# ML-Based Maintenance and Control Process Analysis, Simulation, and Automation--A Review
## 1 Introduction
\\ \hline \multirow{3}{*}{Real-time decision-making} & ML-based automation enables systems to make immediate adjustments in response to changing conditions without human intervention. \\ \hline \multirow{3}{*}{Data-driven process optimisation} & Uses ML to analyse massive amounts of operational data, identify inefficiencies, and suggest improvements for better performance. \\ \cline{1-1}  
Figure 2 shows the evolution of ML-based maintenance and control processes. Figure 3 shows the proposed basic architecture of such a system. This study provides a comprehensive overview of ML-based approaches for the analysis, simulation, and automation of maintenance and inspection processes. It focuses on identifying key trends and advances in the field of ML-based predictive maintenance, drawing insights from existing research and publications. The authors critically evaluate \begin{table}
\begin{tabular}{c c} \hline \hline
**Paradigm/Area** & **Description** \\ \hline Simulation-based optimisation & Uses ML to create virtual models of processes, allowing different strategies to be tested and refined in a risk-free environment. \\ Adaptive control & Involes ML systems that learn and evolve over time, improving their accuracy and efficiency as they encounter more data and varying conditions. \\ \hline Scalability and flexibility & Designing ML solutions that can easily scale with the growth of industrial operations and adapt to different contexts or sectors. \\ \hline Human-AI collaboration & Emphasizes the role of ML in augmenting human decision-making, where AI provides insights and recommendations but ultimate control remains in the hands of human operators. \\ \hline Ethical and transparent AI & Ensures ML models are designed with integrity, accountability, and transparency in mind, addressing concerns about the ’black box’ nature of AI in critical industrial environments. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 3: Basic architecture of analysing, simulating, and automating ML-based maintenance and control processes. Figure 2: Concept of development of ML-based maintenance and control processes. these trends, comparing different methodologies, algorithms, and implementations in the context of their effectiveness and real-world applications. Furthermore, we highlight the strengths and limitations of different ML techniques, offering a balanced perspective of their current and potential future impacts. Drawing on both a literature review and the authors' professional experience, we define the key R&D priorities that need to be addressed to advance the field. Ultimately, we provide a roadmap for future innovations in predictive maintenance, highlighting the importance of continuous exploration of ML-based solutions. Interest in the adoption of BCI systems in predictive maintenance is becoming particularly relevant for cyber-physical-human systems (CPHSs), with possible applications in healthcare and various industries. Research into such applications of brain-computer interfaces (BCIs) is underway at the Augmented Reality Laboratory for Health Monitoring (ARHeMLab) at the University of Naples Federico II (Italy), among others, but is in the prototype phase. Brain-computer interfaces (BCIs) play a transformative role in AI-based predictive maintenance, enabling direct human interaction with complex systems. BCIs allow operators to monitor and control maintenance processes more intuitively, using brain signals to interact with AI models. This improves decision-making through providing real-time input and adjustments based on human knowledge and situational awareness. In predictive maintenance, BCIs can tune AI predictions, thus optimising the planning and execution of maintenance tasks. Overall, BCI integration can lead to more accurate and efficient maintenance operations, reducing downtime and improving system reliability. BCI-based control offers hands-free technologies in such systems. Advances in BCI technology are driving the popularisation of this issue and the growing number of applications.