## 5 Conclusions  
The results of applying the proposed methodology for predicting bearing failures offer valuable insights into the field of predictive maintenance. Through rigorous analysis and experimentation, the capabilities and limitations of this approach in early detection  
Figure 17: Maximum precision results. of bearing problems have been identified, highlighting its potential as an effective tool for improving the reliability and efficiency of rotating machines in industrial environments. The results of this study reveal the evolution in accuracy throughout the training process of the neural network. This means that as the network gains more knowledge through training, its ability to make accurate predictions improves significantly. It is also evident that the proper tuning of the hyperparameters, such as the learning rate, plays an essential role in this process. When properly configured, these hyperparameters allow for promising results in detecting bearing faults with high accuracy, which is essential in predictive maintenance and machinery diagnostic applications. The correct selection of hyperparameters is a key consideration in the development of machine learning models. This study demonstrates that by carefully choosing these values, the variability in the results obtained can be significantly reduced. Consequently, greater consistency in the convergence of the results is observed, which facilitates the interpretation and practical application of the neural network in the detection of bearing faults and other industrial applications. A broader exploration of hyperparameters could further optimize model performance. Although initial tuning was performed, it would be interesting to expand hyperparameter search range using cross-validation techniques in future studies. Regarding the feasibility of using transfer learning, the results obtained in this study are fruitful in applying this technique in a new case study. Transfer learning proves to be a valuable tool that improves not only the generalization capability of the model, but also its convergence speed and accuracy. This is particularly noteworthy given that these outstanding results are achieved with a relatively small dataset compared to more conventional applications such as object recognition. In summary, transfer learning shows significant potential for industrial and diagnostic applications, supporting its utility and feasibility in improving predictive maintenance and machinery diagnostic systems. It has been acknowledged the importance of testing on actual industrial sites. While this study was based on the CWRU dataset, future work will focus on real-world validation under complex operating conditions to assess noise robustness and environmental adaptability. Future research should explore the potential of hybrid models that combine deep learning with classical machine learning techniques, like Support Vector Machines or Random Forest, to enhance the robustness of fault detection systems. Another key area of study is the interpretability of deep learning models, as developing methods to explain neural network decisions would improve trust and facilitate adoption in industrial environments. Additionally, integrating multi-sensor data, such as temperature and acoustic emissions, could create a more comprehensive diagnostic system. Real-time adaptation through online learning techniques would allow the model to continuously update with new data, further improving its predictive capabilities. Finally, cross-dataset validation on different bearing fault datasets beyond the CWRU database would help assess the generalization of the proposed method across various machines and industrial settings. In industrial applications, deploying this methodology on edge computing platforms could enable real-time fault monitoring, reducing unplanned downtime and maintenance costs. Integrating the approach into predictive maintenance systems would automate fault detection in large-scale industrial operations, improving reliability and efficiency.