## 6 Discussion and Conclusions
Although a considerable number of physical variables were measured in this study, not all of them demonstrate a significant contribution to the accurate estimation of bearing temperature. This observation suggests the need for future research efforts to focus on systematically identifying and eliminating variables that do not enhance predictive performance, thereby reducing model complexity and optimizing data processing requirements. In parallel, we also recognize that the current dataset is limited in terms of feature diversity and richness. To address this limitation, future work will expand the sensing system to include additional modalities that can enhance condition monitoring accuracy. Specifically, we will integrate vibration spectrum sensors capable of capturing high-resolution frequency domain data. These signals will be analyzed using Fast Fourier Transform (FFT) techniques to extract frequency-based features indicative of early-stage mechanical faults. In addition, there is interest in exploring multimodal data fusion strategies that combine temperature, time-domain vibration, and frequency-domain features to improve prediction accuracy. This expansion will be designed to ensure complete compatibility with our existing data acquisition and machine learning pipeline, allowing seamless integration and evaluation within the current framework. Moreover, an expansion of the dataset through extended data collection campaigns and the integration of new sensor modalities would not only improve the representativeness and quality of the available information but also enable a robust comparative analysis with more advanced modeling approaches. In particular, such efforts would facilitate the evaluation of state-of-the-art architectures, including Transformer-based models, which require larger and more comprehensive datasets to fully exploit their modeling capabilities. Although the present work focuses on frameworks that are effective under limited data availability conditions, future research will aim to explore the potential advantages offered by these modern deep learning architectures when supported by richer datasets, further validating the versatility of the proposed framework and ensuring its applicability to increasingly complex predictive maintenance scenarios. A relevant area for future work is incorporating explicit cost models that account for both operational disturbances and the infrastructure costs of data collection, storage, and processing. Conceptualization, J.O.-M.; Methodology, J.O.-M. and A.S.-H.; Software, J.O.-M.; Validation, A.S.-H.; Formal analysis, A.S.-H. and A.M.-N.; Investigation, A.S.-H. and A.M.-N.; Resources, J.O.-M. and A.M.-N.; Data curation, A.M.-N.; Writing--original draft, J.O.-M.; Writing--review & editing, A.S.-H. and A.M.-N.; Visualization, A.S.-H.; Supervision, A.M.-N.; Funding acquisition, J.O.-M. All authors have read and agreed to the published version of the manuscript. This research was partially funded by the Ministerio de Ciencia, Innovacion y Universidades of Spain: Grant Ref. PID2022-137748 OB-C31, funded through MCIN/AEI/10.13039/501100011033. Not applicable. The datasets presented in this article are not readily available due to ownership and access restrictions. Requests for access to the datasets should be directed to the correspondant author. J.O-M. recognizes the insights provided by the EU RFCS program within the project DynReAct_PDP (grant ID: 101112421). During the preparation of this manuscript/study, the author(s) used Gemini 2.5 to improve the grammar, syntax, etc., of some sentences. The authors have reviewed and edited the content and take full responsibility for the content of this publication. Conflicts of Interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The acknowledgment section of this manuscript discloses the funding sources.