## 3 Results and Discussion

Table 10: AUC multiclass by accelerometer, failure and classifier. Figure 7: Comparison Bhattacharyya distance by CIs and severity. (**a**) Breaking. (**b**) Cracking. (**c**) Pitting. (**d**) Scuffing. and AUC values exceed 98%, as detailed in Table 9 and Table 10, respectively, when using only 7 CIs for all failure types, accelerometers and in the two classification models, thus reducing the dimensionality of the DB and increasing the efficiency in the classification process and reduce the computing time involved in processing the information. The third objective of this work was to determine whether sensor position and inclination influence the extraction of vibration signal features for a classification model. To meet this objective, factorial ANOVA and Tukey's post-hoc comparisons were conducted to assess differences in classification accuracy. The factors considered in the analysis were accelerometer position, failure, and the classification model employed. The accelerometers A1, A4, A5 and A6 results were analysed to determine the influence of the sensor position. The ANOVA results indicated statistically significant differences (_p_-value < 0.001) in the average classification accuracy across the four accelerometers and for the four types of failure. In contrast, no significant difference was observed between the classification models (_p_-value = 1), while a significant interaction (_p_-value < 0.001) was found between the accelerometer:classifier (Figure 8a), failure:classifier (Figure 8b), and accelerometer:failure (Figure 8c) factors. A summary of the ANOVA results is presented in Table 11.  
Regarding the influence of sensor inclination, the results obtained for accelerometers A1, A2 and A3 were analysed. The ANOVA test revealed statistically significant differences (_p_-value < 0.001) in the average classification accuracy across the three accelerometers, the four types of failure, and the two classification models. In addition, significant interaction effects (_p_-value < 0.001) were observed between the accelerometer:classifier (Figure 9a), \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Factor** & **Df** & **Sum Square** & **Mean Square** & **F-Value** & _p_-Value** \\ \hline A & 3 & 0.2150 & 0.07168 & 1295.10 & \(<\)0.001 \\ Failure & 3 & 0.1954 & 0.06512 & 1176.70 & \(<\)0.001 \\ Classifier & 1 & 0.0000 & 0.0000 & 0.00 & \(<\)0.001 \\ A.Failure & 9 & 0.2012 & 0.02236 & 040.00 & \(<\)0.001 \\ Failure:Classifier & 3 & 0.0762 & 0.02539 & 458.70 & \(<\)0.001 \\ A:Classifier & 3 & 0.0184 & 0.00614 & 111.00 & \(<\)0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Anova factorial test for position. Figure 8: Interaction factors for position. (**a**) Accelerometer:Classifier. (**b**) Failure:Classifier. (**c**) Accelerometer:Failure. failure:classifier (Figure 9b), and accelerometer:failure (Figure 9c) factors. A summary of the ANOVA results is presented in Table 12.  
Figures 10 and 11 illustrate the results of [PERSON]'s post-hoc analyses for sensor position and inclination, respectively, highlighting the significant differences and interaction effects among the evaluated factors.As can be seen in these graphs, the differences between pairs of factor levels are practically all significant except in some cases. However, these differences are of little practical relevance as they are less than 1%, as detailed in Table 9 for the four failure types. This consideration is particularly relevant in real-world applications, where surrounding components and system attachments often limit the physical space available for sensor placement on gearboxes. In [PERSON] et al. (2018) and [PERSON] et al. (2019), a sensor's optimum position in a gearbox when moving it along the \(x\), \(y\) and \(z\) axes are studied and determined to extract the best information from the vibration signal. This position, although optimal, is often not accessible due to the physical layout or mounting of the gearbox.