Structure testing of wave propagation models used in identification of viscoelastic materials

Rensfelt, Agnes; Söderström, Torsten

doi:10.1016/j.automatica.2010.01.027

Cited by 5 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another reason might be that the data model is not flexible enough to accurately describe the data. The validity of the model structure in (14) was studied in [15], and there found to be adequate for this type of experiments. In the following study, the focus will therefore be on the noise properties, since the effect of bias in the modeling of the data might be expected to be small.…”

Section: Methodsmentioning

confidence: 99%

“…The length of the simulated bar was Ä = 2 m, the number of data points AE = 2 15 , and the sampling interval Ì = 10 s. The impulse strain excitation at the right end of the bar was chosen to imitate a standard hammer excitation. The sensor locations were Ü Ò =Ò = 0 0 290 0 646 1 078 1 600 m, and Gaussian white noise with variance 2 = 2 1 ¡ 10 14 was added to each sensor output.…”

Section: ¤ 4 Simulation Studymentioning

confidence: 99%

See 1 more Smart Citation

Parametric identification of complex modulus

Rensfelt

Söderström

2011

Automatica

Self Cite

View full text Add to dashboard Cite

This report treats three different approaches to parametric identification of the complex modulus of a viscoelastic material. In the first approach, a nonparametric estimate is used to fit the parametric model, while in the other two the model is fitted directly from data. In all three cases, theoretical expressions for the accuracy of the estimate are derived under the assumption that the measurement noise is white and that the signal-to-noise ratio is large. The expressions are validated against both simulated and experimental data. In the case of experimental data it is seen that the theoretical expression severely underestimates the variance of the identified parameters, and one of the expressions is therefore modified to cover the case of correlated noise with much better agreement as a result.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: ¤ 4 Simulation Studymentioning

confidence: 99%