Time–Frequency Signal Analysis for Nondestructive Evaluation of Pile with Cap

2008 ◽  
Vol 47-50 ◽  
pp. 9-12
Author(s):  
Kuo Feng Lo ◽  
Sheng Huoo Ni ◽  
Jenq Jy Charng ◽  
Yan Hong Huang
Keyword(s):  
Time Domain ◽  
Signal Analysis ◽  
Time History ◽  
Frequency Signal ◽  
Time Frequency ◽  
Integrity Testing ◽  
Testing Signal ◽  
Pass Through ◽  
As Stress ◽  
Pile Integrity

As stress waves decay as they pass through the pile foundation system, it is extremely challenging for all nondestructive testing methods to evaluate the pile integrity of a shaft underneath a structure. In this study, time–frequency signal analysis (TFSA) is used for signal processing and adopted to interpret the pile integrity testing signal. An experimental case with pile lengths of 58m with caps, were tested by the low strain sonic echo method. Traditional time domain analyses can not identify the pile tip response signals 58m lengths. After time-history curves are transformed into a time–frequency domain distribution, the results indicate the pile tip can be located more easily and clearly than the traditional time-domain analyses of pile integrity testing allowed for.

scholarly journals A Hybrid Model Based on Wavelet Decomposition-Reconstruction in Track Irregularity State Forecasting

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Chaolong Jia ◽  
Lili Wei ◽  
Hanning Wang ◽  
Jiulin Yang
Keyword(s):  
Time Series ◽  
Signal Analysis ◽  
Wavelet Decomposition ◽  
Forecast Model ◽  
Practical Significance ◽  
Frequency Signal ◽  
Time Frequency ◽  
Simple Basis ◽  
Best Fitting ◽  
Track Irregularity

Wavelet is able to adapt to the requirements of time-frequency signal analysis automatically and can focus on any details of the signal and then decompose the function into the representation of a series of simple basis functions. It is of theoretical and practical significance. Therefore, this paper does subdivision on track irregularity time series based on the idea of wavelet decomposition-reconstruction and tries to find the best fitting forecast model of detail signal and approximate signal obtained through track irregularity time series wavelet decomposition, respectively. On this ideology, piecewise gray-ARMA recursive based on wavelet decomposition and reconstruction (PG-ARMARWDR) and piecewise ANN-ARMA recursive based on wavelet decomposition and reconstruction (PANN-ARMARWDR) models are proposed. Comparison and analysis of two models have shown that both these models can achieve higher accuracy.

CWT × DWT × DTWT × SDTWT: Clarifying terminologies and roles of different types of wavelet transforms

2020 ◽  
Vol 18 (06) ◽  
pp. 2030001 ◽  
Author(s):  
Rodrigo Capobianco Guido ◽  
Fernando Pedroso ◽  
André Furlan ◽  
Rodrigo Colnago Contreras ◽  
Luiz Gustavo Caobianco ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Discrete Time ◽  
Wavelet Transforms ◽  
Signal Analysis ◽  
Applied Mathematics ◽  
Discrete Wavelet ◽  
Frequency Signal ◽  
Time Frequency ◽  
Subsequent Investigation ◽  
Different Types

Wavelets have been placed at the forefront of scientific researches involving signal processing, applied mathematics, pattern recognition and related fields. Nevertheless, as we have observed, students and young researchers still make mistakes when referring to one of the most relevant tools for time–frequency signal analysis. Thus, this correspondence clarifies the terminologies and specific roles of four types of wavelet transforms: the continuous wavelet transform (CWT), the discrete wavelet transform (DWT), the discrete-time wavelet transform (DTWT) and the stationary discrete-time wavelet transform (SDTWT). We believe that, after reading this correspondence, readers will be able to correctly refer to, and identify, the most appropriate type of wavelet transform for a certain application, selecting relevant and accurate material for subsequent investigation.

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