# ML-Based Maintenance and Control Process Analysis, Simulation, and Automation--A Review
## 4 Discussion  
ML-based predictive maintenance systems offer a number of benefits, such as reducing unplanned downtime by predicting failures before they occur, leading to significant cost savings and extended equipment life. These systems can optimise maintenance schedules, ensuring that maintenance is performed only when necessary, reducing waste and labour costs. By analysing large data sets from different sources, they can uncover complex patterns and correlations that traditional methods may miss, improving decision-making. They also support real-time monitoring, providing immediate insights and enabling a rapid response to emerging issues. However, these systems also have drawbacks, such as high upfront investments in data collection infrastructure, sensors, and computing resources. The effectiveness of the system is highly dependent on the quality and quantity of data, making data management and pre-processing critical and often difficult tasks. There is also the risk of model inaccuracy or false positives, which can lead to unnecessary maintenance or missed failures, potentially reducing trust in the system. In addition, the complexity of implementing and maintaining these systems requires specialised knowledge and ongoing support, which can be a barrier for some organisations. Finally, the constant evolution of models to adapt to new data can be costly and require ongoing investment to ensure long-term reliability and accuracy [20; 21; 22; 23; 24; 25]. BCIs offer a direct communication path between the human brain and external systems, making them an innovative focal point for AI-based predictive maintenance in the Industry 5.0 paradigm. Industry 5.0 emphasises human-machine collaboration, and BCIs enable seamless interaction between human operators and AI systems, improving the efficiency of decision-making. BCIs can help workers monitor and control machines in a more intuitive way, enabling real-time feedback on system performance based on human cognitive responses. AI can analyse brain signals from BCIs to predict and identify potential equipment failures before they occur, improving predictive maintenance. This reduces downtime, extends machine life and minimises costs. BCIs combined with AI enable adaptive maintenance strategies where human input is integrated into the decision-making process, creating more personalised maintenance protocols. They allow operators to remotely monitor complex systems with cognitive feedback, even in dangerous or inaccessible environments. In addition, AI-based BCIs can improve the training of maintenance personnel by interpreting brain activity to identify learning gaps and suggest improvements. The combination of BCIs and AI is in line with Industry 5.0's goal of enhancing human creativity and knowledge while optimising machine performance. Ultimately, AI-based BCI systems can enable smarter, more responsive and human-centred maintenance processes in smart factories. A study showed that the analysis of signals from hand movement is classifiable at a level of around 90%. This is not a result that matches the level of signal classification  
Figure 18: Cross-validation for recurrent neural network. achieved in other origins. The best tended to be a recurrent network in terms of time spent on learning and classification results [26; 27; 28; 29]. In this case, the recurrent network that classifies the signal achieved better results than the other type of network, which is the spliced network. This justifies further analysis of the two-dimensional recurrent network architecture in traffic classification areas instead of spline networks. The cluttered network is worth mentioning. It cannot be ruled out that enlarging the learning set, especially in the case of traffic-related data, would have a more favourable impact on classification accuracy. When considering the use of ANNs to classify signal sources, results of 75% are possible in some cases. A network with deep layers has the advantage that training, regardless of the learning parameters, takes a very short time.