###### Abstract
The major idea of this method was to detect and remove periodic discrete frequency components. The main difficulty in OMA is to handle the periodic discrete frequency components, i.e., modulation side bands and harmonics. In some cases, the harmonics and side bands can be mistaken for slightly damped modes. In this case, a notch liftering technique was used, combined with an exponential lifter to remove/reduce the aforementioned difficulty. There is an interesting and well-developed method for OMA's applications, with locally preserving projections (LPP) and principal components analysis (PCA) methods, respectively. [PERSON] et al. in 2019 [15] developed a new online operational method, based on vibration analysis, for the control of linear-time varying structures. The main idea was to overcome the limitations of resonance uncertainties of the OMA analysis by combining the idea of \"forgetting factor weighting\", locally preserving projections (LPP), and eigenvector recursive PCA. The authors claimed that the methodology works faster, requires less memory space, and archives higher identification accuracy. In [PERSON] et al. 's 2021 paper [16], further findings were developed. In [15], the method based on moving windows and locally preserving projection algorithms was proposed, in order to successfully identify the modal parameters with the OMA method. [PERSON] et at. (2021) [17] carried out an extensive review of OMA techniques for in-service modal identification. The OMA studied techniques are: peak picking (PP), the basic assumption the modes are well-separated, and the damping is separated; frequency domain decomposition (FDD) can estimate the natural frequencies and closed space modes accurately; time-domain decomposition (TDD)--computationally efficient, but difficult to extract close spaced modal parameters; natural excitation technique (NEXT)--good ground to extend EMA into OMA, but difficulties in data processing; autoregressive moving average (ARMA)--output measurements can be used directly; the computationally intensive method; stochastic subspace identification (SSI)--high parameters estimation and accuracy; and the mathematically complex method. This paper performs an extensive literature review; however, there are methods that were not included in the analysis, such as the RDT and ITD methods, respectively. We can draw some remarks, related to the literature review. OMA techniques showed a lot of potential, due to the simplicity in performing vibration measurements. It can perform under running conditions. There are no necessary extra requirements for measurement conditions, such as EMA. It can handle big structures, such as bridges, buildings, machines, etc. However, there are still open questions. What kind of algorithm can be used? How is it related the structure complexity of the algorithm election choice? Is there any framework for applying a certain algorithm to a certain type of structure? There is still a lot of research to cover the state-of-the-art of OMA. Therefore, this research paper aims to put two of most popular methods, RDT [1, 4, 11, 18] and EITM [3], into perspective, applying them in steel plate in the early stage of development. The validation process was carried out in a steel beam, induction motor, and gearbox. The windowing technique was also applied, similar to that which was used in paper [15]. However, in this case, a liftering technique was used to eliminate the harmonic effects. Different frequency bands were also used as a pre-processing technique to reduce noise. Due to the simplicity of performing only surface structure vibration measurements, OMA is frequently applied in machine diagnosis and health condition monitoring [1; 2; 7; 19; 20]. However, there is still a lot of research to cover OMA's major drawbacks, i.e., the reduction of harmonic's effects, accurate handling of MIMO systems, and difficulty in estimating close space modes. Therefore, our research approach intends to overcome OMA's difficulties with three of most popular methods: RDT, CFE, and EITM.