Sains
Malaysiana 50(11)(2021): 3405-3420
http://doi.org/10.17576/jsm-2021-5011-24
Wavelet Characterizations for Investigating Nonlinear
Oscillators
(Pencirian Gelombang Kecil untuk Mengakaji
Pengayun Tak Linear)
MOHD AFTAR ABU BAKAR1, NORATIQAH MOHD ARIFF1*,
ANDREW V. METCALFE2 & DAVID A. GREEN2
1Department of Mathematical Sciences, Faculty of Science and
Technology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor Darul
Ehsan, Malaysia
2School of Mathematical Sciences, Faculty of Engineering,
Computer and Mathematical Sciences, University of Adelaide, 5005, South
Australia, Australia
Diserahkan: 27 Ogos 2020/Diterima: 3 Mac 2021
ABSTRACT
This study investigates the wavelet-based system
identification capabilities on determining the system nonlinearity based on the
system impulse response function. Wavelet estimates of the instantaneous
envelopes and instantaneous frequency are used to plot the system backbone
curve. This wavelet estimate is then used to estimate the values of the
parameter for the system. Two weakly nonlinear oscillators, which are the
Duffing and the Van der Pol oscillators, have been analyzed using this wavelet
approach. A case study based on a model of an oscillating flap wave energy
converter (OFWEC) was also discussed in this study. Based on the results, it
was shown that this technique is recommended for nonlinear system
identification provided the impulse response of the system can be captured.
This technique is also suitable when the system's form is unknown and for
estimating the instantaneous frequency even when the impulse responses were
polluted with noise.
Keywords: Nonlinear oscillator; system identification;
wavelet; wave energy converter
ABSTRAK
Penyelidikan ini telah mengkaji kemampuan pengecaman sistem
berasaskan gelombang kecil untuk menentukan ketaklinearan sesuatu sistem
berdasarkan fungsi sambutan dedenyut sistem tersebut. Anggaran sampul seketika
dan frekuensi seketika oleh penganggar gelombang kecil digunakan untuk memplot
lengkung tulang belakang sistem tersebut. Penganggar gelombang kecil ini
digunakan untuk menganggarkan nilai parameter bagi sistem tersebut. Dua jenis
pengayun tak linear, iaitu pengayun Duffing dan Van der Pol, telah dianalisis
menggunakan kaedah ini. Satu kajian kes berdasarkan model penukar tenaga ombak
jenis pengayun berkibas (OFWEC) turut dibincangkan dalam kajian ini.
Berdasarkan keputusan yang diperoleh, didapati bahawa teknik ini sesuai
digunakan untuk pengecaman sistem tak linear apabila sambutan dedenyut sistem
tersebut boleh diperoleh. Teknik ini juga sesuai digunakan apabila bentuk
sesuatu sistem itu tidak diketahui dan juga untuk menganggarkan frekuensi
serta-merta walaupun dedenyut sistem dicemari dengan hingar.
Kata kunci: Gelombang
kecil; pengayun tak linear; pengecaman sistem; penukar tenaga ombak
RUJUKAN
Bakar, M.A.A., Green, D.A.,
Metcalfe, A.V. & Ariff, N.M. 2014. Unscented Kalman filtering for wave
energy converters system identification. AIP
Conference Proceedings: Proceedings of the 3rd International Conference on
Mathematical Sciences 1602: 304-310.
Bakar, M.A.A., Green, D.A.,
Metcalfe, A.V. & Najafian, G. 2013. Comparison of heaving buoy and
oscillating flap wave energy converters. AIP
Conference Proceedings: Proceedings
of the 20th National Symposium on Mathematical Sciences 1522: 86-101.
Bakar, M.A.A., Green, D.A. &
Metcalfe, A.V. 2012. Comparison of spectral and wavelet estimators of transfer
function for linear systems. East Asian
Journal on Applied Mathematics 2(3): 214-237.
Carmona, R., Torrésani, B.,
Whitcher, B., Wang, A., Hwang, W.L. & Lees, J.M. 2018. Rwave: Time-Frequency Analysis of 1-D Signals. R package version
2.4-8.
Carmona, R., Hwang, W.L. &
Torrésani, B. 1998. Practical
Time-Frequency Analysis: Gabor and Wavelet Transforms, with an Implementation
in S. New York: Academic Press.
Carmona, R., Hwang, W.L. &
Torrésani, B. 1997. Characterization of signals by the ridges of their wavelet
transforms. IEEE Transactions on Signal
Processing 45(10): 2586-2590.
Feldman, M. 1994. Non-linear system
vibration analysis using Hilbert transform--I. Free vibration analysis method
’Freevib’. Mechanical Systems and Signal
Processing 8(2): 119-127.
Folley, M., Whittaker, T. &
Van't Hoff, J. 2007. The design of small seabed mounted bottom hinged wave
energy converters. 7th European Wave and
Tidal Energy Conference, Porto, Portugal. 11-13 September.
Gouttebroze, S. & Lardies, J.
2001. On using the wavelet transform in modal analysis. Mechanics Research Communications 28(5): 561-569.
Hardle, W., Kerkyacharian, G.,
Picard, D. & Tsybakov, A. 1998. Wavelets,
Approximation and Statistical Applications. New York: Springer-Verlag.
Kijewski, T. & Kareem, A. 2003.
Wavelet transforms for system identification in civil engineering. Computer-Aided Civil and Infrastructure
Engineering 18(5): 339-355.
Lin, C.S. & Lin, M.H. 2020.
System identification from stationary ambient response using wavelet analysis
with variable modal scales. Archive of
Applied Mechanics. Online First https://link.springer.com/article/10.1007%2Fs00419-020-01792-2.
Londoño, J.M., Neild, S.A. &
Cooper, J.E. 2015. Identification of backbone curves of nonlinear systems from
resonance decay responses. Journal of
Sound and Vibration 348: 224-238.
McCusker, J.R., Danai, K. &
Kazmer, D.O. 2010. Validation of dynamic models in the time-scale domain. Journal of Dynamic Systems, Measurement, and
Control 132(6): 061402.
Mohammed, S.A., Bakar, M.A.A. &
Ariff, N.M. 2020. Volatility forecasting of financial time series using wavelet
based exponential generalized autoregressive conditional heteroscedasticity
model. Communications in Statistics -
Theory and Methods 49(1): 178-188.
Moradi, L., Mohammadi, F. &
Baleanu, D. 2019. A direct numerical solution of time-delay fractional optimal
control problems by using Chelyshkov wavelets. Journal of Vibration and Control 25(2): 310-324.
Morison, J., Johnson, J. &
Schaaf, S. 1950. The force exerted by surface waves on piles. Journal of Petroleum Technology 2(5):
149-154.
Nayfeh, A.H. 2008. Perturbation Methods. Weinheim:
Wiley-VCH.
Olsson, D.M. & Nelson, L.S.
1975. The Nelder-Mead simplex procedure for function minimization. Technometrics 17(1): 45-51.
Perez-Ramirez, C.A., Jaen-Cuellar,
A.Y., Valtierra-Rodriguez, M., Dominguez-Gonzalez, A., Osornio-Rios, R.A.,
Romero-Troncoso, R.D.J. & Amezquita-Sanchez, J.P. 2017. A two-step strategy
for system identification of civil structures for structural health monitoring
using wavelet transform and genetic algorithms. Applied Sciences 7(2): 111.
Pernot, S. & Lamarque, C.H.
2001. A wavelet-galerkin procedure to investigate time-periodic systems:
Transient vibration and stability analysis. Journal
of Sound and Vibration 245(5): 845-875.
Pirboudaghi, S., Tarinejad, R. &
Alami, M.T. 2018. Damage detection based on system identification of concrete
dams using an extended finite element–wavelet transform coupled procedure. Journal of Vibration and Control 24(18):
4226-4246.
Ruzzene, M., Fasana, A., Garibaldi,
L. & Piombo, B. 1997. Natural frequencies and dampings identification using
wavelet transform: Application to real data. Mechanical Systems and Signal Processing 11(2): 207-218.
Spina, D., Valente, C. &
Tomlinson, G. 1996. A new procedure for detecting nonlinearity from transient
data using the Gabor transform. Nonlinear
Dynamics 11(3): 235-254.
Staszewski, W.J. 2000. Analysis of
non-linear systems using wavelets. Proceedings
of the Institution of Mechanical Engineers – Part C – Journal of Mechanical
Engineering Science 214(11): 1339-1353.
Staszewski, W.J. 1998.
Identification of non-linear systems using multi-scale ridges and skeletons of
the wavelet transform. Journal of Sound
and Vibration 214(4): 639-658.
Staszewski, W.J. 1997.
Identification of damping in MDOF systems using time-scale decomposition. Journal of Sound and Vibration 203(2):
283-305.
Strogatz, S.H. 2000. Nonlinear Dynamics and Chaos: With
Applications to Physics, Biology, Chemistry, and Engineering. New York: CRC
Press.
Swaidan, W. & Hussin, A. 2016.
Haar wavelet method for constrained nonlinear optimal control problems with
application to production inventory model. Sains
Malaysiana 45(2): 305-313.
Wang, Y. 2017. A new concept using
LSTM neural networks for dynamic system identification. 2017 American Control Conference (ACC), Seattle, WA, USA. 24-26 May. pp. 5324-5329.
Whittaker, T. & Folley, M. 2012.
Nearshore oscillating wave surge converters and the development of oyster. Philosophical Transactions of The Royal
Society A 370: 345-364.
Woo, J., Park, J., Yu, C. & Kim,
N. 2018. Dynamic model identification of unmanned surface vehicles using deep
learning network. Applied Ocean Research 78: 123-133.
Yu, Y., Shenoi, R.A., Zhu, H. &
Xia, L. 2006. Using wavelet transforms to analyze nonlinear ship rolling and
heave-roll coupling. Ocean Engineering 33(7): 912-926.
Zhang, G., Tang, B. & Chen, Z.
2019. Operational modal parameter identification based on PCA-CWT. Measurement 139: 334-345.
*Pengarang untuk surat-menyurat; email: tqah@ukm.edu.my
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