Application of Local Wave Method in the Structural Health Monitoring Signal Decomposition

Article Preview

Abstract:

Health monitoring of the bridge structure has gradually become one of the hot topics. The signal decomposition technology is the key technique of the bridge structural health monitoring. The traditional data analysis and processing methods, which can only be applied to stationary or linear signal processing, have significant limitations. However, the structural response signals tested are mostly non-stationary and nonlinear. So methods that can effectively analyze non-stationary and nonlinear signal are urgently needed. Based on the summarization and analysis of the shortage of wavelet analysis method, the application of local wave method for data processing and analysis in structural health monitoring is put forward. The feasibility and superiority of local wave method is discussed. Experimental simulation results show that the application of local wave method in bridge health monitoring signal decomposition is feasible.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

969-973

Citation:

Online since:

October 2013

Authors:

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] A JOHNSON, H F LAM, et al. Phase I IASC-ASCE Structural health monitoring benchmark problem using simulated data[J]. Journal of Engineering Mechanics, 2004, 20(2): 3-15.

DOI: 10.1061/(asce)0733-9399(2004)130:1(3)

Google Scholar

[2] HUANG N E. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proc Roy Soc London A, l998, 454(4): 903-995.

Google Scholar

[3] BALOCCHI, MENICUCCI, SANTARCANGELO, GEMIGNANI, GELLARDUCCI, VARANINI. Deriving the respiratory sinus arrhythmia from the heartbeat time series using empirical mode decomposition[J]. Chaos, Solitons and Fractals, 2004, 20(1): 171-177.

DOI: 10.1016/s0960-0779(03)00441-7

Google Scholar

[4] HUANG N E, WU M L, QU WENDONG, et a1. Applications of Hilbert-Huang transform to non-stationary financial time series analysis[J]. Applied Stochastic Models in Business and Industry, 2003, 19(5): 245-268.

DOI: 10.1002/asmb.501

Google Scholar

[5] GA Q, Ma X J. The partial wave method for the analysis of non-stationary signals and its use in machine fault diagnosis[J]. Proceedings of the International Symposium on Measurement and Test, IEEE, 2001: 1465-1468.

Google Scholar

[6] ZHANG R, ASCE M, Ma SHUO, et al. Hilbert-Huang Transform analysis of dynamic and earthquake motion recordings[J]. Journal of Engineering Mechanics, 2003, 3(7): 861-875.

DOI: 10.1061/(asce)0733-9399(2003)129:8(861)

Google Scholar

[7] NUNES J C, NIANG O, BOUAOUNE Y, et a1. Texture analysis based on the bidimensional empirical mode decomposition with gray-level cooccurrence models[J]. IEEE, 2003: 633-635.

DOI: 10.1109/isspa.2003.1224962

Google Scholar

[8] LCOHEN. Time-Frequency Analysis[J]. Englewood clifs, NJ, Prentice-Hall, 1995, 1(7): 57-59.

Google Scholar