Towards an Automatic Modal Parameter Estimation Framework: Mode Clustering
Paper i proceeding, 2015

The estimation of modal parameters from a set of measured data is a highly judgmental task, with user expertise playing a significant role for distinguishing between physical and spurious modes. However, it can be very tedious especially in situations when the data is difficult to analyze. This study presents a new algorithm for mode clustering as a preliminary step in a multi-step algorithm for performing physical mode selection with little or no user interaction. The algorithm commences by identification of a high-order model from estimated frequency response functions to collect all the important characteristics of the structure in a so-called library of modes. This often results in the presence of spurious modes which can be detected on the basis of the hypothesis that spurious modes are estimated with a higher level of uncertainty comparing to physical modes. Therefore, we construct a series of data using a simple random sampling technique in order to obtain a set of linear systems using subspace identification. Then, their similar modes are grouped together using a new correlation criterion, which is called Modal Observability Correlation (MOC). An illustrative example shows the efficiency of the proposed clustering technique and also demonstrates its capability to dealing with inconsistent data.

Inconsistent datam

QR- and singular value decomposition


FRF based N4SID

Modal parameters

Modal observability correlation


Majid Khorsand Vakilzadeh

Svenskt VindkraftsTekniskt Centrum (SWPTC)

Chalmers, Tillämpad mekanik, Dynamik

Vahid Yaghoubi Nasrabadi

Chalmers, Tillämpad mekanik, Dynamik

Anders Johansson

Chalmers, Tillämpad mekanik, Dynamik

Thomas Abrahamsson

Chalmers, Tillämpad mekanik, Dynamik

Proceedings of the 33rd IMAC, A Conference and Exposition on Structural Dynamics, 2015; (Topics in Modal Analysis, Volume 10)

2191-5652 (eISSN)