## 5 Conclusions and Future Work  
A novel robust health prognostics technique that identifies outer race bearing failures and predicts the remaining useful lifetime of the bearings of electric motors was designed, implemented, trained, and tested. HHT for data preprocessing, frequency-domain analysis using FFT for feature extraction, stacked autoencoders (SAEs) for feature denoising and feature fusion, and bidirectional long short-term memory (BiLSTM) for RUL prediction were used to build the prediction model.  
The robustness of the proposed model was demonstrated while predicting the RUL of bearings with different motors and motor operating conditions (shaft frequency, load, type of bearing), without retraining or fine-tuning the model. The most popular datasets for RUL prediction in electric motors have been used. IMS dataset no. 2 was used for training while IMS dataset no. 3, FEMTO, and XJTU-SY bearing datasets were used for testing the developed model, validating it with a total of four different motor operating conditions. The results obtained with the FEMTO and XJTU-SY bearing datasets are more accurate than  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & & & \multicolumn{6}{c}{**Prediction Error (\%)**} \\ \hline
**Testing Dataset** & \(P_{\text{T}}\) & \(R_{A}\) & \(R_{\text{P}}\) & **Proposed Model** & [33] & [56] & [57] & [58] & [59] & [60] \\ \hline IMS dataset no. 3 & 6174 & 6315 & 6314 & 0.71 & - & - & - & - & - \\ FEMTO bearing 1\_3 & 2200 & 2376 & 2374 & 0.91 & 43.28 & 2.58 & 1.05 & \(-\)0.69 & \(-\)2.62 & - \\ FEMTO bearing 1\_4 & 1247 & 1428 & 1427 & 1.75 & 67.55 & \(-\)9.14 & 20.35 & 3.10 & 17.40 & - \\ FEMTO bearing 1\_7 & 2030 & 2260 & 2254 & 2.08 & 17.83 & \(-\)0.70 & 29.19 & 7.00 & 1.06 & - \\ XJTU-SY bearing 2\_5 & 265 & 338 & 338 & 1.52 & - & - & - & - & 8.95 \\ XJTU-SY bearing 3\_1 & 2433 & 2527 & 2525 & 1.06 & - & - & - & - & 10.42 \\ \hline \hline \end{tabular}
\end{table}
Table 4: RUL results in comparison with other works.  
Figure 8: Inner race failure RUL prediction. (**a**) Proposed model. (**b**) Presented model at [58], reprinted with permission from Ref. [58], Copyright 2021, Elsevier.  
those of previous works trained and tested with the same dataset, without demonstrating the robustness of their models to changes in motor or motor conditions. Its robustness, high accuracy, and fast prediction make the proposed health prognostics technique suitable for predicting RUL in electric motors working under different conditions.  
Future work will be geared toward developing new health prognostic techniques that predict RUL and identify other types of bearing failures, such as inner race or ball-bearing failures. The ultimate objective was geared toward prescriptive maintenance, combining all of these health prognostics techniques in order to predict RUL, identify different kinds of failures, and provide information about the extent to which they contribute to the bearing degradation.  
Conceptualization, L.M. and J.C.G.; methodology, L.M.; software, L.M.; validation, L.M. and J.C.G.; formal analysis, L.M.; investigation, L.M., F.J.S., J.C.G., F.J.d. and D.F.G.; resources, F.J.S., J.C.G. and F.J.d.; data curation, J.C.G.; writing--original draft preparation, L.M.; writing--review and editing, L.M., F.J.S., J.C.G., and F.J.d.; visualization, L.M., F.J.S. and J.C.G.; supervision, F.J.S., J.C.G., F.J.d. and D.F.G.; project administration, F.J.S.; funding acquisition, F.J.S. and D.F.G. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by the Spanish National Plan of Research, Development, and Innovation under project OCAS (RTI2018-094849-B-100), EDNA (PID2021-124383 OB-100), and the University of Oviedo.  
Not applicable.  
The authors declare no conflict of interest.