# Unbalance Detection in Induction Motors through Vibration Signals Using Texture Features
## 2 Methodology
### Comparison  
This section describes the comparison made to the VTA model. First, the model is compared with three classic machine learning models. This comparison aims to choose a model according to a specific need (in this case the accuracy). Finally, the VTA approach is compared with different state-of-the-art models. In this last comparison, the objective is to analyze the cost according to the complexity and computational resources.  
Firstly, the VTA model proposed in this study is compared with three classic machine learning models; logistic regression (LR), K-nearest neighbors (KNN), and a simple perceptron (SP) are used as comparative models. These models were trained using the same training-validation and test sets. Table 2 shows a comparison between the VTA model and classic machine learning classifiers.  
From this Table 2, the accuracy achieved for the VTA approach is superior to the classic models. Our VTA model achieves an accuracy of 99.7% in comparison with the 99.3% achieved by the LR and KNN, and the 99.5% achieved by the SP, respectively. This is because the classic models are based on linear approximation, preventing the capture of the subjacent structure of the data (non-linearity). On the one hand, the time consumption of each model is considerably low, allowing the industry to use these models as a monitoring tool only if time is the most important resource to save. On the other hand, if both accuracy and time are the most important resources to use, the VTA model has the best qualities for the industry.  
Finally, a comparison with different approaches is presented in Table 3. These models are the most popular tools in the state-of-the-art in IM fault detection: Convolutional neural networks [14; 35], Long-short term memory neural networks [15], and artificial neural networks [36]. From this table, we can see that these approaches represent a powerful tool for the classification process. The closest approach is the one proposed by [PERSON] et al. in terms of accuracy with 99.6% in comparison with our 99.7%. As we can see from this table, the number of features required by other approaches is considerably higher in comparison with our VTA. In addition, the dimensionality of each method is considerably higher, which makes it susceptible to overfitting [23]. Additionally, the tuning process is complex due to the number of hyperparameters (hidden layers, filters in convolutions, activation functions, padding, dropout layers, learning rate, regularization factor, etc.). Finally, a comparison with the model presented by [PERSON] et al. is presented. This model is trained according to the specifications established in his paper [14]. Note, the time consumption for training the model is 15 min. Specific hardware may be required to decrease training time. Furthermore,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Healthy** & **Unbalance** \\ \hline healthy & 498 & 2 \\ \hline unbalance & 1 & 499 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification results from the VTA approach.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & \begin{tabular}{c} **Accuracy** \\ **\%** \\ \end{tabular} & \
\begin{tabular}{c} **Tunable** \\ **Parameters** \\ \end{tabular} & **Dimensionality** &
\begin{tabular}{c} **Training Time** \\ **Minutes** \\ \end{tabular} \\ \hline LR & 99.3 & 1 & \(1\times 2\) & 0.36 \\ \hline SP & 99.5 & \(1\) & \(1\times 2\) & 0.31 \\ \hline KNN & 99.3 & \(1\) & \(1\times 2\) & 0.28 \\ \hline VTA model & 99.7 & \(1\) & \(1\times 2\) & 0.55 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classification results using classic machine learning models.  
Data Availability Statement: Data available on request due to restrictions eg privacy or ethical.  
The authors would like to thank the University of Guanajuato for the financial support. In addition, we would like to thank the Mexican CONACyT for the financial support provided.  
The authors declare no conflict of interest.