pyOMA2’s documentation!
This is the new and updated version of pyOMA module, a Python module designed for conducting operational modal analysis. With this update, we’ve transformed pyOMA from a basic collection of functions into a more sophisticated module that fully leverages the capabilities of Python classes.
The module now supports analysis of both single and multi-setup data measurements, which includes handling multiple acquisitions with a mix of reference and roving sensors. We’ve also introduced interactive plots, allowing users to select desired modes for extraction directly from the plots generated by the algorithms. Additionally, a new feature enables users to define the geometry of the structures being tested, facilitating the visualization of mode shapes after modal results are obtained. The underlying functions of these classes have been rigorously revised, resulting in significant enhancements and optimizations. Since version 1.0.1, the uncertainty bounds of the modal properties can also be estimated for the SSI family of algorithms.
We provide four Examples to show the modules capabilities:
Check out the project source.
Important
We have introduced some clustering specialised classes that allow the users to perform Automatic OMA. This update enables users to implement and compare a large number of the most popular algorithms introduced over the last 15 years, all within the same analysis framework. Furthermore, users have the flexibility to mix specific strategies from different algorithms to tailor a specific clustering process to their needs.
Note
Please note that the project is still under active development.
Schematic organisation of the module showing inheritance between classes
Index
References
Rainieri, C., & Fabbrocino, G. (2014). Operational modal analysis of civil engineering structures. Springer, New York, 142, 143.
Brincker, R., & Ventura, C. (2015). Introduction to operational modal analysis. John Wiley & Sons.
Brincker, R., Zhang, L., & Andersen, P. (2001). Modal identification of output-only systems using frequency domain decomposition. Smart Materials and Structures, 10(3), 441.
Brincker, R., Ventura, C. E., & Andersen, P. (2001). Damping estimation by frequency domain decomposition. In Proceedings of IMAC 19: A Conference on Structural Dynamics.
Zhang, L., Wang, T., & Tamura, Y. (2010). A frequency–spatial domain decomposition (FSDD) method for operational modal analysis. Mechanical Systems and Signal Processing, 24(5), 1227-1239.
Zaletelj, K., Bregar, T., Gorjup, D., Slavič, J. (2020) sdypy-pyEMA, 10.5281/zenodo.4016670, https://github.com/sdypy/sdypy
Peeters, B., & De Roeck, G. (1999). Reference-based stochastic subspace identification for output-only modal analysis. Mechanical Systems and Signal Processing, 13(6), 855-878.
Döhler, M. (2011). Subspace-based system identification and fault detection: Algorithms for large systems and application to structural vibration analysis. Diss. Université Rennes 1.
Döhler, M., & Mevel, L. (2013). Efficient multi-order uncertainty computation for stochastic subspace identification. Mechanical Systems and Signal Processing, 38(2), 346-366.
Döhler, M., Lam, X. B., & Mevel, L. (2013). Uncertainty quantification for modal parameters from stochastic subspace identification on multi-setup measurements. Mechanical Systems and Signal Processing, 36(2), 562-581.
Amador, S. D., & Brincker, R. (2021). Robust multi-dataset identification with frequency domain decomposition. Journal of Sound and Vibration, 508, 116207.
Peeters, B., Van der Auweraer, H., Guillaume, P., & Leuridan, J. (2004). The PolyMAX frequency-domain method: a new standard for modal parameter estimation?. Shock and Vibration, 11(3-4), 395-409.