# Virtual Reality and Digital Twins for Catastrophic Failure Prevention in Industry 4.0
## 3 Workflow
In addition, as mentioned above, to create and implement this virtual environment, a study related to the direct case of application for which it was designed was also carried out, including subsequent sensorization, which was compatible with the objectives of the case applied in this study. This allowed us to simulate the operating conditions of the engine not as if it were dragged, but as an internal combustion engine. This is because we had exhaust gas temperature, ambient and pressure sensors, which, through data injection, made the simulation of the case feasible. Thanks to the possibility of using the environment described above, the first two steps proposed by the methodology presented in this text were already completed. Therefore, all that remained was to implement the early warning system or EWS. For the implementation of the early warning system, it was first necessary to perform an analysis of the critical operational limits of the parameters measured by the sensors installed in the equipment. This type of analysis is an essential step in the design of any effective EWS, since it allows the early identification of deviations that could compromise the operational safety of the system. As has been demonstrated in studies on predictive models based on time series data, an adequate delimitation of these thresholds allows user not only to foresee faults, but also to reduce downtime and avoid economic losses associated with unplanned outages [24]. In this context, we proceeded to review the official technical and service manuals of the equipment in question, in addition to obtaining direct operational information from the professional in charge of machine safety and maintenance. This approach was aligned with scenario-based methodologies that integrate both historical data analysis and operator experience to establish causal networks that anticipate undesired events [25]. The key parameters and critical limits are summarized in Table 1. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sensor** & **Critical Limit** & \begin{tabular}{c} **Work of** \\ **Maintenance** \\ \end{tabular} \\ \hline \hline \multirow{3}{*}{T\({}^{\circ}\) Ambient} & \multirow{3}{*}{45 \({}^{\circ}\)C} & Immediate improvement of area’s ventilation and cooling system \\  & & Remove personnel from site of operation \\ \hline \hline Admission T\({}^{\circ}\) & 60 C\({}^{\circ}\) & \begin{tabular}{c} Inspect filters and air ducts \\ Increase air flow around the equipment \\ \end{tabular} \\ \hline T\({}^{\circ}\) oil & 120 \({}^{\circ}\)C & \begin{tabular}{c} Check oil level and quality \\ Check air cooling system \\ \end{tabular} \\ \hline Exhaust T\({}^{\circ}\) & 550 \({}^{\circ}\)C & \
\begin{tabular}{c} Check fuel system and injection \\ Check valve and exhaust system \\ \end{tabular} \\ \hline \hline Compression pressure & 32 bar &
\begin{tabular}{c} Inspection and cleaning of combustion chamber \\ Check timing and calibration of injection system \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters, critical limits and maintenance work. Once the critical operational limits of the measured parameters were defined, a list of inspection and maintenance tasks to be performed in case any of these values were exceeded was prepared. With this information available, it was integrated into the virtual environment, where a system of pop-up windows was developed that were automatically activated when any of the parameterized values reached their critical limit. These pop-up windows had the function of indicating previously defined inspection and/or maintenance tasks, with the objective of improving and accelerating the response process in the event of an imminent equipment failure. This automated response logic was consistent with solutions applied in structural intelligent monitoring systems, where distributed sensors provide alerts in real time about critical risks, allowing immediate intervention even in adverse conditions [26].