KOMA is a package for operational modal analysis in Python. For additional details about the implementation of the covariance-driven stochastic subspace identification algorithm please refer to [5]. For automatic OMA and clustering analysis, please refer to [6]. More information and functionality will be added after publication of the cited paper.
Either download the repository to your computer and install, e.g. by pip
pip install .
or install directly from github:
pip install git+https://www.github.com/knutankv/koma.git@master
Import the relevant package modules, exemplified for the oma module, as follows:
from koma import omaFor details, please refer to the examples. For code reference visit knutankv.github.io/koma.
Examples are provided as Jupyter Notebooks in the examples folder.
[1] L HERMANS and H VAN DER AUWERAER. MODAL TESTING AND ANALYSIS OF STRUCTURES UNDER OPERATIONAL CONDITIONS: INDUSTRIAL APPLICATIONS. Mechanical Systems and Signal Processing, 13(2):193–216, mar 1999. URL: http://www.sciencedirect.com/science/article/pii/S0888327098912110, doi:http://dx.doi.org/10.1006/mssp.1998.1211.
[2] Peter Van Overschee and Bart De Moor. Subspace identification for linear systems: theory, implementation, applications. Kluwer Academic Publishers, Boston/London/Dordrecht, 1996.
[3] Carlo Rainieri and Giovanni Fabbrocino. Operational Modal Analysis of Civil Engineering Structures. Springer, New York, 2014.
[4] Brad A. Pridham and John C. Wilson. A study of damping errors in correlation-driven stochastic realizations using short data sets. Probabilistic Engineering Mechanics, 18(1):61–77, jan 2003. URL: http://www.sciencedirect.com/science/article/pii/S0266892002000425, doi:10.1016/S0266-8920(02)00042-5.
[5] 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.
[6] 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.
Zenodo research entry:
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