to each other and are thus suitable for an instantaneous frequency analysis. The direct Hilbert spectrum is employed to illustrate the instantaneous time-frequency-energy distribution. Commendable agreement between the illustrations of the proposed direct Hilbert spectrum and the traditional Fourier spectrum was observed. This method provides robust identifications of vibration modes embedded in vibration processes, deemed to be an efficient means to obtain valuable instantaneous information.

참고문헌 (31)

  1. Bendat, J. S., "Random Data : Analysis and Measurement Procedures" (2010), John Wiley & Sons
  2. Boashash, B., "Estimating and interpreting the instantaneous frequency of a signal. I. Fundamentals", Proc. IEEE, 80, pp.520~538 (1992)
  3. Boashash, B., "Estimating and interpreting the instantaneous frequency of a signal II. Algorithms and applications", Proc. IEEE, 80, pp.540~568 (1992)
  4. Zheng-Shou Chen, "Effect of bidirectional internal flow on fluid–structure interaction dynamics of conveying marine riser model subject to shear current", International Journal of Naval Architecture and Ocean Engineering, 4(1), pp.57~70 (2012), 대한조선학회
  5. Zheng-Shou Chen, "Empirical decomposition method for modeless component and its application to VIV analysis", International Journal of Naval Architecture and Ocean Engineering, 7(2), pp.301~314 (2015), 대한조선학회
  6. Chen, Z. S., "Effect of traveling wave on the vortex-induced vibration of a long flexible pipe", Appl. Ocean Res, 84, pp.122~132 (2019)
  7. Choi, J. S., "Measurement technique for strains of a slender structure using fiber bragg grating sensor", J. Shipp. Ocean Eng, 46, pp.67~73 (2008)
  8. Cohen, L., "Time-frequency Analysis" (1995), Prentice Hall
  9. Dragomiretskiy, K., "Variational mode decomposition", IEEE Trans. Signal Process, 62, pp.531~544 (2014)
  10. Gilles, J., "Empirical wavelet transform", IEEE Trans. Signal Process, 61, pp.3999~4010 (2013)
  11. Huan, J., "Prediction of dissolved oxygen in aquaculture based on EEMD and LSSVM optimized by the Bayesian evidence framework", Comput. Electron. Agric, 150, pp.257~265 (2018)
  12. Huang, N. E., "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis", Proc. Royal Soc. A: Math., Phys. Eng. Sci, 454, pp.903~995 (1998)
  13. Jithin Jose, "Estimation of slamming coefficients on local members of offshore wind turbine foundation (jacket type) under plunging breaker", International Journal of Naval Architecture and Ocean Engineering, 9(6), pp.624~640 (2017), 대한조선학회
  14. Lamb, H., "Hydrodynamics" (1975), Cambridge University Press
  15. Liu, F. B., "Application of the EEMD method for distinction and suppression of motion-induced noise in grounded electrical source airborne TEM system", J. Appl. Geophys, 139, pp.109~116 (2017)
  16. Liu, F. S., "Frequency response estimation of floating structures by representation of retardation functions with complex exponentials", Mar. Struct, 54, pp.144~166 (2017)
  17. Liu, F. S., "Weak-mode identification and time-series reconstruction from high-level noisy measured data of offshore structures", Appl. Ocean Res, 56, pp.92~106 (2016)
  18. Liu, F. S., "Discrepancy study of modal parameters of a scale jacket-type supporting structure of 3. 0-MW offshore wind turbine in water and in air", Renew. Energy, 89, pp.60~70 (2016)
  19. Liu, H., "Smart wind speed forecasting using EWT decomposition, GWO evolutionary optimization, RELM learning and IEWT reconstruction", Energy Convers. Manag, 161, pp.266~283 (2018)
  20. Longuet-Higgins, M. S., "The statistical analysis of a random moving surface", Philos. Trans. Roy. Soc. A: Math., Phys. Eng. Sci, 249, pp.321~387 (1957)
  21. Meignen, S., "A new formulation for empirical mode decomposition based on constrained optimization", IEEE Signal Process. Lett, 14, pp.932~935 (2007)
  22. Michael, F., "Theoretical analysis and comparison of the Hilbert transform decomposition methods", Mech. Syst. Signal Process, 22, pp.509~519 (2008)
  23. Mohanty, S., "Hurst based vibro-acoustic feature extraction of bearing using EMD and VMD", Meas, 117, pp.200~220 (2018)
  24. Omidvarnia, A., "A time-frequency based approach for generalized phase synchrony assessment in nonstationary multivariate signals", Digit. Signal Process, 23, pp.780~790 (2013)
  25. Rehman, N. U., "filter bank property of multivariate empirical mode decomposition", IEEE Trans. Signal Process, 59, pp.2421~2426 (2011)
  26. Schwartz, M., "Communications Systems and Techniques" (1966), McGraw-Hill
  27. Wang, D., "Sparsity guided empirical wavelet transform for fault diagnosis of rolling element bearings", Mech. Syst. Signal Process, 101, pp.292~308 (2018)
  28. Willden, R. H. J., "Multi-modal vortex-induced vibrations of a vertical riser pipe subject to a uniform current profile", Eur. J. Mech. B Fluid, 23, pp.209~218 (2004)
  29. Wu, Z. H., "Ensemble empirical mode decomposition : a noiseassisted data analysis method", Adv. Adapt. Data Anal, 1, pp.1~41 (2009)
  30. Yuan, J., "Integrated ensemble noisereconstructed empirical mode decomposition for mechanical fault detection", Mech. Syst. Signal Process, 104, pp.323~346 (2018)
  31. Zhang, X., "A parameter-adaptive VMD method based on grasshopper optimization algorithm to analyze vibration signals from rotating machinery", Mech. Syst. Signal Process, 108, pp.58~72 (2018)

관련 논문AI추천

이 간행물의 다른 논문 (28건)