Next Article in Journal
Genetic Testing for Inherited Thrombophilia: 20 Years of Experience in a University and Tertiary Care Centre
Previous Article in Journal
Damage Characterisation for Cement and Concrete Using Microwave Induced Damage
Article Menu
Issue 8 (ICEM 2018) cover image

Export Article

Open AccessProceedings
Proceedings 2018, 2(8), 519;

Efficient Use of the Output Information to Improve Modal Parameter Estimation

Mechanical Engineering Department, Vrije Universiteit Brussel, Acoustics and Vibration Research Group, B-1050 Brussels, Belgium
Mechanical Design Engineering Department, Helwan University, Cairo 11790, Egypt
Presented at the 18th International Conference on Experimental Mechanics (ICEM18), Brussels, Belgium, 1–5 July 2018.
Author to whom correspondence should be addressed.
Published: 26 June 2018
PDF [417 KB, uploaded 19 July 2018]


In modal identification, the value of the model parameters and the associated uncertainty depends on the quality of the measurements. The maximum likelihood estimator (mle) is a consistent and efficient estimator. This means that the value of the parameters trends asymptotically close to the true value, while the variance of such parameters is the lowest possible with the associated data. The mle implementation and application can be complex and generally need strong computational requirements. In applications where the number of inputs and outputs are elevated (as in modal analysis) is common to reduce the covariance matrix to a diagonal one where only the variances are considered. This implementation is still consistent but not efficient. However, it generates acceptable results. The current work shows that using efficiently the output information as complement to the input–output relations, it is possible to improve the model identification reaching similar levels than the mle, while reducing the execution time and the computational load.
Keywords: modal parameters estimation; maximum likelihood estimator; output–output relations modal parameters estimation; maximum likelihood estimator; output–output relations
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Olarte, O.; El-Kafafy, M.; Guillaume, P. Efficient Use of the Output Information to Improve Modal Parameter Estimation. Proceedings 2018, 2, 519.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Article Access Statistics



[Return to top]
Proceedings EISSN 2504-3900 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top