## 10 Future Work  
This study provides a validated framework for predictive maintenance using electrical signals and deep learning, but it does not implement domain adaptation or transfer learning strategies. However, these approaches were discussed and are considered highly relevant for future developments, particularly in the context of scaling the framework across different industrial settings or motor types.  
Future research should explore domain-adaptation techniques to improve the model's ability to handle data from different operating conditions, motor specifications, or industrial environments. By aligning feature distributions between source and target domains, these methods could reduce performance degradation when the framework is applied to new assets or plants without requiring full model retraining.  
Similarly, transfer learning presents a valuable opportunity to reuse knowledge learned from one asset such as a specific motor and apply it to another with minimal labeled data. This is especially relevant given the framework's design, which is modular and generalizable. It could be adapted to various types of rotating machinery, enabling faster deployment in real-world applications with lower data and computational requirements.  
In parallel, by extending the framework to incorporate multi-sensor data, including vibration and temperature would allow for a more comprehensive view of equipment health, potentially capturing fault signatures that are not evident in electrical signals alone. Reinforcement learning also remains a promising path, offering the ability to optimize maintenance policies based on continuous feedback from the environment.  
Finally, these future enhancements aim to evolve the current solution into a fully adaptive, cross-domain, and intelligent maintenance framework that meets the practical challenges of diverse Industry 4.0 ecosystems.  
J.P.C.: conceptualization; methodology; data analysis; software development; writing--original preparation; visualization. J.T.F.: supervision; project administration; writing--review and editing; methodological guidance. M.M.: investigation; validation; review and editing. J.O.E.: industrial implementation support; experimental validation; data; technical supervision. All authors have read and agreed to the final version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data used in this study were obtained from ENGING, an industrial partner specializing in online condition monitoring. These datasets are proprietary and part of ENGING's monitoring platform. As such, access is restricted and not publicly available due to confidentiality and commercial considerations. Researchers interested in collaboration or further information may contact the authors or ENGING directly to explore potential data access under appropriate agreements.  
The authors gratefully acknowledge the technical and infrastructural support provided by Enging--Make Solutions S.A., whose real-world industrial data and monitoring platforms were essential for validating the proposed methodology. The authors also extend their sincere 526 appreciation to the RCM2+ Research Centre for Asset Management and Systems Engineering for its 527 continued support in fostering research excellence in predictive maintenance. Special thanks are due to the engineering teams involved in data acquisition and system integration, whose insights into 529 operational constraints greatly enriched the practical relevance of this work.  
Conflicts of Interest: [PERSON] was employed by the company ENGING. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.