Frequency Response Function Measurements via Local Rational Modeling, Revisited
A finite measurement time is at the origin of transient (sometimes called leakage) errors in nonparametric frequency response function (FRF) estimation. If the FRF varies significantly over the frequency resolution of the experiment (= reciprocal of the measurement time), then these transients (leak...
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| Vydáno v: | IEEE transactions on instrumentation and measurement Ročník 70; s. 1 - 16 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York
IEEE
2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0018-9456, 1557-9662 |
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| Abstract | A finite measurement time is at the origin of transient (sometimes called leakage) errors in nonparametric frequency response function (FRF) estimation. If the FRF varies significantly over the frequency resolution of the experiment (= reciprocal of the measurement time), then these transients (leakage) errors cause important bias and variance errors in the FRF estimate. To decrease these errors, several local modeling techniques have been proposed in the literature. This article presents an overview of the existing methods and gives an in-depth bias and variance analysis of the FRF and disturbing noise variance estimates. In addition, a new local modeling approach is described, which combines the small bias error of the local rational approximation with the low noise sensitivity of the local polynomial approximation. It is based on an automatic local model-order selection procedure applied to a specific subclass of rational functions. |
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| AbstractList | A finite measurement time is at the origin of transient (sometimes called leakage) errors in nonparametric frequency response function (FRF) estimation. If the FRF varies significantly over the frequency resolution of the experiment (= reciprocal of the measurement time), then these transients (leakage) errors cause important bias and variance errors in the FRF estimate. To decrease these errors, several local modeling techniques have been proposed in the literature. This article presents an overview of the existing methods and gives an in-depth bias and variance analysis of the FRF and disturbing noise variance estimates. In addition, a new local modeling approach is described, which combines the small bias error of the local rational approximation with the low noise sensitivity of the local polynomial approximation. It is based on an automatic local model-order selection procedure applied to a specific subclass of rational functions. |
| Author | Peumans, Dries Lataire, John Pintelon, Rik Vandersteen, Gerd |
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| Cites_doi | 10.1109/TIM.2009.2022376 10.1109/TIM.2018.2809138 10.1016/j.automatica.2005.09.004 10.1016/j.ymssp.2017.11.044 10.1155/2012/678094 10.1109/TIM.2019.2924570 10.1109/TIM.2012.2232451 10.1109/TIM.2004.838132 10.1137/S0895479896309074 10.3182/20120711-3-BE-2027.00299 10.1109/TIM.2019.2913612 10.1109/TAC.1963.1105517 10.1109/TIM.2018.2800958 10.1109/TIM.2020.2987476 10.1002/9781118287422 10.1016/j.ymssp.2009.08.009 10.1109/TIM.2019.2904131 10.1016/S0167-7152(96)00192-7 10.1109/TAC.1959.6429401 |
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| References | ref13 ref12 ref14 ref10 ref2 ljung (ref23) 1999 ref17 brillinger (ref6) 1981 ref19 ref18 ref24 ewins (ref1) 1991 ref25 ref20 van assche (ref16) 2006; 2 ref22 ref21 ref8 ref7 ref9 geerardyn (ref11) 2016 ref4 ref3 henrici (ref15) 1974; 1 ref5 picinbono (ref26) 1993 |
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| SubjectTerms | Approximation Bias Estimation Frequency measurement Frequency response Frequency response function (FRF) Frequency response functions Leakage local polynomial (LP) modeling local rational modeling Low noise Mathematical analysis Measurement uncertainty Modelling Noise measurement Noise reduction Noise sensitivity nonparametric estimation Nonparametric statistics Polynomials Rational functions Time measurement transient Transient analysis Variance analysis |
| Title | Frequency Response Function Measurements via Local Rational Modeling, Revisited |
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