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Test Design for Finite Element Model Updating: Identifiable Parameters and Informative Test Data
Chalmers tekniska högskola. (Maskinteknik)ORCID iD: 0000-0002-4404-5708
2003 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

It is important to predict structural phenomena, such as noise and fatigue, stemming from vibra- tions. To do this, reliable structural dynamic models are needed. To be useful the models have to compare well with reality in the validation against test data; if not, the models should be mod- ified. The thesis research is in the field of computational model updating, which is, more often than not, the updating of uncertain parameters of a finite element model to better correlate to test data. This is a specialization that started to grow in the 1970s, and since then much research has been done. The work presented here concerns the design of tests for model updating, which is one of several model updating sub-tasks.

 For a test to be useful for model updating, the test data set must be such that the model param- eters are sufficiently well identifiable. The dynamic properties of a structure to be compared with test data may under certain conditions change similarly when one parameter or a set of other parameters is changed. When this happens, there is lack of identifiability and, before a meaningful model updating can take place, either complementary test data have to be added or a re-parameterization of the model must be made. An index was developed, the Orthogonality- Co-linearity Index (OCI), that helps to find the best way to reduce the number of parameters when there is low identifiability. For the model updating, test data also need to be informative with respect to the parameters to be tuned. The data informativeness depends on the test design, i.e. the choice of stimuli and the placement of the actuators and sensors. A data informativeness index that supports the design of an informative test is proposed. Procedures were also worked out to make the test design robust with respect to parameter uncertainties. The study is limited to linear and time-invariant systems.

Place, publisher, year, edition, pages
Göteborg: Chalmers tekniska högskola , 2003. , 33 p.
Series
Doktorsavhandlingar vid Chalmers tekniska högskola, ISSN 0346-718X
Keyword [en]
Test Design Finite Element Model Updating Informativeness Identifiability optimization Fisher information modal tests
National Category
Applied Mechanics
Research subject
Technology (byts ev till Engineering), Mechanical Engineering
Identifiers
URN: urn:nbn:se:lnu:diva-34602ISBN: 91-7291-393-2 (print)OAI: oai:DiVA.org:lnu-34602DiVA: diva2:721159
Public defence
2004-02-13, Chalmers, Göteborg, 15:20 (English)
Supervisors
Note

Avhandlingen var framlagd vid Chalmers tekniska högskola

Available from: 2014-06-09 Created: 2014-06-03 Last updated: 2015-05-24Bibliographically approved
List of papers
1. Parameter Identifiability in Finite Element Model Error Localization
Open this publication in new window or tab >>Parameter Identifiability in Finite Element Model Error Localization
2003 (English)In: Mechanical systems and signal processing, ISSN 0888-3270, E-ISSN 1096-1216, Vol. 17, no 3, 579-588 p.Article in journal (Refereed) Published
Abstract [en]

A fundamental question in finite element model updating and error localisation is whether sufficient identifiability of model parameters is at hand for a given set of test data. Under certain conditions, the dynamic properties (to be compared with test data) of a structural model, may change similarly when a certain model parameter or a combination of other parameters are modified. Since low confidence in identified parameters can also be expected for marginally identifiable systems, due to the omnipresent noise when real test data are used, one should seek such states so as to avoid them. Should the problem lack identifiability, then before a meaningful error localisation can be made; either complementary test data have to be added or new parameters chosen for the model. The latter is studied in this paper. An index, the orthogonality/colinearity index, was developed to facilitate finding the best way to reduce the number of parameters when there is low identifiability The use of the index is demonstrated on a six-degree-of-freedom system in a numerical example. The example shows that error localisation or model updating using a parameterisation which has insufficient parameter identifiability is pointless.

Place, publisher, year, edition, pages
Elsevier, 2003
Keyword
Parameter Identifiability Finite Element Model Updating
National Category
Mechanical Engineering
Research subject
Technology (byts ev till Engineering), Mechanical Engineering
Identifiers
urn:nbn:se:lnu:diva-34216 (URN)10.1006/mssp.2002.1522 (DOI)
Available from: 2014-05-18 Created: 2014-05-18 Last updated: 2015-05-24Bibliographically approved
2. Optimizing the informativeness of test data used for computational model updating
Open this publication in new window or tab >>Optimizing the informativeness of test data used for computational model updating
2005 (English)In: Mechanical systems and signal processing, ISSN 0888-3270, E-ISSN 1096-1216, Vol. 19, no 4, 736-750 p.Article in journal (Refereed) Published
Abstract [en]

In advance of a computational model updating or an error localisation, it can be advantageous to make a preparatory error localisation using data from a nominal analytical model. The purpose is then to select parameters for quantifying model errors and also to design effective tests for determining the best parameter setting. For successful subsequent error localisation, the test data must be informative with respect to the model parameters chosen when such data become available after test. The demand for test data informativeness puts requirements on the experiment with regard to spatial resolution of sensors, bandwidth of excitation, signal-to-noise ratios, etc.

Optimising a test design is a huge task, sometimes impossible in practice, due to its combinatorial nature. The number of possible sensor/actuator placement combinations grows rapidly as the number of sensor and actuator candidates increases. For industrial sized problems, finding a sub-optimal solution may be a more realistic target. Such solutions are sought in this work.

The aim of this study is to quantify data informativeness, shown to relate to the Fisher information matrix, with respect to physical parameters that are used in error localisation and model updating. Deterministic finite-element models in combination with stochastic noise models are used for assessing data informativeness, and a procedure for test design optimisation with respect to this is devised.

Place, publisher, year, edition, pages
Elsevier, 2005
Keyword
model updating, test, informativity
National Category
Mechanical Engineering
Research subject
Technology (byts ev till Engineering), Mechanical Engineering
Identifiers
urn:nbn:se:lnu:diva-34215 (URN)10.1016/j.ymssp.2004.09.003 (DOI)
Available from: 2014-05-18 Created: 2014-05-18 Last updated: 2015-05-24Bibliographically approved
3. Robust Optimal Sensor Placement for Computational Model Updating
Open this publication in new window or tab >>Robust Optimal Sensor Placement for Computational Model Updating
2017 (English)In: Mechanical systems and signal processing, ISSN 0888-3270, E-ISSN 1096-1216Article in journal, Editorial material (Refereed) Submitted
Place, publisher, year, edition, pages
Elsevier, 2017
Keyword
Robust Optimal Sensor Placement Computational Model Updating
National Category
Mechanical Engineering
Research subject
Technology (byts ev till Engineering), Mechanical Engineering
Identifiers
urn:nbn:se:lnu:diva-34218 (URN)
Available from: 2014-05-18 Created: 2014-05-18 Last updated: 2017-09-14
4. Computational Model Updating of a Fan Blade Using Optimal Robust Test Data
Open this publication in new window or tab >>Computational Model Updating of a Fan Blade Using Optimal Robust Test Data
2017 (English)In: Mechanical systems and signal processing, ISSN 0888-3270, E-ISSN 1096-1216Article in journal (Refereed) Submitted
Place, publisher, year, edition, pages
Elsevier, 2017
Keyword
Computational Model Updating Robust
National Category
Mechanical Engineering
Research subject
Technology (byts ev till Engineering), Mechanical Engineering
Identifiers
urn:nbn:se:lnu:diva-34217 (URN)
Available from: 2014-05-18 Created: 2014-05-18 Last updated: 2017-09-14

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