English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In this paper, the MLMM modal parameter estimation method (Maximum Likelihood estimation of a Modal Model) and its new variant will be introduced. The MLMM method tackles some of the remaining challenges in modal analysis (e.g., modal analysis of highly-damped cases where a large amount of excitation locations is needed such as the modal analysis of a trimmed car body). Another big advantage of the MLMM method is its capability to fully integrate, within the estimated modal model, some important physical constraints, which are required for the intended applications, e.g., realness of the mode shape and FRFs reciprocity. More classical modal parameter estimation methods have rarely the possibility to fully integrate these constraints and the obtained modal parameters are typically altered in a subsequent step to satisfy the desired constraints. It is obvious that this may lead to sub-optimal results. The MLMM method uses the Levenberg-Marquardt optimization scheme to directly fit the modal model to the measured FRFs. The applicability of MLMM to estimate an accurate constrained modal model will be demonstrated using two challenging industrial applications.

A Robust Test-Based Modal Model Identification Method for Challenging Industrial Cases