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KOMA is a package for operational modal analysis, with core functionality available both in Python and MATLAB languages. For additional details about the implementation of the covariance-driven stochastic subspace identification algorithm please refer to [1]. Data-SSI and FDD are implemented in the MATLAB version of KOMA. For automatic OMA and clustering analysis, please refer to [2]. More information and functionality will be added after publication of the cited paper.

Please refer to the documentation for more details.

Either download the repository to your computer and install, e.g. by pip

pip install .

or install directly from the python package index.

pip install git+https://www.github.com/knutankv/koma.git@master

Thereafter, import the package modules, exemplified for the `oma´ module, as follows:

import koma.oma

Download or clone repository. Folder containing package root has to be in the MATLAB path:

addpath('C:/Users/knutankv/git-repos/koma/');

Ideally this is done permanently, such that it is always accessible. Then, the package can be imported with the following command:

import koma.*

Now all the subroutines of the package are accessible through

koma.function_name

E.g., to use the function covssi.m located at .../+koma/+oma/ the following syntax is applied:

[lambda,phi,order] = koma.oma.covssi(data, fs, i, 'order', order);

Functions inside private folders are accessible only from functions inside the folder at the root of the private folder.

Please cite both the original research paper for which the code was developed for [1] and the code if used in research.

Please open an issue for support.

Jos van der Geest contribution to Mathworks Central File Exchange is used to produce error bars in stability plots of MATLAB function stabplot.m.

[1] Knut Andreas Kvåle, Ole Øiseth, and Anders Rønnquist. Operational modal analysis of an end-supported pontoon bridge. Engineering Structures, 148:410–423, oct 2017. URL: http://www.sciencedirect.com/science/article/pii/S0141029616307805, doi:10.1016/j.engstruct.2017.06.069.

[2] K.A. Kvåle and Ole Øiseth. Automated operational modal analysis of an end-supported pontoon bridge using covariance-driven stochastic subspace identification and a density-based hierarchical clustering algorithm. IABMAS Conference, 2020.