# Virtual Reality and Digital Twins for Catastrophic Failure Prevention in Industry 4.0
## 5 Discussion  
In industry, the use of technologies such as virtual reality (VR) and digital twins in industrial environments has become increasingly necessary, allowing significant progress in monitoring, maintenance and training tasks by offering a safe, accessible and controlled alternative to field operations. However, there are still technical limitations that prevent the implementation of these technologies on a large scale. These include the absence of realistic tactile feedback, which reduces the effectiveness of hands-on learning in industrial simulations [27]. In addition, current systems face latency problems that hinder synchronization between the physical environment and its virtual representation, especially in critical contexts such as subway or hard-to-access facilities [28]. Added to this is the need for more intuitive interfaces, as many platforms remain complex to use for non-specialized operators, making it more difficult to increase efficiency in design validation and assisted maintenance processes [29]. In the face of these challenges, future lines of research are aimed at improving the accuracy and responsiveness of systems through solutions such as advanced haptic devices, 5G networks and edge computing to reduce latency, along with the integration of artificial intelligence to complement predictive-type analytics and the personalization of the user experience. These improvements would enable not only more realistic and effective training, but also safer remote operations, such as real-time remote inspection in hostile industrial environments, thanks to immersive 360\({}^{\circ}\) video streams combined with smart sensors [30]. Likewise, the adoption of digital twins coupled to VR environments allows us to visualize in advance the behavior of equipment under different operating conditions, facilitating decision making in preventive and predictive maintenance [31]. Despite the promising results of the proposed system, several practical limitations may hinder its large-scale adoption in industrial contexts. These include latency issues in data transmission, especially in remote environments; the cost of VR-compatible hardware and sensing equipment, which may not be affordable for all companies; and network stability, which is critical for ensuring real-time synchronization between physical assets and their digital counterparts. To address these challenges, future research will explore the incorporation of 5G communication technologies and edge computing to reduce latency and increase system responsiveness. Additionally, integrating artificial intelligence for automated diagnosis and decision support may enhance the robustness and scalability of the platform, paving the way for more resilient and intelligent maintenance systems under the Industry 4.0 paradigm. The proposed methodology demonstrates a strong potential for scalability and generalization across different industrial scenarios. Although this study focused on a specific use case involving an internal combustion engine, the system architecture is modular and adaptable. By modifying the 3D digital model and updating the types of sensors connected to the PLC, the same communication flow--from data acquisition to immersive visualization--can be applied to a wide range of equipment or processes. This flexibility allows the platform to be deployed in various industrial environments such as fluid transport systems, robotic workcells, or energy plants, making it a replicable and extensible solution for Industry 4.0 training and monitoring applications. Despite the system's successful performance in a controlled environment, certain technological limitations must be considered for real industrial deployment. These include latency due to network instability, limitations in data throughput, and the processing load of real-time visualization.