# ML-Based Maintenance and Control Process Analysis, Simulation, and Automation--A Review
## 1 Introduction  
Automation and digitalisation in various industries are advancing rapidly as we move towards the paradigms of Industry 4.0 and 5.0, driven by sensor integration, the Industrial Internet of Things (IIoT), and advanced 5G and emerging 6G cellular networks. These technologies are further enhanced through the simulation and automation of processes using artificial intelligence (AI) and machine learning (ML) approaches. Ensuring the continuity of operations under various conditions is becoming increasingly critical for industrial systems. Predictive maintenance, which uses ML to simulate and automate maintenance processes, is currently one of the most sought-after solutions. This approach allows industries to predict potential equipment failures and proactively perform maintenance, minimising downtime and costs. This study identifies the main trends in ML-based predictive maintenance, especially human-machine communications based on brain-computer interfaces (BCIs). It aims to critically evaluate and compare the current trends, highlighting the most effective strategies and technologies. Furthermore, this study aims to define research and development priorities to further enhance the effectiveness of ML-based predictive maintenance. In this way, it contributes to the continuous evolution of industrial practices, in line with the assumptions of Industry 4.0/5.0. ML-based maintenance and control process analysis involves using advanced algorithms to monitor, predict, and optimise the performance of industrial systems, including new control systems. In the area of interfaces, this is facilitated by both new techniques for transmitting control commands (hands-free) and visualisation methods that make iteasier to understand and spot potential errors, even in complex industrial processes and installations. Through analysing historical and real-time data, ML models can identify patterns and anomalies that indicate potential failures or inefficiencies in machines and processes. This predictive ability enables proactive maintenance, thus reducing downtime and extending the life of equipment. Simulation models powered by ML enable the testing and optimisation of different maintenance strategies and control processes in a virtual environment, reducing the risks and costs associated with real-world experiments. Automation is enhanced by integrating ML algorithms into control systems, enabling dynamic adjustments and decision-making with little or no human intervention (for commands/scenarios requiring human approval). These automated systems can learn and adapt over time, improving their accuracy and efficiency as more data are collected. Integrating ML into maintenance and inspection processes also facilitates better resource allocation, as maintenance activities can be scheduled based on the actual condition of the equipment, rather than fixed intervals. Additionally, ML-based analytics helps to identify bottlenecks and inefficiencies in inspection processes, enabling continuous improvement and optimisation. Applying machine learning to maintenance and inspection processes results in smarter and more efficient operations while significantly reducing costs and increasing system reliability (Figure 1). Key paradigms for analysing, simulating, and automating ML-based maintenance and control processes revolve around several fundamental concepts, which are outlined in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Paradigm/Area** & **Description** \\ \hline \multirow{3}{*}{IIoT integration} & ML models are embedded within the IoT framework, enabling seamless communication and data flow between devices and systems to provide more consistent operations. \\ \hline \multirow{3}{*}{Condition monitoring} & Uses sensors and ML algorithms to continuously assess the health of machines, enabling early detection of anomalies that could lead to system failure.