## 4 Results
### Proposed Method  
As can be observed in the results in Figures 7-9, none of the features calculated from the current and vibration signals could distinguish between all the conditions under study. This led to the fusion of the features using a dimensionality reduction process to obtain sets of features that allowed better separation between the classes.  
Starting with the 450 \(\times\) 19 \(\times\) 5 matrix of current features, a dimensionality reduction was performed to cluster the data according to the conditions and project them in a 3D space for better differentiation. Figure 10 presents the results obtained when analyzing the current signals and Figure 10a shows the projection of the reduced feature matrix with the LDA process and the hyperplanes of the SVM, where it is possible to observe the separation of the HLT class from the faulty conditions; however, the defect-related data are overlapped, making the classification difficult. In the Figure 10b, the confusion matrix for the validation of the SVM model corroborates the complexity of separating the faults correctly when using current signals, as it obtains 66.9% accuracy.  
Figure 9: Boxplots for the use of HFD feature: (**a**) results for the analysis of \(x\)-axis vibrations; (**b**) plot for the vibrations on the \(y\)-axis; (**c**) \(z\)-axis vibrations results; and (**d**) current signal boxplots.  
Similarly, the results of the vibration data analysis are presented in Figure 11. For the vibration signals, the \(450\times 57\times 5\) feature matrix was reduced to a three-dimensional representation with LDA, displayed in Figure 11a, as well as the hyperplanes from the SVM model; it is possible to observe a better separation than with the current data, but the classes are not as compact, leading to some overlap between conditions. Figure 11b corresponds to the confusion matrix for the model validation, achieving 89.3% accuracy, a 20% improvement compared to the current analysis.  
Figure 10: Analysis results for the current data: (**a**) 3D projection of the fused data after the LDA process and the hyperplanes created by the SVM model; (**b**) confusion matrix for the validation of the SVM model, achieving 66.9% accuracy.  
Considering the previous results, the proposed methodology, focused on the fusion of current and vibration signals, allowed a better separation of the classes, as can be observed in Figure 12a, where the hyperplanes generated by the SVM model are also shown; note the separation between the HLT condition and the faults, as they are far apart, as well as some overlapping of the C3 OR and C5 OR classes, which correspond to those with the more significant defects, and more scattered CONT data. In Figure 12b, the confusion matrix for the validation of the model is shown, achieving 97.1% accuracy for the classification of the  
Figure 11: Analysis results for the vibration data: (**a**) 3D projection of the fused data after the LDA process and the hyperplanes created by the SVM model; (**b**) confusion matrix for the validation of the SVM model, achieving 89.3% accuracy.  
five conditions, a notable improvement when compared to the results from the analysis of current and vibration signals individually.