### Literature Survey  
Identification of bearing coefficients in a rotor-bearing system is an old problem. Some scholars have investigated theoretical model-based methods to obtain the bearing stiffness and damping coefficients. [PERSON] [3] tried to predict dynamic coefficients of sliding bearings based on the Reynolds equation and Computational Fluid Dynamics. [PERSON] [4] calculated stiffness and damping coefficients of journal bearing using a 3D transient flow calculation. [PERSON] [5] obtained bearing coefficients based on the approximate analytical solutions of the Reynolds equation. [PERSON] [6] evaluated the dynamic coefficients of finite length journal bearing using a regular perturbation method. However, the simplification in modeling inevitably leads to errors between the calculated and actual values [7]. Therefore, experimental identification methods were developed. They can be categorized according to the applied load (static load and dynamic force, i.e., using exciter, impulse, or unbalanced force) [8; 9; 10]. The static load approach is sensitive to measuring errors, and the methods using an exciter or an impact hammer are not easy to carry out and are time-consuming. From the perspective of practicality, it is easier to apply an unbalanced force than an excitation force because no sophisticated device is required. [PERSON] and [PERSON] [11] measured for the first time the journal-bearing coefficients by using an unbalanced force; however, they ignored the cross-coupled stiffness and damping coefficients. [PERSON] and [PERSON] [12] used the aforementioned method and proposed an iterative procedure to calculate coefficients including the cross-coupled coefficients of large journal bearings. With the assumption that the excitation frequency does not influence bearing coefficients. [PERSON] [13] developed an estimation algorithm using the unbalance responses from bearing housings in the horizontal and vertical directions with the assumption that bearing coefficients are speed dependent. At least two run-downs with different unbalance configurations are obtained. The method has considerable potential because the synchronous responses of rotating machines from a machine run-down/run-up are not difficult to obtain. Additionally, optimization techniques were investigated. The Bayesian inference optimization technique combined with the imbalance excitation methods was proposed to improve the identification accuracy [14]. However, this method may involve an ill-conditioned matrix problem. To avoid the problem, [PERSON] [15] proposed four complementary equations that are uncorrelated with the dynamic equations from unbalance responses. The least-squares method is combined with vibration theory by [PERSON] [16] for estimating the dynamic characteristics of journal bearings. Some scholars tried to estimate bearing coefficients and rotor unbalance simultaneously [17]. [PERSON] and [PERSON] applied different frequency excitations to estimate rotor unbalance as well as bearing coefficients [18]. Based on the method proposed by [PERSON] [19], [PERSON] [20] conducted an experiment that applied impulse excitation on a double-disc and single-span rotor to identify rotor unbalance as well as stiffness and damping coefficients. [PERSON] [21] developed an algorithm to simultaneously estimate rotor unbalance, and four stiffness coefficients and four damping coefficients of bearings in a multi-degree-of-freedom (MDOF) flexible rotor using impulse responses transformed as frequencies. The rotor is modeled as a Timoshenko beam with gyroscopic effects using the finite element method. Standard condensation is utilized to reduce the model's degree of freedom. The algorithm can incorporate any type and any number of bearings. [PERSON] [22] subsequently formulated another algorithm for the simultaneous estimation method using unbalance responses from three different unbalance configurations for both clockwise and anti-clockwise rotations.