###### Abstract
\begin{table}
\begin{tabular}{l l l} \hline
**Reference** & **Scope and Main Findings** & **Limitations** \\ \hline
[8] & Application of support vector machine (SVM) for faults identification and quantification using FRA signatures. Results show the & Lack of training data and inability to identify fault location. \\  & effectiveness of using SVM to analyze FRA signatures. & \\ \hline
[9] & Application of digital imaging to analyze FRA signature. Results & Inability to identify the extent and \\  & reveal the ability of this method to detect several faults such as disc & location of the winding deformation. \\  & space variation and radial deformation. & \\ \hline
[10] & Using transformer high-frequency equivalent circuits to understand & \\  & FRA signatures. Results show that the physical meaning of circuit & Lack of experimental verifications. \\  & parameters can help understand the impact of several faults on the & \\  & FRA signatures. & \\ \hline
[11] & The simulation FRA results and the measurements FRA on actual & Only one experiment on one 3 MVA \\  & windings were found very promising and encouraging to appraise & transformer unit was employed in the \\  & the feasibility of the proposed measurement FRA method. & research. \\ \hline
[12] & FRA method and intelligence classifiers can be used to detect the & Used parameters are based on estimation \\  & transformer fault with a high level of accuracy. & methods. \\ \hline
[13] & Estimation techniques in the study can be used for identifying and & Simulation analysis was performed but \\  & quantifying the FRA signatures in power transformers for fault & was not verify practical measurement \\  & analysis. & results. \\ \hline \end{tabular}
\end{table} 
Table 3: Research published on the application of FRA on power transformers. FRA method displays a graphical signature of the electrical machine transfer function in a typical frequency range of 10 Hz to 2 MHz [19; 20; 21]. Changes occurring within the machine due to faults or insulation degradation are reflected on the FRA signature [22; 23]. However, the main drawback of using FRA is that there is no widely approved standard or code to interpret the results, especially for rotating machines. Therefore, the FRA interpretation still depends on personal expertise. Some statistical indicators were proposed in the literature to quantify the comparison of FRA signatures [24; 25]. A survey was conducted on the applicability of using various statistical indicators on FRA [26]. Among these indicators, correlation coefficient (CC), mean squared error (MSE), the absolute sum of logarithmic error (ASLE), root mean square error (RMSE), the absolute average difference (DABS), Covariance (COVAR), standard deviation (SD) and maximum-minimum ratio (MM) have shown high sensitivity to changes in the FRA responses. The drawback of using statistical indicators is that there are no approved benchmarks or threshold limits to identify and quantify various faults accurately. From the above discussion, the problem can be stated as: FRA test has been successfully utilized for detecting mechanical deformations within power transformers, but the FRA application has not been widely extended to include other critical assets such as IMs. Furthermore, FRA interpretation for power transformer is still a challenging task due to the lack of standard interpretation codes. Hence, the main contribution of this paper can be summarized below:  
* Presenting an analogical experimental study for the application of FRA on power transformers and IMs in order to understand and explore the wider application of the FRA technique on IMs. * Employing several statistical indicators including CC, SD, ASLE, MSE, RMSE, DABS, COVAR and MM to calculate the variation between normal and faulty FRA signatures. * Identifying threshold boundaries between normal and faulty cases or limits for the used statistical indicators to facilitate an easy and automated FRA interpretation process. The paper is organized as follows.