International Journal of Naval Architecture and Ocean Engineering

  • 제11권2호
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  • Pages.939-951
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  • 2019
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  • 2092-6782(pISSN)
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  • 2092-6790(eISSN)
대한조선학회 (The Society of Naval Architects of Korea)
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Empirical mode decomposition based on Fourier transform and band-pass filter

  • Chen, Zheng-Shou (Department of Naval Architecture and Ocean Engineering, Zhejiang Ocean University) ;
  • Rhee, Shin Hyung (Department of Naval Architecture and Ocean Engineering, Seoul National University) ;
  • Liu, Gui-Lin (College of Engineering, Ocean University of China)
  • 투고 : 2019.01.25
  • 심사 : 2019.04.27
  • 발행 : 2019.02.18
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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.

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참고문헌

  1. Bendat, J.S., Piersol, A.G., 2010. Random Data: Analysis and Measurement Procedures, fourth ed. John Wiley & Sons, Hoboken NJ.
  2. Boashash, B., 1992a. Estimating and interpreting the instantaneous frequency of a signal. I. Fundamentals. Proc. IEEE 80, 520-538. https://doi.org/10.1109/5.135376
  3. Boashash, B., 1992b. Estimating and interpreting the instantaneous frequency of a signal II. Algorithms and applications. Proc. IEEE 80, 540-568. https://doi.org/10.1109/5.135378
  4. Chen, Z.S., Kim, W.J., 2010. Effect of bidirectional internal flow on fluid-structure interaction dynamics of conveying marine riser model subject to shear current. Int. J. Nav. Arch. Ocean Eng. 4, 57-70. https://doi.org/10.3744/JNAOE.2012.4.1.057
  5. Chen, Z.S., Park, Y.S., Wang, L.P., Kim, W.J., Sun, M., Li, Q., 2015. Empirical decomposition method for modeless component and its application to VIV analysis. Int. J. Nav. Arch. Ocean Eng. 7, 301-314. https://doi.org/10.1515/ijnaoe-2015-0021
  6. Chen, Z.S., Rhee, S.H., 2019. Effect of traveling wave on the vortex-induced vibration of a long flexible pipe. Appl. Ocean Res. 84, 122-132. https://doi.org/10.1016/j.apor.2018.12.011
  7. Choi, J.S., Hong, S., Kim, H.W., Yeu, T.K., Paik, B.G., Kim, J.H., Kim, Y.S., 2008. Measurement technique for strains of a slender structure using fiber bragg grating sensor. J. Shipp. Ocean Eng. 46, 67-73.
  8. Cohen, L., 1995. Time-frequency Analysis. Prentice Hall, Englewood NJ.
  9. Dragomiretskiy, K., Zosso, D., 2014. Variational mode decomposition. IEEE Trans. Signal Process. 62, 531-544. https://doi.org/10.1109/TSP.2013.2288675
  10. Gilles, J., 2013. Empirical wavelet transform. IEEE Trans. Signal Process. 61, 3999-4010. https://doi.org/10.1109/TSP.2013.2265222
  11. Huan, J., Cao, W., Qin, Y., 2018. Prediction of dissolved oxygen in aquaculture based on EEMD and LSSVM optimized by the Bayesian evidence framework. Comput. Electron. Agric. 150, 257-265. https://doi.org/10.1016/j.compag.2018.04.022
  12. Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.C., Tung, C.C., Liu, H.H., 1998. 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, 903-995. https://doi.org/10.1098/rspa.1998.0193
  13. Jose, J., Choi, S.J., 2017. Estimation of slamming coefficients on local members of offshore wind turbine foundation (jacket type) under plunging breaker. Int. J. Nav. Arch. Ocean Eng. 9, 624-640. https://doi.org/10.1016/j.ijnaoe.2017.03.006
  14. Lamb, H., 1975. Hydrodynamics, seventh ed. Cambridge University Press, New York NY.
  15. Liu, F.B., Li, J.T., Liu, L.H., Huang, L., Fang, G.Y., 2017b. Application of the EEMD method for distinction and suppression of motion-induced noise in grounded electrical source airborne TEM system. J. Appl. Geophys. 139, 109-116. https://doi.org/10.1016/j.jappgeo.2017.02.013
  16. Liu, F.S., Chen, J.F., Qin, H.D., 2017a. Frequency response estimation of floating structures by representation of retardation functions with complex exponentials. Mar. Struct. 54, 144-166. https://doi.org/10.1016/j.marstruc.2017.04.001
  17. Liu, F.S., Li, H.J., Lu, H.C., 2016b. Weak-mode identification and time-series reconstruction from high-level noisy measured data of offshore structures. Appl. Ocean Res. 56, 92-106. https://doi.org/10.1016/j.apor.2016.01.001
  18. Liu, F.S., Qi, Y., Li, H.J., Li, W., Wang, B., 2016a. 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, 60-70. https://doi.org/10.1016/j.renene.2015.11.078
  19. Liu, H.,Wu, H., Li, Y., 2018. Smart wind speed forecasting using EWT decomposition, GWO evolutionary optimization, RELM learning and IEWT reconstruction. Energy Convers. Manag. 161, 266-283. https://doi.org/10.1016/j.enconman.2018.02.006
  20. Longuet-Higgins, M.S., 1957. The statistical analysis of a random moving surface. Philos. Trans. Roy. Soc. A: Math., Phys. Eng. Sci. 249, 321-387. https://doi.org/10.1098/rsta.1957.0002
  21. Meignen, S., Perrier, V., 2007. A new formulation for empirical mode decomposition based on constrained optimization. IEEE Signal Process. Lett. 14, 932-935. https://doi.org/10.1109/LSP.2007.904706
  22. Michael, F., 2008. Theoretical analysis and comparison of the Hilbert transform decomposition methods. Mech. Syst. Signal Process. 22, 509-519. https://doi.org/10.1016/j.ymssp.2007.09.013
  23. Mohanty, S., Gupta, K.K., Raju, K.S., 2018. Hurst based vibro-acoustic feature extraction of bearing using EMD and VMD. Meas 117, 200-220. https://doi.org/10.1016/j.measurement.2017.12.012
  24. Omidvarnia, A., Azemi, G., Colditz, P.B., Boashash, B., 2013. A time-frequency based approach for generalized phase synchrony assessment in nonstationary multivariate signals. Digit. Signal Process. 23, 780-790. https://doi.org/10.1016/j.dsp.2013.01.002
  25. Rehman, N.U., Mandic, D.P., 2011. filter bank property of multivariate empirical mode decomposition. IEEE Trans. Signal Process. 59, 2421-2426. https://doi.org/10.1109/TSP.2011.2106779
  26. Schwartz, M., Bennett, W.R., Stein, S., 1966. Communications Systems and Techniques. McGraw-Hill, New York NY.
  27. Wang, D., Zhao, Y., Yi, C., Tsui, K.L., Lin, J.h., 2018. Sparsity guided empirical wavelet transform for fault diagnosis of rolling element bearings. Mech. Syst. Signal Process. 101, 292-308. https://doi.org/10.1016/j.ymssp.2017.08.038
  28. Willden, R.H.J., Grahan, M.R., 2004. Multi-modal vortex-induced vibrations of a vertical riser pipe subject to a uniform current profile. Eur. J. Mech. B Fluid 23, 209-218. https://doi.org/10.1016/j.euromechflu.2003.09.011
  29. Wu, Z.H., Huang, N., 2009. Ensemble empirical mode decomposition: a noiseassisted data analysis method. Adv. Adapt. Data Anal. 1, 1-41. https://doi.org/10.1142/S1793536909000047
  30. Yuan, J., Ji, F., Gao, Y., Zhu, J., Wei, C., Zhou, Y., 2018. Integrated ensemble noisereconstructed empirical mode decomposition for mechanical fault detection. Mech. Syst. Signal Process. 104, 323-346. https://doi.org/10.1016/j.ymssp.2017.11.004
  31. Zhang, X., Miao, Q., Zhang, H., Wang, L., 2018. A parameter-adaptive VMD method based on grasshopper optimization algorithm to analyze vibration signals from rotating machinery. Mech. Syst. Signal Process. 108, 58-72. https://doi.org/10.1016/j.ymssp.2017.11.029

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