Blind Modal Identification (BMID) toolbox

Toolbox for performing modal identification using second order blind source separation methods.

Description

Developed by Scot McNeill at the Univerisity of Houston for Ph.D. work under David Zimmerman.

This version uses the analytic signal of measured data as in the first reference below. The pairing step is eliminated. The two-step JAD algorithm (whitening and rotation) is employed.

This version essentially applies second order blind identification (SOBI) on an analytic signal of the vibration response data to estimate complex modes and modal responses.

To get started, run the example bmid_3dof_rand.m in the rand3dof folder or
bmid_frame1.m in the frame folder.
Control systems toolbox is required to run the 3 DOF simulation, else
a data file generated from the simulation is loaded.

Any works published whereby the BMID method or any of the tools in this toolbox were employed,
the following references must be cited.

S.I. McNeill, An Analytic Formulation for Blind Modal Identification. Accepted paper in
the Journal of Vibration and Control.

S.I. McNeill, D.C. Zimmerman, A framework for blind modal identification using joint
approximate diagonalization. Mechanical Systems and Signal Processing 22(7), 1526-1548, 2008.

S. McNeill, Modal identification using blind source separation techniques, PhD Dissertation,
The Department Mechanical Engineering, University of Houston, Houston, Texas, 2007.

• MATLAB release: MATLAB 7.4 (R2007a)