Share Email Print
cover

Proceedings Paper

Big data extraction with adaptive wavelet analysis (Presentation Video)
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

Nondestructive evaluation and sensing technology have been increasingly applied to characterize material properties and detect local damage in structures. More often than not, they generate images or data strings that are difficult to see any physical features without novel data extraction techniques. In the literature, popular data analysis techniques include Short-time Fourier Transform, Wavelet Transform, and Hilbert Transform for time efficiency and adaptive recognition. In this study, a new data analysis technique is proposed and developed by introducing an adaptive central frequency of the continuous Morlet wavelet transform so that both high frequency and time resolution can be maintained in a time-frequency window of interest. The new analysis technique is referred to as Adaptive Wavelet Analysis (AWA). This paper will be organized in several sections. In the first section, finite time-frequency resolution limitations in the traditional wavelet transform are introduced. Such limitations would greatly distort the transformed signals with a significant frequency variation with time. In the second section, Short Time Wavelet Transform (STWT), similar to Short Time Fourier Transform (STFT), is defined and developed to overcome such shortcoming of the traditional wavelet transform. In the third section, by utilizing the STWT and a time-variant central frequency of the Morlet wavelet, AWA can adapt the time-frequency resolution requirement to the signal variation over time. Finally, the advantage of the proposed AWA is demonstrated in Section 4 with a ground penetrating radar (GPR) image from a bridge deck, an analytical chirp signal with a large range sinusoidal frequency change over time, the train-induced acceleration responses of the Tsing-Ma Suspension Bridge in Hong Kong, China. The performance of the proposed AWA will be compared with the STFT and traditional wavelet transform.

Paper Details

Date Published: 13 May 2015
PDF: 2 pages
Proc. SPIE 9435, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2015, 94350V (13 May 2015); doi: 10.1117/12.2083698
Show Author Affiliations
Hongya Qu, Missouri Univ. of Science and Technology (United States)
Genda Chen, Missouri Univ. of Science and Technology (United States)
Yiqing Ni, The Hong Kong Polytechnic Univ. (China)


Published in SPIE Proceedings Vol. 9435:
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2015
Jerome P. Lynch, Editor(s)

© SPIE. Terms of Use
Back to Top