# Real-Time Monitoring of Wind Turbine Bearing Using Simple Neural Network on Raspberry Pi
## 5 Conclusions  
This study developed a machine learning-based model on a Raspberry Pi to detect wind turbine bearing faults in real time. The model was designed and trained on a desktop computer due to its higher performance. Then, real-time implementation was achieved by running the model on a Raspberry Pi for real-time wind turbine bearing fault detection. The experimental results demonstrate that the model achieves a high accuracy and rapid detection of faults within milliseconds of their occurrence. The model achieved an accuracy rate of 99.8% and the testing time was 0.059 ms, indicating its effectiveness and precision in detecting wind turbine bearing faults.  
This study demonstrates the practical implications and potential applications of real-time fault detection in wind turbines using a neural network model. The model provides significant accuracy in quickly identifying bearing faults and providing immediate predictions during fault detection, resulting in reduced maintenance costs, increased turbine availability, and improved overall efficiency. By being able to identify faults during turbine operation in a timely manner, the development of this neural network model facilitates early warning and rapid response, minimizing downtime and associated maintenance costs. Ultimately, real-time fault detection ensures reliable turbine operation and increased availability.  
The findings of this study are significant for promoting renewable energy generation and reducing reliance on fossil fuels. The model improves operational efficiency and reliability by enabling the real-time detection of wind turbine bearing faults, minimizing energy waste. This facilitates the wider adoption of renewable energy sources, reducing dependence on finite fossil fuel resources and promoting sustainable energy development and environmental protection.  
In the future, many works will require enhancement, and network design optimization will be of paramount importance. This model can only be used on specific models of wind turbines and is restricted to the generator bearings of these turbines. Future research must establish a multi-channel, multi-type, and multi-scenario detection model that can be further optimized to improve the generalizability of models.  
Continued innovation and improvement in this area will drive renewable energy generation, reducing dependence on fossil fuels and paving the way for a cleaner, more sustainable energy future.  
Conceptualization, H.M., T.W., R.Q. and F.Z.; methodology, H.M. and T.W.; validation, T.W.; formal analysis, T.W. and R.Q.; investigation, T.W., R.Q. and F.Z.; resources, T.W.; data curation, T.W.; writing--original draft preparation, T.W., R.Q. and F.Z.; writing--review and editing, T.W., H.M. and A.K.N.; supervision, H.M. and T.W.; project administration, H.M. and T.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Royal Society award (number IEC\(\backslash\)NSFC\(\backslash\)223294) to Professor [PERSON].  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to commercial privacy.  
The authors declare no conflicts of interest.