## 6 Conclusions  
This research contributes to the detection of common defects that affect bearings, OR damage and lubricant contamination, with the development of a methodology for measuring and processing vibration and current signals using time domain features and data fusion. The sensors and acquisition system allowed the correct measurement of the physical magnitudes, capturing low-noise signals processed directly with low conditioning. The results show the advantage of data fusion from different sensors for diagnosing combined faults, as individual indicators present difficulties for the correct separation of the classes. Similarly, the fusion and projection of the data using LDA allowed the classes to be compact and well distributed in a 3D space for better diagnosis. The linear SVM classifier proposed in the methodology presents a low efficiency when working with current signals, achieving 69.9%; for the fusion of the vibration signals measured with a three-axis accelerometer, the results increased to 89.3% efficiency, while when fusing the four signals (x-, y-, and \(z\)-axis vibration and one-phase current signals), the SVM reached 97.1% efficiency, being able to discriminate correctly across the five conditions under study, resulting in a system capable of diagnosing the presence of contaminants in the lubricating grease and three different levels of OR damage in bearings used in a VFD-fed induction motor. These results are contrasted with other works in the literature, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Proposal** & **Classification Model** & **Faults** &
\begin{tabular}{c} **Number of** \\ **Severities** \\ \end{tabular} & **Signals** & **Accuracy** \\ \hline
[8] & CNN & OR, IR, ball, cage, lack of & 6 & Thermal images & 99.8\% \\
[12] & CNN & OR and IR & 3 & Vibration & 93.97\% \\
[13] & RFA & OR, IR, ball & 3 & Vibration & 99.57\% \\  & Multi-source domain & & & & \\
[17] & information fusion & OR, IR, ball & 13 & Vibration & 98.31\% \\  & network (MDIFN) & & & & \\
[20] & ANN & OR, IR, ball, cage & 16 & Vibration & 91.6\% \\
[23] & SVM and CNN & Contamination & 3 & Vibration & 100\% and 98.33\% \\ Proposed & Linear SVM & OR and Contamination & 5 & Vibration and current & 97.1\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the proposed methodology to proposals reported in the literature. presenting an approach that considers the presence of contamination in the bearings and proposing a different classification model, achieving results on par with those studies. Improvements can be made to the methodology to increase the performance and capability to detect the defects under study. These prospective improvements include the use of other noninvasive sensors for the measurement of other physical magnitudes, such as stray magnetic flux, the testing of different locations of the sensors to study the effects on the methodology, analysis of more conditions, including different contaminant concentrations, the introduction of individual OR damages, variations of the motor speed or the frequency of the VFD and load levels to generate a more extensive database, as well as the selection of the most significant features to improve class separation, and the implementation of different kernels for the SVM and other classifiers, such as ANNs or k-NN. Conceptualization, R.A.O.-R. and J.A.A.-D.; methodology, G.D.-S. and R.A.O.-R.; software, G.D.-S. and J.C.-O. ; validation, J.C.-O., L.D., and L.S. ; investigation, L.D. and L.S. ; data curation, J.A.A.-D.; writing--original draft preparation, I.Z.-R.; writing--review and editing, G.D.-S., I.Z.-R., and R.A.O.-R., visualization, J.C.-O., J.A.A.-D., R.A.O.-R., and L.S. ; supervision, I.Z.-R., R.A.O.-R., and J.A.A.-D.; project administration, R.A.O.-R., J.A.A.-D., and L.D. ; funding acquisition, J.A.A.-D. and L.D. All authors have read and agreed to the published version of the manuscript.