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Highlights • Comparison and application of data-driven methods for operational modal analysis. • Damping ratios extraction using Hilbert Transform and Random Decrement Technique. • Proposed methods are validated by a discrete system and a continuous steel plate. • Dynamic parameters (modal shapes, frequencies, damping ratios) are identified. • Present the results of research from the field of signal theory and engineering. Abstract The operational modal parameters identification under ambient excitation has been widely applied in structural dynamics analysis, where the input force is difficult to measure. This paper makes comprehensive and systematic comparisons of four statistical learning algorithms (PCA, ICA, SOBI, LLE) on analyzing their performance for resolving operational modal parameters identification. It mainly focuses on the comparison and evaluation of the four data-driven methods based for operational modal analysis and explores the use of Hilbert Transform and Random Decrement Technique for the identification of modal damping ratios. The further tests, the robustness of four algorithms, are conducted for investigating the influences of external noise to the performance of the algorithms. On the basis of the modal expansion theory, the vibration response signals could be decomposed into the inner product of modal shapes matrix and modal responses vector in the modal coordinate, from which the modal shapes, modal natural frequencies and modal damping ratios can be well estimated. Hence, a one-to-one mapping between the mathematical model of four statistical learning algorithms and physical model of dynamic systems is established. To validate the effectiveness of the proposed methods, performance and comparison of the proposed methods are studied using a discrete three degree-of-freedom (DOF) system and continuous cantilever steel plate. The simulation and experimental results show that, for the same experimental data, the SOBI algorithm has better performance than other algorithms, and it is more suitable for operational modal identification. Finally, the characteristics of four algorithms and further directions are concluded and discussed in this paper. [ABSTRACT FROM AUTHOR]

Source number estimation is the premise for applying blind source separation algorithms, and it's a technical difficulty. The influence of source number estimation on application of blind source separation algorithms is researched in present paper. Results show that, in the condition of over-determined and positive-determined, if the estimated source number is more than practical source number, then the separated signals would be more than practical source signals, usually all of the source signals can be separated, and the part of separated signals more than practical source signals would be the practical source signals’ copy. When the estimated source number is less than that of practical source signals, the separated source signals would be part of the practical source signals. In the condition of under-determined, when estimated source number is more than practical source number, all columns of the practical mixing matrix can be estimated, and the other estimated columns are the practical columns’ copy or redundancy, all of the practical source signals can be separated, and the other part are the practical source signals’ copy or redundancy. If the estimated source number is less than that of practical source signals, part columns of the practical mixed matrix can be estimated, and part of the practical source signals can be recovered. [ABSTRACT FROM AUTHOR]