### Resumé

Originalsprog | Engelsk |
---|---|

Tidsskrift | Mechanical Systems and Signal Processing |

Vol/bind | 8 |

Udgave nummer | 4 |

Sider (fra-til) | 639-647 |

ISSN | 0888-3270 |

DOI | |

Status | Udgivet - 24. apr. 2002 |

Udgivet eksternt | Ja |

### Fingeraftryk

### Citer dette

*Mechanical Systems and Signal Processing*,

*8*(4), 639-647. https://doi.org/doi:10.1006/mssp.1994.1045

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*Mechanical Systems and Signal Processing*, bind 8, nr. 4, s. 639-647. https://doi.org/doi:10.1006/mssp.1994.1045

**On the Optimal Location of Sensors for Parametric Identification of Linear Systems.** / Kirkegaard, Poul Henning; Brincker, Rune.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review

TY - JOUR

T1 - On the Optimal Location of Sensors for Parametric Identification of Linear Systems

AU - Kirkegaard, Poul Henning

AU - Brincker, Rune

PY - 2002/4/24

Y1 - 2002/4/24

N2 - An outline of the field of optimal location of sensors for parametric identification of linear structural systems is presented. There are few papers devoted to the case of optimal location of sensors in which the measurements are modeled by a random field with non-trivial covariance function. It is assumed most often that the results of the measurements are statistically independent random variables. In an example the importance of considering the measurements as statistically dependent random variables is shown. The covariance of the model parameters expected to be obtained is investigated with variations in the number and location of sensors. Further, the influence of noise on the optimal location of the sensors is investigated. It is found that the optimal locations of sensors seem to become less sensitive to e.g. the noise-to-signal ratio within increasing number of sensors.

AB - An outline of the field of optimal location of sensors for parametric identification of linear structural systems is presented. There are few papers devoted to the case of optimal location of sensors in which the measurements are modeled by a random field with non-trivial covariance function. It is assumed most often that the results of the measurements are statistically independent random variables. In an example the importance of considering the measurements as statistically dependent random variables is shown. The covariance of the model parameters expected to be obtained is investigated with variations in the number and location of sensors. Further, the influence of noise on the optimal location of the sensors is investigated. It is found that the optimal locations of sensors seem to become less sensitive to e.g. the noise-to-signal ratio within increasing number of sensors.

U2 - doi:10.1006/mssp.1994.1045

DO - doi:10.1006/mssp.1994.1045

M3 - Journal article

VL - 8

SP - 639

EP - 647

JO - Mechanical Systems and Signal Processing

JF - Mechanical Systems and Signal Processing

SN - 0888-3270

IS - 4

ER -