scholarly journals Application of damage detection methods using passive reconstruction of impulse response functions

2015 ◽  
Vol 373 (2035) ◽  
pp. 20140070 ◽  
Author(s):  
J. D. Tippmann ◽  
X. Zhu ◽  
F. Lanza di Scalea
Keyword(s):  
Damage Detection ◽  
Impulse Response ◽  
Impulse Response Function ◽  
Processing Technique ◽  
Offshore Wind ◽  
Detection Methods ◽  
Impulse Response Functions ◽  
Response Functions ◽  
New Research ◽  
Detection And Localization

In structural health monitoring (SHM), using only the existing noise has long been an attractive goal. The advances in understanding cross-correlations in ambient noise in the past decade, as well as new understanding in damage indication and other advanced signal processing methods, have continued to drive new research into passive SHM systems. Because passive systems take advantage of the existing noise mechanisms in a structure, offshore wind turbines are a particularly attractive application due to the noise created from the various aerodynamic and wave loading conditions. Two damage detection methods using a passively reconstructed impulse response function, or Green's function, are presented. Damage detection is first studied using the reciprocity of the impulse response functions, where damage introduces new nonlinearities that break down the similarity in the causal and anticausal wave components. Damage detection and localization are then studied using a matched-field processing technique that aims to spatially locate sources that identify a change in the structure. Results from experiments conducted on an aluminium plate and wind turbine blade with simulated damage are also presented.

IMPROVING EXTRACTION OF IMPULSE RESPONSE FUNCTIONS USING STATIONARY WAVELET SHRINKAGE IN TERAHERTZ REFLECTION IMAGING

2010 ◽  
Vol 09 (04) ◽  
pp. 387-394 ◽  
Author(s):  
YANG CHEN ◽  
YIWEN SUN ◽  
EMMA PICKWELL-MACPHERSON
Keyword(s):  
Experimental Data ◽  
Impulse Response ◽  
Response Function ◽  
Impulse Response Function ◽  
Terahertz Imaging ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Inverse Filtering ◽  
Wavelet Shrinkage ◽  
Gaussian Filtering

In terahertz imaging, deconvolution is often performed to extract the impulse response function of the sample of interest. The inverse filtering process amplifies the noise and in this paper we investigate how we can suppress the noise without over-smoothing and losing useful information. We propose a robust deconvolution process utilizing stationary wavelet shrinkage theory which shows significant improvement over other popular methods such as double Gaussian filtering. We demonstrate the success of our approach on experimental data of water and isopropanol.

Hilbert Transformation Impulse Response

2014 ◽  
Vol 19 (4) ◽  
pp. 27-35
Author(s):  
Mariusz Sulima
Keyword(s):  
Time Series ◽  
Impulse Response ◽  
Response Function ◽  
Signal To Noise Ratio ◽  
Impulse Response Function ◽  
Equation System ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Signal To Noise ◽  
Input Time

Abstract This work presents a new DHT impulse response function based on the proposed nonlinear equation system obtained as a result of combining the DHT and IDHT equation systems. In the case of input time series with selected characteristics, the DHT results obtained using this impulse response function are characterised by a higher accuracy compared to the DHT results obtained based on the convolution using other known DHT impulse response functions. The results are also characterised by a higher accuracy than the DHT results obtained using the popular indirect DHT method based on discrete Fourier transform (DFT). Analysis of these example time series with selected characteristics was performed based on the signal-to-noise ratio.

ANALYSIS OF ALTERNATIVE METHODS FOR IMPULSE RESPONSE FUNCTIONS BASED ON SIGNAL-TO-NOISE RATIO ENHANCEMENT AND COMPLETENESS OF SOURCE SIGNAL RECONSTRUCTION USING PASSIVE TIME REVERSAL

2013 ◽  
Vol 21 (03) ◽  
pp. 1350008 ◽  
Author(s):  
YU-HAO HSIEH ◽  
GEE-PINN TOO
Keyword(s):  
Impulse Response ◽  
Response Function ◽  
Time Reversal ◽  
Signal To Noise Ratio ◽  
Impulse Response Function ◽  
Computation Time ◽  
Alternative Methods ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Source Signal

Noise reduction and signal separation are important functions of acoustic signal processing. This study presents a detailed analysis for designing an acoustic signal processing procedure based on the time-reversal method. For some applications, setting transducers to retransmit at source locations is impracticable. Modeling a wave propagation path between two points using impulse response function is one way to overcome this limitation. This paper introduces alternative methods to calculate impulse response function, including an adaptive digital filter, deconvolution with singular value decomposition and Tikhonov regularization, and correlation. A discussion is also provided on the applicable frequency range and anti-noise ability of the impulse response functions obtained by all three techniques through simulation, and subsequently applies them to the designed time reversal process to enhance the signal-to-noise ratio (SNR) and restore source signals through experimentation. The conclusions of this study are given based on the level of accuracy using the SNR and correlation coefficient as indicators, and the computation time required by alternative methods is also an important factor to be discussed for real-time system design. Results prove that the proposed passive time reversal process is capable of enhancing the SNR and restoring the source signal. The alternative methods of calculating the impulse response function offer various advantages, and should be selected according to the application. If the time-cost is the first consideration and there is no dominant noise source, then correlation is the best choice for calculating impulse response function. If completeness of the reconstructed signal is the key point, the optimal deconvolution process is appropriate. If noise reduction is the highest priority in extracting a useful signal from noisy environments while ensuring acceptable restoration capability and computation time, an adaptive digital filter is suitable.

Effect of subthreshold voltage-dependent conductances on the transfer function of branched excitable cells and the conduction of synaptic potentials

1988 ◽  
Vol 59 (3) ◽  
pp. 706-716 ◽  
Author(s):  
K. Yoshii ◽  
L. E. Moore ◽  
B. N. Christensen
Keyword(s):  
Steady State ◽  
Impulse Response ◽  
Response Function ◽  
Transfer Functions ◽  
Impulse Response Function ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Synaptic Current ◽  
Voltage Dependent ◽  
Negative Conductance

1. Impulse response functions were determined from complex point impedance and transfer functions from cultured NG-108 cells to simulate the propagation of a synaptic potential in response to the release of transmitter. In general, the flow of synaptic current has a much shorter duration than the normal membrane time constant, thereby making the use of impulse response functions useful approximations to synaptic events. 2. The resonance observed during the activation of the potassium conductance was reflected in the impulse response function as a pronounced damped oscillation. A comparison of the impulse response functions calculated from point impedance and transfer functions showed similar results for current injections in the growth cone. 3. In addition to the resonance effects of the voltage-dependent conductances on transfer and impulse response functions due principally to the activation of conductances for outward currents, transfer functions were measured during the activation of a steady-state negative conductance. Under these conditions the phase function approaches 180 degrees, indicating that the voltage response is out of phase with the current. 4. In the steady state, the effect of a negative conductance is to algebraically add to the positive conductances and generally decrease the absolute conductance unless there is a net negative current. The decreased conductance enhances the impulse response and the DC space constant, thus leading to a better propagation of slow potentials. This effect can be seen as a decrease in the electrotonic length, L, with intermediate depolarizations. At large depolarizations the steady-state activation of the K conductance generally dominates and leads to a greatly increased electrotonic length. 5. Both the net conductances and the associated kinetics play a role in shaping the potential changes during a synaptic current. This is especially critical if there is a net negative steady-state conductance. Under these conditions there is a surprising reduction in the impulse response function. 6. Thus, during a subthreshold activation of the voltage-dependent negative conductances, the observable synaptic potentials would be either large potential responses due to an apparent increase in the impedance (algebraic summation of positive and negative conductances with a net positive conductance) or a minimal response because of the phasic cancellation due to a net negative conductance. The latter condition could exist near the synaptic reversal potential due to a large synaptic drive and would appear experimentally as a form of inhibition.(ABSTRACT TRUNCATED AT 400 WORDS)

Impulse-response functions and anthropogenic CO2

1995 ◽  
Vol 22 (4) ◽  
pp. 413-416 ◽  
Author(s):  
Francesco N. Tubiello ◽  
Michael Oppenheimer
Keyword(s):  
Impulse Response ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Anthropogenic Co2

A Comparison of Approaches to Select the Informativeness of Priors in BVARs

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Jan Prüser ◽  
Christoph Hanck
Keyword(s):  
Impulse Response ◽  
Prior Information ◽  
Hierarchical Modeling ◽  
Small Samples ◽  
Model Parameters ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Vector Autoregressions ◽  
The Rich ◽  
Bayesian Vars

Abstract Vector autoregressions (VARs) are richly parameterized time series models that can capture complex dynamic interrelationships among macroeconomic variables. However, in small samples the rich parametrization of VAR models may come at the cost of overfitting the data, possibly leading to imprecise inference for key quantities of interest such as impulse response functions (IRFs). Bayesian VARs (BVARs) can use prior information to shrink the model parameters, potentially avoiding such overfitting. We provide a simulation study to compare, in terms of the frequentist properties of the estimates of the IRFs, useful strategies to select the informativeness of the prior. The study reveals that prior information may help to obtain more precise estimates of impulse response functions than classical OLS-estimated VARs and more accurate coverage rates of error bands in small samples. Strategies based on selecting the prior hyperparameters of the BVAR building on empirical or hierarchical modeling perform particularly well.

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