# ML-Based Maintenance and Control Process Analysis, Simulation, and Automation--A Review
## 5 Conclusions  
ML-based predictive maintenance systems offer significant benefits by enabling the proactive identification of potential equipment failures, reducing downtime, and optimising maintenance schedules. They leverage real-time data, historical records, and advanced algorithms to provide accurate predictions, improving overall operational efficiency. While these systems can lead to cost savings and extended equipment life, their success depends on the quality of the data and the robustness of the models used. The continuous monitoring and updating of models are essential to maintaining accuracy and adapting to changing conditions. Overall, machine learning-based predictive maintenance is a powerful tool for modern industries, driving smarter and more efficient maintenance strategies. In smart factories, BCIs enable operators to control AI-based predictive maintenance systems with direct neural data, increasing the speed and accuracy of maintenance deci \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{STRENGTHS} \\ \multicolumn{2}{c}{Automatisation of EEG data collection} \\ \multicolumn{2}{c}{Intuitive use} \\ \multicolumn{2}{c}{Individualised use} \\ \multicolumn{2}{c}{24/7 wellbeing monitoring and prediction} \\ \multicolumn{2}{c}{within Industry 5.0} \\ \multicolumn{2}{c}{ML-based integrated analysis and prediction} \\ \multicolumn{2}{c}{Built-in warnings and alerts} \\ \multicolumn{2}{c}{Historical data sets with full users’ history} \\ \hline \multicolumn{2}{c}{OPPORTUNITIES} \\ \multicolumn{2}{c}{Objectivisation of assessment} \\ \multicolumn{2}{c}{Reduced workload toward classification and} \\ \multicolumn{2}{c}{optimisation} \\ \multicolumn{2}{c}{Early diagnosis} \\ \multicolumn{2}{c}{Preventive or early intervention} \\ \multicolumn{2}{c}{Easier testing} \\ \multicolumn{2}{c}{Novel diagnostic methods} \\ \multicolumn{2}{c}{Novel factors/mechanisms taken into} \\ \multicolumn{2}{c}{consideration within diagnosis} \\ \multicolumn{2}{c}{Possibility of standardisation} \\ \multicolumn{2}{c}{Quick further development} \\ \multicolumn{2}{c}{Part of bigger systems (e.g., eHealth, smart} \\ \multicolumn{2}{c}{home smart factory)} \\ \hline \hline \end{tabular}
\end{table} 
Table 9: SWOT analysis for ML-based EEG classification systems as control system for predictive maintenance [81; 82; 83; 84; 85; 86]. sions. BCIs allow operators to seamlessly interact with artificial intelligence algorithms, adjusting maintenance schedules and responses based on real-time cognitive insights. This interaction helps optimise equipment performance by enabling intuitive adjustments to AI predictions, preventing potential failures before they occur. By integrating BCIs, smart factories can achieve more responsive and adaptive maintenance processes, reducing unexpected downtime and operational costs. Ultimately, BCIs contribute to highly efficient, self-optimising manufacturing environments. Key findings from the research highlight that achieving a 96% accuracy in BCI-based control systems is a promising result, but there is potential for even better signal classification, warranting further research. This current accuracy level is sufficient for industrial control systems, but complex multi-channel control requires additional data collection for improved performance. The results emphasise the importance of not just network architecture and learning parameters, but also the type of network used for data analysis. Modular systems may benefit from allowing learned network modules to be swapped out for ones better suited to specific tasks or conditions. Adaptability is key, as systems must meet the user's needs with minimal adjustments required from the user. Otherwise, the technology may face challenges in adoption. The research suggests that effective communication and control between user and device is essential for system success. Additionally, it is crucial for the device to be flexible enough to handle diverse operational contexts.