# ML-Based Maintenance and Control Process Analysis, Simulation, and Automation--A Review
## 3 Results
### ML-Based Predictive Maintenance Systems Operation  
The ML-based predictive maintenance system operation algorithm is composed as follows:  
* Data collection;
* Data preprocessing;
* Feature engineering;
* Model training;
* Model validation;
* Real-time monitoring;
* Forecasting and decision-making;
* Alert generation;
* Performing maintenance actions;
* Model updating.  
The algorithm begins with continuous data collection from various sources, such as sensors on equipment, historical maintenance logs, and environmental conditions, providing a comprehensive data set for analysis. The raw data are then cleaned, normalised, and transformed to remove noise, handle missing values, and convert them into an appropriate format for model input. This step may involve feature extraction and selection. The algorithm identifies and constructs significant features from the pre-processed data, such as calculating running averages, trends, or anomalies, to improve the model's predictive capabilities. Using the prepared data, the algorithm trains machine learning models such as regression models, decision trees, or neural networks to learn patterns and relationships that precede equipment failures or maintenance needs. The algorithm tests the trained model on a separate validation data set, adjusting hyperparameters and evaluating per  
Figure 10: Data flow for advanced video-based analysis.  
formance metrics to ensure that the model generalises well to new, unseen data. The deployed model continuously monitors incoming data in real time, applying the learned patterns to detect early signs of potential failures or anomalies that may indicate a need for maintenance. When the model predicts an impending failure or maintenance need, it generates a prediction, often including a confidence score, that informs decisions about maintenance actions. Based on the model's predictions, the algorithm triggers alerts or notifications to maintenance teams indicating the urgency and nature of the potential problem, enabling timely intervention. Maintenance teams respond to the alerts by performing inspections, repairs, or replacing parts as needed, guided by the algorithm's predictions. Post-maintenance data are fed back into the system, allowing the algorithm to learn from the results of the actions taken. This continuous feedback loop improves the model, increasing its accuracy and reliability over time.  
Predictive maintenance architecture will evolve significantly with the integration of BCIs. A new layer will be introduced to capture and process EEG signals from operators, linking human cognitive data directly to the maintenance system. This will require incorporating EEG sensors and real-time data-processing modules to interpret brainwave activity. The BCI system will need to communicate with existing IoT sensors and machine learning algorithms, creating a hybrid model where human insights complement automated monitoring tools. Data storage and analysis will be extended to include not only machine performance data but also EEG-based patterns that indicate how the operator perceives the state of the machine. The feedback loop will be faster because operators will be able to trigger maintenance actions through brain activity, bypassing the need for manual control. Machine learning models will evolve, combining cognitive and sensor data and improving prediction accuracy with more complex decision-making algorithms. User interfaces will also change, providing operators with visual or audible feedback from their BCI interactions to increase control over maintenance processes. Security measures will need to be improved to protect sensitive EEG data, and new protocols will be required to handle system errors or cognitive overload. Operator training programmes will be revised to include BCIs, ensuring that they effectively contribute to predictive maintenance with brain-driven systems.