## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Work** & **Classification Method** & **Accuracy** & **Detected Faults** & **Advantages** & **Disadvantages** \\ \hline
[17] & ANN & 96\% & \begin{tabular}{c} Broken rotor bar \\ Rolling bearing \\ Misalignment \\ \end{tabular} & \
\begin{tabular}{c} The method has an acceptable \\ accuracy for fault classification in \\ the induction motor. \\ \end{tabular} &
\begin{tabular}{c} The methodology is complex \\ because there are several tasks that \\ need to be performed before \\ classification. It focuses only on the \\ analysis of the induction motor. \\ \end{tabular} \\ \hline
[18] & \begin{tabular}{c} Self-Organizing Maps \\ (SOMs) for fault \\ detection \\ \end{tabular} & 94.8\% & \begin{tabular}{c} Bearing defect \\ Unbalance \\ Misalignment \\ \end{tabular} & \
\begin{tabular}{c} The accuracy is lower than most \\ of the methods reported in the \\ literature. However, the method \\ has an acceptable accuracy for \\ fault classification. \\ \end{tabular} &
\begin{tabular}{c} The accuracy is lower than most \\ the methodologies reported in the \\ literature, and they performed the \\ study only on the induction motor. \\ \end{tabular} \\ \hline
[19] & \begin{tabular}{c} 2-D convolutional \\ neural network (CNN) \\ for feature extraction \\ and classification. \\ \end{tabular} & 100\% & Bearing & \
\begin{tabular}{c} The methodology is completely \\ accurate. \\ \end{tabular} &
\begin{tabular}{c} The methodology is only \\ responsible for the study of \\ induction motor bearings. It does \\ not study other failures or parts \\ found in the induction motor. \\ \end{tabular} \\ \hline
[20] & \begin{tabular}{c} SoftMax regression \\ classifier for fault \\ pattern identification. \\ \end{tabular} & 98\% & \begin{tabular}{c} Different bearing defects \\ Rotor \\ unbalance \\ \end{tabular} & \begin{tabular}{c} The method has an acceptable \\ accuracy for fault classification in \\ the induction motor. \\ \end{tabular} & \begin{tabular}{c} The methodology only proposes \\ three different case studies in \\ induction motor; it does not \\ consider the effects that they can \\ have on the kinematic chain, which \\ can cause other failures in the \\ elements that make up the chain. \\ \end{tabular} \\ \hline Proposed work & \begin{tabular}{c} CNN is used for both \\ image segmentation and \\ classification. \\ \end{tabular} & 98.5\% & \begin{tabular}{c} Different bearing defects \\ Rotor \\ unbalance \\ \end{tabular} & \
\begin{tabular}{c} The method has an acceptable \\ accuracy for fault classification in \\ the induction motor. \\ \end{tabular} &
\begin{tabular}{c} The methodology only proposes \\ three different case studies in \\ induction motor; it does not \\ consider the effects that they can \\ have on the kinematic chain, which \\ can cause other failures in the \\ system. Another advantage of the \\ proposed methodology is that, in \\ order to test the robustness of the \\ classification algorithm, noisy \\ images have been modified, \\ obtaining good accuracy results. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 7: IRT-based methods for the automatic identification of multi-faults in electromechanical systems for qualitative comparison. When comparing the precision reported in the methodologies proposed in the literature with respect to the proposal made in this work, it can be observed that an accuracy of 100% is reported [19]. At first glance, one might think that the multi-fault detection problem in electromechanical systems is practically solved. However, there are some limitations to consider. As it can be a small data set, it can result in misleading precision [41]. In this sense, the size of the data set used in all the methodologies presented in Table 4 did not exceed 400 thermographic images, while in this work, to avoid a similar problem, a total of 4800 thermographic images were analyzed.