###### Abstract
in 2013 [6] studied the OMA and blind source separation, also known as the blind signal separation method, for the identification of modal parameters. The second-order blind source separation (SOBSS) was applied for the analysis procedure, and the applicability of the framework of SOBSS in OMA was established. It was also established that the theoretical connection of SOBSS and stochastic subspace identification (SSI) stays as one of the aims of reference in OMA. The OMA's research took a new turn with the application of maximum likelihood estimation (MLE), as data preprocessing by estimating modal parameters by [PERSON] et al. in 2012 [7]. The experimental modal analysis is an iterative method to find maximum likelihood estimation (MLE), which can handle, in this case, the state space model's parameters. However, the above method has two drawbacks: (a) slow convergence and (b) high dependence on the initial conditions. To solve the difficulties, a stochastic subspace and the initial conditions with random points were used. The research work performed by [PERSON] et al. in 2014 [8] presented the maximum likelihood estimator (MLE) for its ability to identify the structural modal time-frequency domain parameters. The obtained results were based on two time-frequency functions: the bivariate orthogonal and bivariate power polynomials. The eigensystem realization algorithm (ERA) is used to identify dynamical structure parameters, which is commonly used with natural excitation technique (NEXT) to identify modal parameters from ambient vibration. [PERSON] et al. in 2014 [9] applied the ERA, which is one of the most popular methods in civil engineering for dynamical structural identification parameters. These papers focused on spurious mode, mode energy estimating, and analysis of the stabilization diagram. A new criterion was proposed, the modal similarity index (MSI), to measure the reliability of the modes. The mode energy content was used to define the dominant mode. The finite element method (FEM) was used by [PERSON] et al. in 2013 [10], in order to determine the modal scaling in OMA, who introduced the modal scaling in OMA using the mass matrix of a finite element model (FEM). The developed algorithms were validated by numerical simulations on a planar bridge and cantilever beam. [PERSON] et al. [11] in 2015, applied a non-overlapped (RDT) for parameters identification with OMA. There was a drawback using RDT--due to averaging the raw data time segments, triggering the signal at the initial points of segmentation causes an overlap during triggering, which causes a residual excitation peak at the natural frequency. To solve the above difficulty, the following paper presented a non-overlapping technique to eliminate the peaks. [PERSON] et al. in 2016 [12] studied a paper with the title, \"Repressing the effects of variable speed harmonic orders in OMA\". This paper illustrated the machine shaft orders effects, which can disturb the OMA. To reduce difficulties, they studied three alternatives: (a) applied the time synchronous averaging (TSA), (b) the signals were transformed to order domain and applied a cepstral notch method to reduce speed harmonics and transformed it back to time-domain for OMA, (c) applied the raw vibration signal of an exponential short pass litter to enhance the modal information. [PERSON] et al. in 2017 [13] performed a research study to extract modal parameters and compared them using various OMA methods, such as the frequency decomposition domain (FDD), enhanced frequency decomposition domain (EFDD), and stochastic subspace identification (SSI). A commercial software, PULSETM, was also used for validation proposes. The measured object was a four-stage centrifugal compressor. The identification of the of shaft harmonics were based on the values of the enhanced kurtosis analysis, However, the harmonics eliminations were not clear. An extensive survey of cepstrum's applications for structural dynamical identification parameters was presented by [PERSON] et al. in 2019 [14].