In this paper, the use of the combined deterministic-stochastic subspace identification algorithm for the experimental modal analysis of mechanical structures is discussed. The algorithm requires artificial forces to be applied to the structure, so it can be used for experimental modal analysis (EMA). The algorithm can also be used for operational modal analysis (OMA), since the excitation level of the artificial force(s) can be low compared to the excitation level of the ambient forces. Both the modes that are artificially excited and those that are excited by the ambient forces are identified. This type of operational modal analysis is called an OMAX analysis (Operational Modal Analysis with eXogenous inputs) [1]. The main advantages of OMAX over OMA are that the modes that are excited by the artificial forces can be scaled to unity modal mass and that a higher number of modes can be identified.

Reference-based combined deterministic-stochastic subspace identification for experimental and operational modal analysis

https://hal.inria.fr/hal-00811706/document

Uncertainty quantification for modal parameters from stochastic subspace identification on multi-setup measurements ☆

https://backend.orbit.dtu.dk/ws/files/161882129/bajric_hogsberg_rudinger_2018_postprint.pdf

Evaluation of damping estimates by automated Operational Modal Analysis for offshore wind turbine tower vibrations

Bayesian operational modal analysis of Jiangyin Yangtze River Bridge

Uncertainty quantification in data-driven stochastic subspace identification

https://hal.inria.fr/hal-01328435/document

Variance estimation of modal parameters from output-only and input/output subspace-based system identification

In Operational Modal Analysis (OMA) of large structures it is often needed to process output-only sensor data from multiple non-simultaneously recorded measurement setups, where some reference sensors stay fixed, while the others are moved between the setups. A standard approach to process the data together for global system identification is to transfer the data into frequency domain and merge it there. However, this only works if the unmeasured ambient excitation remains stationary throughout all setups. As the ambient excitation can be different from setup to setup, the amplitude of the measured data can be different as well and the data has to be normalized. Recently, a method has been developed for covarianceand data-driven Stochastic Subspace Identification (SSI) to automatically normalize and merge the data from multiple setups in order to obtain the global modal parameters (natural frequencies, damping ratios, mode shapes), instead of doing the SSI for each setup separately. In this paper, we adapt this approach to multi-setup SSI in frequency domain, where we use spectra data instead of time series data. We demonstrate the advantages of the new merging approach in the frequency domain and apply it to a relevant industrial large scale example, where we compare the estimation results of the modal parameters between the time and frequency domain approaches.

Philippe Mellinger

Papers

Variance estimation of modal parameters from output-only and input/output subspace-based system identification

Data Fusion for Frequency Domain Stochastic Subspace Identification

Uncertainty Quantification for Combined Data-Driven Subspace Identification

Race and Labor in Western Copper