## 5 Conclusions  
The methodology proposed in this study for detecting faults in wind turbine blades using computational learning techniques has proven to be both effective and robust. The use of logistic regression as part of the first model outperformed neural networks, decision trees, and the naive Bayes method, highlighting its value as a tool for identifying patterns associated with faults. However, the clustering approach implemented in the second model demonstrated notable superiority in both precision and data segmentation capability, suggesting that clustering can better capture the underlying characteristics of the data compared to supervised methods. These findings underscore the importance of considering multiple approaches within computational learning for fault detection in complex systems such as wind turbine blades. The methodology presented not only offers a new perspective on early fault identification but also emphasizes the utility of accessible and flexible tools like Orange Data Mining for implementing advanced solutions in the wind energy industry. Future research could focus on the combination of these methods to further enhance the accuracy and efficiency of the detection system, as well as on applying these techniques to other critical components within the energy infrastructure. Further work is required to combine methods and apply these techniques to other critical components in energy infrastructure, such as hybrid modeling approaches and the combination of different models, including neural networks, decision trees, and logistic regression, into an ensemble approach such as stacking, bagging, or boosting. This could enhance predictive performance. Additionally, the implementation of a voting mechanism, whereby the predictions of multiple models are combined to arrive at a final decision, has the potential to enhance the accuracy of the process. The models could be assigned weights based on their performance on specific classes, such as Blade 2 in this study. Furthermore, multiple neural networks with varied architectures or training data subsets could be combined. Techniques such as dropout could also be employed to create an ensemble effect within a single neural network model. The application of pre-trained models from similar domains and fine-tuning of this specific problem could facilitate model development and enhance accuracy, particularly in cases with limited data. Additionally, the exploration of advanced feature engineering techniques to create more discriminative features could assist in improving the model's performance. Techniques such as principal component analysis (PCA) for dimensionality reduction or the utilization of domain knowledge to engineer novel features pertinent to blade failures may prove beneficial. The development of techniques to detect outliers or anomalies that may indicate the early signs of failure could serve to enhance the efficacy of the classification models. Anomalies may be identified through the utilization of clustering techniques, statistical methods, or specialized models such as autoencoders. Furthermore, the techniques explored herein could be adapted for the prediction of failures in other critical components, including wind turbine blades and solar panel systems. These components exhibit analogous characteristics with respect to wear and tear, environmental impact, and importance in energy generation.