Fault Diagnosis of Offshore Platforms Using the Local Mean Decomposition Method

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Abstract:

Traditional techniques are not suitable for exploring non-stationary and nonlinear signals. Although empirical mode decomposition (EMD) is a powerful tool for the non-stationary and nonlinear signal analysis, yet it still has some shortcomings. Local mean decomposition (LMD), a novel signal processing method, seemingly overcomes many deficiencies of the EMD method and can take place of the EMD method for analyzing non-stationary and nonlinear signals. In this paper, the LMD method is employed to examine the signal captured from the decks of the WZ12-1 platform and succeeds in displaying the reasons causing the excessive vibration of the WZ12-1 platform. The results suggest that the LMD method seems to be a feasible method for fault diagnosis of offshore platforms.

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94-97

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October 2011

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© 2012 Trans Tech Publications Ltd. All Rights Reserved

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[1] Rujian Ma, Guixi Li, Jinshan Lin, Vibration measurement and load identification of offshore platform, Proc. ASME International Conference on Mechanical and Electrical Technology (ICMET2009), Vol. 1 (2009), pp.13-17.

Google Scholar

[2] Andrew K.S. Jardine, Daming Lin, Dragan Banjevic, A reriew machenical diagnostics and prognostics implementing condition-based maintenance, Mechanical Systems and Signal Processing, vol. 20 (2006), p.1483–1510.

DOI: 10.1016/j.ymssp.2005.09.012

Google Scholar

[3] Z.K. Peng, F.L. Chu, Application of the wavelet transform in mechanical condition monitoring and fault diagnostics: a review bibliography, Mechanical Systems and Signal Processing, Vol. 18 (2004), p.199–221.

DOI: 10.1016/s0888-3270(03)00075-x

Google Scholar

[4] N. Tandon, A. Choudhury, A review of vibration and acoustic measurement methods for the diction of defects in rolling element bearing, Tribology International, Vol. 32 (1999), pp.469-480.

DOI: 10.1016/s0301-679x(99)00077-8

Google Scholar

[5] N.E. Huang, Z. Shen, S.R. Long, The Empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proceedings of the Royal Society of London, Vol. A454 (1998), p.903–995.

DOI: 10.1098/rspa.1998.0193

Google Scholar

[6] B. Liu, S. Riemenschneider, Y. Xu, Gearbox fault diagnosis using empirical mode decomposition and Hilbert spectrum, " Mechanical Systems and Signal Processing, Vol. 20 (2006), p.718–734.

DOI: 10.1016/j.ymssp.2005.02.003

Google Scholar

[7] Jonathan S Smith, The local mean decomposition and its application to EEG perception data, J. R. Soc. Interface Vol. 2 (2005), p.443–454.

DOI: 10.1098/rsif.2005.0058

Google Scholar