## 5 Conclusions  
The proposed model showed a great capacity to adapt to three types of configurations and three different databases, despite a training set with a smaller number of categories. The \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Work**} & \multirow{2}{*}{**Ref.**} & \
\begin{tabular}{c} **Network** \\ **Structure** \\ **[\# of Layers]** \\ \end{tabular} & \multirow{2}{*}{**Database**} & **Database Size** & **Accuracy** &
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} \\ \cline{3-5}  & & & **Analyses** & **[\# of Signals]** & **[\%]** & **[\%]** \\ \hline
1 & [30] & 7 & CWRU & 20,000 & 100 & – \\ \hline
2 & [31] & 8 & CWRU & 20,000 & 99.77 & 0.66 \\ \hline
3 & [33] & 7 & CWRU & 10,000 & 99.27 & 0.13 \\ \hline
4 & [34] & 6 & CWRU & 177,000 & 99.57 & 0.15 \\ \hline \multirow{4}{*}{5} & \multirow{4}{*}{Proposed model} & \multirow{4}{*}{6} & Case I: CWRU & 3570 & 99.52 & 0.119 \\ \cline{3-5}  & & & Case II: [PERSON] & 4527 & 99.31 & 0.234 \\ \cline{3-5}  & & & Case III: NSF-IMS & 1904 & 99.64 & 0.493 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of the results obtained by the proposed model with other investigations. network still detected faults at early damage stages. Additionally, the low computational cost demonstrate DLNN's suitability for real-time applications in industry. In conclusion, the proposed 1D-CNN showed the following advantages:  
1. Higher accuracy was achieved by increasing the number of training iterations. 2. Regardless of the number classes, the 1D-CNN allowed for differentiation of classes not even included as samples in the training stage. 3. By eliminating the easiest identifiable classes, 1D-CNN yielded the highest accuracy in classifying more diffuse classes. 4. Reduced processing time of around 8 ms per signal, demonstrating the possibility of real-time application. As machinery failure and noise related to rotation speed are generated by vibration, the accurate and prompt detection of vibration signals based on 1D-CNN can lead to an identification of not only machinery failure, but also noise pollution. Additionally, noise pollution significantly alters human mood (e.g., stress, anxiety and loss of attention) and body functioning (e.g., heart and breathing rates, and blinks), which in turn affect work performance and life quality of nearby inhabitants, especially in harmonic frequencies that are not audible [39]. As future work, the developed model has been verified with vibration signals obtained by accelerometers. However, the use of convolutional network can be extended to using acoustic emission signals as system inputs. The proposed network model uses the backpropagation algorithm for training, and the criterion used to stop the process is the number of training cycles (epochs), so it is possible to implement a cross-validation algorithm to increase efficiency by stopping the process when the desired maximum performance is obtained. The processing speed and limited use of computational resources for the operation of the 1D CNN network mean that the applicability of this method is not only limited to the field of bearing fault detection, but can also be implemented in other important fields, such as medicine. The operational characteristics of the developed model meet the necessary requirements for applications that require real-time processing and being implemented in embedded devices, such as Raspberry Pi, Beaglebone, etc. Conceptualization, J.C.-S. and D.I.I.-Z. ; methodology, D.I.I.-Z. ; software, J.C.-S.; validation, L.M.A.-V. and J.C.-S.; formal analysis, D.I.I.-Z. ; investigation, L.M.A.-V.; resources, J.C.-S.; data curation, J.C.-S.; writing--original draft preparation, D.I.I.-Z. ; writing--review and editing, L.M.A.-V.; visualization, J.C.-S.; supervision, L.M.A.-V.; project administration, D.I.I.-Z. ; funding acquisition, D.I.I.-Z. All authors have read and agreed to the published version of the manuscript. This research was funded by Tecnologico de Monterrey. Not applicable.