Signal Averaging at Modest Cost

Hudgings, D. W.; Dragt, Alex J.

doi:10.1119/1.1986804

Cited by 5 publications

(5 citation statements)

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“…In order to render the PRRF insensitive to the noise and strengthen the signal containing the damage information, a signal averaging technique is employed in the present study 46 . Signal averaging has been extensively used for dealing with noisy measurements in fault diagnosis problems 47,48 .…”

Section: The Damage Identification Algorithmmentioning

confidence: 99%

“…Since the noise is random, that is, uncorrelated with the input, it is expected that

< false(N_{i}^{λ} - N_{i}^{*} false) >_{n}

tends toward zero as n becomes large 46 . When the noise is uncorrelated, the signal‐to‐noise ratio for < N i > n will be improved by a factor of n 1/2 46 .…”

Section: The Damage Identification Algorithmmentioning

confidence: 99%

“…Since the noise is random, that is, uncorrelated with the input, it is expected that

< false(N_{i}^{λ} - N_{i}^{*} false) >_{n}

tends toward zero as n becomes large 46 . When the noise is uncorrelated, the signal‐to‐noise ratio for < N i > n will be improved by a factor of n 1/2 46 . The signal‐to‐noise ratio is increased if the following conditions are satisfied: Signal

N_{i}^{s}

is uncorrelated to noise, and noise (

N_{i}^{λ} - N_{i}^{*}

) is uncorrelated. Signal power E [( N s ) 2 ] is constant in replicate measurements. Noise is random, with a mean of zero and constant variance in the replicate measurements. …”

Section: The Damage Identification Algorithmmentioning

confidence: 99%

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Indirect health monitoring of bridges using Tikhonov regularization scheme and signal averaging technique

Krishnanunni

Rao

2021

Struct Control Health Monit

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Summary This paper presents a novel damage identification technique for bridges based on the dynamic response of a moving vehicle. A quarter car vehicle model instrumented with two accelerometers and an inertial profilometer is used for this purpose. The method involves the coupling of Tikhonov regularization scheme with signal averaging technique to handle the problem of measurement noise and road roughness. In the first stage, a damage‐dependent road roughness profile is estimated from measured acceleration response by minimizing a Tikhonov regularized least squares cost function. The second stage involves the minimization of the profile roughness residual function that depends on the location and magnitude of damage. This objective function compares the roughness profile computed from the first stage and that measured by the inertial profilometer. It is proved that efficient damage detection ensues from considering multiple runs of the vehicle and choosing the appropriate regularization parameter. The present study considers various aspects, such as the uniqueness of results, robustness of results to measurement noise, effect of modelling error, effect of vehicle speed, and uncertainty in estimation. Numerical results show that the approach is capable of detecting the magnitude as well as the location of damage. In addition, the problem of road roughness and measurement noise is handled competently.

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Section: The Damage Identification Algorithmmentioning

confidence: 99%

“…Since the noise is random, that is, uncorrelated with the input, it is expected that

< false(N_{i}^{λ} - N_{i}^{*} false) >_{n}

tends toward zero as n becomes large 46 . When the noise is uncorrelated, the signal‐to‐noise ratio for < N i > n will be improved by a factor of n 1/2 46 .…”

Section: The Damage Identification Algorithmmentioning

confidence: 99%

“…Since the noise is random, that is, uncorrelated with the input, it is expected that

< false(N_{i}^{λ} - N_{i}^{*} false) >_{n}

N_{i}^{s}

is uncorrelated to noise, and noise (

N_{i}^{λ} - N_{i}^{*}

) is uncorrelated. Signal power E [( N s ) 2 ] is constant in replicate measurements. Noise is random, with a mean of zero and constant variance in the replicate measurements. …”

Section: The Damage Identification Algorithmmentioning

confidence: 99%

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Indirect health monitoring of bridges using Tikhonov regularization scheme and signal averaging technique

Krishnanunni

Rao

2021

Struct Control Health Monit

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“…In several experiments involving tetrarnethylethylene, however, the decay rate of S( 3P) was sufficiently fast that a time base of 50 ps per channel had to be employed. This was achieved by using an external crystal clock [8] with the analyzer. T o reduce the statistical fluctuations in the signal to an acceptable level (-3%), a kinetic decay curve for sulfur atoms was developed by multiple flashing of the reaction gas mixture.…”

Section: Methodsmentioning

confidence: 99%

A flash photolysis–resonance fluorescence kinetics study of the reactions of ground‐state sulfur atoms. V. Rate parameters for the reaction of S(³P)with cis‐2‐butene and tetramethylethylene

Davis

Klemm

1973

Int J of Chemical Kinetics

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Rate parameters for the reaction of ground-state atomic sulfur, S(3P), with the olefins cis-2-butene and tetramethylethylene have been determined over a temperature range of -280°K.A major finding of this study was that the rate constants for both reactions showed negative temperature dependencies. When k is expressed in the form of an Arrhcnius equation, this necessarily leads to negative activation energies: (f0.23 f 0.09 kcal/mole) (2 19"500"K) R T (+ 1.29 f 0.23 kcal/mole) RT k l = (4.68 f 0.70) X 10-12 exp (252"-500"K) k p = (4.68 & 1.70) X 10-12expUnits are cm3 molec-'s-'. When a threshold energy of 0.0 kcal/mole is assumed for reaction(2), the temperature dependence of the preexponential term has a value of T-Z. Making the usual simplifying assumptions, neither collision theory nor transition state theory leads to a preexponential factor with a strong enough negative temperature dependence. A comparison of these results with those derived from studies of the reactions of atomic oxygen, O( "), with the same olefins shows that in both studies simple bimolecular processes were being examined. Also discussed are the possible experimental and theoretical ramifications of these new results.

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“…In experimental science, a situation often arises where the response to some stimulus is very weak or the measurable signal is exceedingly small, swamped by unwanted noise [1]. In experimental physics, there are many practical examples in which one may come across with this type of signal.…”

Section: Introductionmentioning

confidence: 99%

Reducing noise by repetition: introduction to signal averaging

Hassan

Anwar

2010

Eur. J. Phys.

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This paper describes theory and experiments, taken from biophysics and physiological measurements, to illustrate the technique of signal averaging. In the process, students are introduced to the basic concepts of signal processing, such as digital filtering, Fourier transformation, baseline correction, pink and Gaussian noise, and the cross- and autocorrelation functions. From these computations, the students estimate physically interesting parameters such as the pulse rate and blood flow velocity. They also learn about some of the pitfalls encountered in quantifying the signal and noise components for a meaningful computation of the signal-to-noise ratio.

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Signal Averaging at Modest Cost

Cited by 5 publications

References 0 publications

Indirect health monitoring of bridges using Tikhonov regularization scheme and signal averaging technique

Indirect health monitoring of bridges using Tikhonov regularization scheme and signal averaging technique

A flash photolysis–resonance fluorescence kinetics study of the reactions of ground‐state sulfur atoms. V. Rate parameters for the reaction of S(³P)with cis‐2‐butene and tetramethylethylene

Reducing noise by repetition: introduction to signal averaging

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Signal Averaging at Modest Cost

Cited by 5 publications

References 0 publications

Indirect health monitoring of bridges using Tikhonov regularization scheme and signal averaging technique

Indirect health monitoring of bridges using Tikhonov regularization scheme and signal averaging technique

A flash photolysis–resonance fluorescence kinetics study of the reactions of ground‐state sulfur atoms. V. Rate parameters for the reaction of S(3P)with cis‐2‐butene and tetramethylethylene

Reducing noise by repetition: introduction to signal averaging

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A flash photolysis–resonance fluorescence kinetics study of the reactions of ground‐state sulfur atoms. V. Rate parameters for the reaction of S(³P)with cis‐2‐butene and tetramethylethylene