Short-Time Continuous Wavelet Transform of the Response of Time-Varying Systems
Abstract
This study provides the first report on the formulation of short-time continuous wavelet transform and its signal reconstruction. This new development is motivated by the needs for adjustable frequency resolution over time in analyzing time-varying systems. An analytical signal with time-dependent frequency components is first used to characterize the behavior of the new transform in terms of time and frequency resolution distribution in time-frequency domain. The dynamic responses of time-varying systems such as a softening Duffing oscillator are next used as an example to demonstrate the effectiveness of the proposed transform and reconstruction. Finally, the developed short-time continuous wavelet transform is applied to develop an adaptive wavelet transform and identify the characteristic frequencies of reinforced concrete slabs with embedded delamination defects from impact-echo testing. The proposed new transform can detect and locate the embedded defects more accurately than the conventional continuous wavelet transform. In engineering applications, synchro-squeezed adaptive wavelet transform gives even more accurate results than the conventional adaptive wavelet.
Recommended Citation
G. Chen, "Short-Time Continuous Wavelet Transform of the Response of Time-Varying Systems," Proceedings of the 2018 International Conference on Sensor Networks and Signal Processing (2018, Xi'an, China), Sensor Networks and Signal Processing (SNSP), Oct 2018.
Meeting Name
2018 International Conference on Sensor Networks and Signal Processing, SNSP 2018 (2018: Oct. 28-31, Xi'an, China)
Department(s)
Civil, Architectural and Environmental Engineering
Research Center/Lab(s)
INSPIRE - University Transportation Center
Keywords and Phrases
Continuous Wavelet Transform; Signal Reconstruction; Short-Time Continuous Wavelet Transform; Adaptive Wavelet Transform; Synchro-Squeezed Adaptive Wavelet Transform
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2018 SNSP, All rights reserved.
Publication Date
01 Oct 2018
Comments
Keynote presentation