## 2 Materials and Methods
### Steps 2 and 3: Conceptualization and Computational Modeling
#### 2.2.1 Operational Availability and Intelligent Maintenance  
The \"working\" state is considered as the mother state in Figure 2, which means that the equipment is available as long as the agent \"Compressor\" has not triggered a maintenance event requiring equipment stoppage. However, the equipment might be available and running normally in the \"normal\" state while the condition monitoring system is active, and the equipment health is being checked on a daily and monthly basis (rates). The daily and monthly checks have specific time amounts (i.e., timeout in Anylogic) and might trigger a maintenance event that results in equipment stoppage. The monthly monitoring checks are done by two stakeholders (1) condition monitoring providers and (2) the technical service provider. Moreover, there are minor and major scheduled maintenance work that is taking place while the equipment is running (which does not lead to production stoppage). Furthermore, the PdM might also trigger a maintenance event that can take place in the following opportunistic intervals. This state is named \"Intelligent Maintenance\" and does not lead to production stoppage as it utilizes potential opportunistic intervals. The time amount for these maintenance events specifically connected to the intelligent maintenance and is extracted based on OREDA data for MTTR. Table 2 addresses the triggers of the transitions between the different states included in Figure 2. As seen, the transitions from the \"normal\" state to the states concerning (minor and major) \"scheduled maintenance\", and back again, are triggered by timeouts (specific time interval). The states of \"condition monitoring\" are triggered by rates. In this case, a conditional transition including a \"randomTrue probability distribution\" of the input failure data is used to demonstrate the probability of detection. This means that, if the condition is false, the condition monitoring system is not able to detect anything abnormal with the operation and enters the normal working state again. In contrast, if the condition is true, the condition monitoring system has detected abnormal behavior of the system and the presence of failure. The logic of these latter states concerning condition monitoring is not yet incorporated into the computational model, as the extracted failure rates used in this research only contain system failure, and therefore the impact of the monitoring system has already been taking into consideration, indirectly. However, the states are included in the computational model as they pose an impact on the model output in terms of maintenance workload. At last, the \"IntelligentMaintenance\" is triggered by the flow \"OpportunisticMaintenance\" from system dynamics and then back to the \"normal\" state again by a timeout function of 18 h that is extracted from the OREDA database [17] and traceable to the specific MTTR values of the failure modes monitored in this case study. Figure 1: The novel computational model of intelligent maintenance. Figure 3: Scheduled maintenance state (at both system and component level) representing opportunistic maintenance intervals. Figure 2: Operational availability and intelligent maintenance. the case study, while scheduled maintenance at the system level focuses on system-level (systems connected to the case study, e.g., scrubber, cooler)--hence, scheduled maintenance causing unavailability of any of these systems requires stoppage of the case study. Table 3 highlights the triggers that are causing transitions between the different states present in the scheduled maintenance and opportunistic maintenance intervals. In this case, the scheduled maintenance states are triggered by rates that are extracted from the case company data. The durations of the states are, on the other hand, highlighted by triangular distributions either based on the case study data or the OREDA database [17] (dependent on use case scenario).